The widespread use of modern light sources, particularly light emitting diodes (LEDs), creates requirements related to lighting quality that demand new approaches for their solution. One of the key aspects is ensuring the colour uniformity of radiation from luminaires based on LEDs. Currently, colour uniformity is achieved through binning, that is sorting LEDs according to their chromaticity coordinates (x, y) in the CIE 1931 colour space. However, binning according to current standards does not yield the necessary effect; differences in chromaticity are still observed, which forces LED manufacturers to independently develop and apply alternative binning approaches. Furthermore, the current binning method does not account for the influence of luminance and chromatic adaptation on the perception of LED chromaticity. The foundation of LED binning is the colour threshold – the minimum difference in colour that an observer can notice. This work investigates the influence of adaptation luminance on colour thresholds and compares the obtained data with results calculated using the main models of colour perception: CIECAM02, CIECAM16, and the colour sensation model VRVGVB. These models account for the adaptation mechanisms of vision, allowing for more accurate prediction of colour perception under various lighting conditions. The research results can be useful for developing advanced LED binning methods that ensure high colour uniformity.

1. Standard RF GOST 34819–2021: Light devices. Light requirements and test methods / By order of the Federal Agency for Technical Regulation and Metrology dated January 20, 2022, No. 28‑st, the interstate standard GOST 34819–2021 was put into effect as a national standard of the Russian Federation from July 1, 2022.
2. Khanh, T.Q., Bodrogi, P., Vinh, Q.T., Winkler, H. LED Lighting, Technology and Perception // Wiley-VCH, 2015, p. 299–301.
3. Meshkov, V.V., Matveev, A.B. Fundamentals of Light Engineering [Osnovy Svetotekhniki] Part 2 / M.: Energoatomizdat, 1989.
4. Fairchild, M.D. Colour Appearance Models / 2nd. ed., Wiley, Chichester, 2005.
5. Belyaeva, N.M. Construction of an Equi-Contrast System for Determining Colour Relationships in Architectural Interiors [Postroyeniye ravno-kontrastnoy sistemy dlya opredeleniya tsvetovykh sootnosheniy v arkhitekturnykh inter’yerakh] / Candidate Dissertation, 1972, MPEI, Moscow.
6. Budak, V. P., Delian, R.A. Changing Mac-Adam Ellipses with Adaptation // Light & Engineering, 2023, Vol. 31, # 4, pp. 16–18.
7. Matveev, A.B., Belyaeva, N.M. An Equi-Contrast Colour System // Svetotekhnika, 1965, # 9, pp. 1–6.
8. The CIE 2016 Colour Appearance Model for Colour Management Systems: CIECAM16, CIE 248:2022 / CIE, Vienna
9. Wyszecki, G., & Stiles, W.S. Colour Science: Concepts and Methods, Quantitative Data and Formulae / John Wiley & Sons, 2000.
10. MacAdam, D.L. Visual Sensitivities to Colour Differences in Daylight // Journal of the Optical Society of America, 1942, 32, pp. 247–274.

Indoor sports halls are areas, in which sports competitions are frequently held, and factors affecting physical risk, such as lighting, temperature, and sound, must be controlled in order to conduct these competitions efficiently. Of these, lighting is the most important, as it directly affects the vision of athletes, referees, and spectators. Ensuring ideal lighting in indoor sports halls is essential in order to prevent performance losses due to visual impairments in athletes and referees, and to enhance the spectators’ viewing pleasure. The primary conditions for achieving this are maintaining the average luminance at an optimal level and eliminating harmonics in the luminance distribution, thereby ensuring uniformity of luminance on the floor. Hence, the luminance values in the environment should be periodically checked, and necessary maintenance should be performed as required. Measuring luminance values at various points in the environment is essential for critical maintenance work and improvements, but this process is currently labour-intensive and time-consuming, making it impossible to mathematically calculate the luminance distribution in an environment following physical wear and tear. Given these challenges, the need for a new method is evident. This study proposes a new prediction method for inspecting the uniformity of luminance in indoor sports halls. Using this method, the average luminance values in indoor sports halls can easily be predicted and non-uniform points in the luminance distribution identified, allowing timely intervention in the system./br>

1. Ünlü, Y. D., Şahin, N. High Energy Efficient and LED Lighting Vehicles in Lighting of Sports Halls // Bitlis Eren University BEU Journal of Science, 2021, Vol. 10, # 1, pp. 277–286.
2. De Graaf, T., Dessouky, M., Müller, H.F. Sustainable Lighting of Museum Buildings // Renewable Energy, 2014, Vol. 67, pp. 30–34.

3. Khosrowshahi, F., Alani, A. Visualisation of Impact of Time on the Internal Lighting of a Building // Automation in Construction, 2011, Vol. 20, # 2, pp. 145–154.
4. Veitch, J. A., Newsham, G.R. Lighting Quality and Energy-Efficiency Effects on Task Performance, Mood, Health, Satisfaction, and Comfort // Journal of the Illuminating Engineering Society, 1998, Vol. 27, # 1, pp. 107–129.
5. Hsu, C.H. The Effects of Lighting Quality on Visual Perception at Sports Events: A Managerial Perspective // International Journal of Management, 2010, Vol. 27, # 3, pp. 693–703.
6. Houser, K., Wei, M., Royer, M.P. Illuminance Uniformity of Outdoor Sports Lighting // Leukos, 2011, Vol. 7, # 4, pp. 221–235.
7. Kayakuş, M., Üncü. İ.S. Measuring the Luminance of an Indoor Basketball Hall with Developed Artificial Neural Networks Based Software // European Journal of Science and Technology, 2020, # 19, pp. 770–777.
8. Özkaya, M., Tüfekçi, T. Lighting Technique / Birsen Publishing House, 2011, İstanbul.
9. Kocabey, S., Ekren, N. A New Approach for Examination of Performance of Interior Lighting Systems // Energy and Buildings, 2014, Vol. 74, pp. 1–7.
10. Şahin, M., Oğuz, Y., Büyüktümtürk, F. ANN Based Estimation of Time-dependent Energy Loss in Lighting Systems // Energy and Buildings, 2016, Vol. 116, 11. pp. 455–467.
12. Kayakuş M., Çevik K.K. Estimation of Luminance Measurements in Road Lighting with Deep Learning Method // International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME 2019), 2019, pp. 111–112.
13. Kazanasmaz, T., Günaydın, M., Binol, S. Artificial Neural Networks to Predict Daylight Illuminance in Office Buildings // Building and Environment, 2009, Vol. 44, # 8, pp. 1751–1757.
14. Şahin, M., Büyüktümtürk, F., Oğuz, Y. Lighting Quality Control with Artificial Neural Networks // Afyon Kocatepe University Journal of Science and Engineering, 2013, Vol. 13, pp. 1–10.
15. Da Fonseca, W. R., Didoné, L. E., Pereira R.O.F. Using Artificial Neural Networks to Predict the Impact of Daylighting on Building Final Electric Energy Requirements // Energy and Buildings, 2013, Vol. 61, pp. 31–38.
16. Tran, D., Tan, Y.K. Sensorless Illumination Control of A Networked LED-Lighting System Using Feedforward Neural Network. // IEEE Transactions on Industrial Electronics, 2014, Vol. 61, pp. 2113–2121.
17. Şahin, M., Büyüktümtürk, F., Yüksel, O., Yalçın, P. Estimation of Luminous Flux Decreases in Lighting Elements with Artificial Neural Networks // Erzincan University Journal of Science and Technology, 2014, Vol. 7, # 1, pp. 19–36.
18. Şahin, M., Oğuz, Y., Büyüktümtürk, F. Approximate and Three-Dimensional Modeling of Brightness Levels in Interior Spaces by Using Artificial Neural Networks // Journal of Electrical Engineering and Technology, 2015, Vol. 10, # 4, pp. 1822–1829.
19. Şahin, M. Estimation of Average Illuminance Level in Indoor Lighting Systems with Artificial Neural Networks // Afyon Kocatepe University Journal of Science and Engineering, 2019, Vol. 19, # 2, pp. 348–360.
20. Kayakuş, M., Üncü, S.İ. Measuring Luminance on Tennis Courts Using Artificial Neural Networks // In book: Strategy-Focused Academic Assessments, 2019, pp. 171–190, Chapter: 9, Publisher: SRA Academic Publishing.
21. Kayakuş, M. Determınatıon of White Board Luminance Used in Classrooms With Artificial Neural Networks // Proceedings of 6 International Management Information Systems Conference “Connectedness and Cybersecurity”, 2019, İstanbul, pp. 355–361.
22. Kayakuş, M., Üncü, S.İ. The Luminance Estimation of Basketball Halls Using Machine Learning Methods // Düzce University Journal of Science & Technology, 2020, Vol. 8, # 4, pp. 2468–2479.
23. Georgiev, K., Ivanov, D., and Petrinska, I. Lighting Mesurements of Reconstructed Multifunctional Sports Hall // 2024 Ninth Junior Conference on Lighting, 2024, pp. 1–4.
24. URL‑1, https://www.jarvislighting.com/.22/01/2025.
25. Fiorentin, P., Iacomussi, P., Rossi, G. Characterization and Calibration of a CCD Detector for Light Engineering // IEEE Transactions on instrumentation and measurement, 2005, Vol. 54, # 1, pp. 171–177.
26. Kocabey, S. Finding the illumination level distribution in interior volumes and examining it with the finite element method // Ph. D. Thesis, 2008, Marmara University, Türkiye.
27. Marques, É., Melo, R. B., Carvalho, F. Ergonomic Work Analysis of Industrial Quality Control Workstations // In International Conference on Applied Human Factors and Ergonomics, 2017, Springer, Cham., pp. 532–544.
28. Ekrias, A., Eloholma, M., Halonen, L., Song, X. J., Zhang, X., & Wen, Y. Road lighting and headlights: Luminance measurements and automobile lighting simulations // Building and Environment, 2008, Vol. 43, # 4, pp. 530–536.
29. Davarci, Z. O., Sahin, M., & Akar, O. Luminance Measurement and Estimation Methods in Road // Light & Engineering, 2022, Vol. 30, # 6, pp. 106–123.
30. Büyükkınacı, B. Road Lighting Automation // M. Sc. Thesis, 2008, Istanbul Technical University, Türkiye.
31. Şahin, M. Determining the Maintenance Coefficients of Environments with Different Technical and Physical Properties and Estimating the Light Levels with Artificial Neural Networks // Ph. D. Thesis, 2014, Marmara University, Türkiye.
32. Alizadeh, M. J., Shabani, A., & Kavianpour, M.R. Predicting longitudinal dispersion coefficient using ANN with metaheuristic training algorithms // International Journal of Environmental Science and Technology, 2017, Vol. 14, pp. 2399–2410.
33. Jassim, M. S., Coşkuner, G., Zontul, M. Comparative Performance Analysis of Support Vector Regression and Artificial Neural Network for Prediction of Municipal Solid Waste Generation // Waste Management & Research, 2022, Vol. 40, pp. 195–204.
34. Saeidlou, S., & Ghadiminia, N. A construction cost estimation framework using DNN and validation unit // Building Research & Information, 2023, Vol. 52, # 1–2, pp. 38–48.
35. Lewis, C.D. Industrial and Business Forecasting Methods // Butterworths Publishing, 1982, London, 40.
36. Witt S.F., and Witt C. Modeling and Forecasting Demand in Tourism // Academic Press, 1992, London.
37. Yanık E. Demand Forecasting Application with Artificial Neural Networks in the Construction Equipment Industry // MSc Thesis, 2019, Kırıkkale University (Turkey).
38. Taşkıner B. Prediction of Natural Gas Consumption in Ankara Province with Artificial Neural Networks // MSc Thesis, 2018, Istanbul Technical University (Turkey).
39. Karahan M. Forecasting Amount of Export Demand with Artificial Neural Networks Method: A Comparative Analysis of ARIMA and ANN // Ege Academıc Review, 2015, Vol. 15, # 2, pp. 165–172.

Ground surface reflectance recovered from the satellite data are the basis for satellite monitoring of the state of the ground surface. To obtain high-quality information about the reflectivity properties of the ground surface, it is necessary to perform atmospheric correction of the images. The formation of the received radiance is influenced by a significant number of factors, which include the following:
– Radiation scattering,
– Radiation absorption by atmospheric gases,
– Adjacency effect,
– Multiple reflection of radiation,
– Radiation polarization,
– Non-Lambertian
surface reflection,
– Relief,
– Cloudiness,
– Interaction of radiation with the vegetation layer.
At present, there are no algorithms that would take into account all the main factors affecting radiation transfer. Previous approaches widely use the approximation of a uniform surface (independent pixels). In more complex algorithms, adjacency effect is taken into account exactly, and additional illumination by multiple reflected radiation is taken into account in the approximation of a uniform surface. In algorithms that take relief into account, scattering is considered to have no effect on the result, or the approximation of a uniform surface is used. In the proposed approach, unlike alternative ones, the influence of the non-uniformity of the ground surface reflectance and the altitude of the observed areas on the formation of adjacency effect and additional illumination of the ground surface is taken into account. The presented test calculation shows that the proposed algorithm for the considered situation has an average difference in results Δrsurf =0.014, a root-mean-square difference in results RMSE = 0.017 and a correlation coefficient r =0.97 compared to the results of the NASA MOD09 algorithm.

1. Otterman, J., Fraser, R.S. Adjacency effects on imaging by surface reflection and atmospheric scattering: cross radiance to zenith // Applied Optics, 1979, Vol. 18, # 16, pp. 2852–2860.
2. Vermote, E.F., El Saleous, N.Z., Justice, C.O. Atmospheric correction of MODIS data in the visible to middle infrared: first results // Remote Sensing of Environment, 2002, Vol. 83, # 1–2, pp. 97–111.
3. Berk, A., Adler-Golden, S.M., Ratkowski, A.J. et al. Exploiting MODTRAN radiation transport for atmospheric correction: The FLAASH algorithm // Proceedings of the Fifth International Conference on Information Fusion, FUSION 2002, (IEEE Cat.No. 02EX5997), 2002, Vol. 2, pp. 798–803.
4. Katkovsky, L.V. Parameterization of outgoing radiation for fast atmospheric correction of hyperspectral images [Parametrizatsiya ukhodyashchego izlucheniya dlya bystroy atmosfernoy korrektsii giperspektral’nykh izobrazheniy] // Atmospheric and Oceanic Optics, 2016, Vol. 29, # 9, pp. 778–784.
5. Putsay, M. A simple atmospheric correction method for the shortwave satellite images // International Journal of Remote Sensing, 1992, Vol. 13, # 8, pp. 1549–1558.
6. Breon, F.-M., Vermote, E. Correction of MODIS surface reflectance time series for BRDF effects // Remote Sensing of Environment, 2012, Vol. 125, pp. 1–9.
7. Vermote, E.F., Vermeulen, A. Atmospheric correction algorithm: spectral reflectance (MOD09) / Algorithm Theoretical Background document, version 4.0. 1999. URL: http://modis.gsfc.nasa.gov/data/atbd/atbd_mod08.pdf (date of access 30.01.2023).
8. Lyapustin, A., Martonchik, J., Wang, Y., Laszlo, I., Korkin, S. Multiangle implementation of atmospheric correction (MAIAC): 1. Radiative transfer basis and look-up tables // J. Geophys. Res., 2011, Vol. 116, D03210.
9. Lyapustin, A., Wang, Y., Korkin, S., Huang, D. MODIS Collection 6 MAIAC algorithm // Atmos. Meas. Tech., 2018, Vol. 11, pp. 5741–5765.
10. Tarasenkov, M.V. Improving the accuracy of atmospheric correction of satellite images and restoring the characteristics of the atmospheric optical communication channel beyond the line of sight [Povysheniye tochnosti atmosfernoy korrektsii sputnikovykh snimkov i vosstanovleniye kharakteristik atmosfernogo opticheskogo kanala svyazi za predelami pryamoy vidimosti] / Dissertation Doctor of Phys.-Math. Sciences, Tomsk: IAO SB RAS, 2024, 406 p.
11. Mousivand, A., Verhoef, W., Menenti, M., Gorte, B. Modelling top of atmosphere radiance over heterogeneous non-Lambertian rugged terrain // Remote Sens., 2015, Vol. 7, pp. 8019–8044.
12. Yin, G., Li, A., Zhao, W., Jin, H., Bian, J., Wu, S. Modelling canopy reflectance over sloping terrain based on path length correction // IEEE Trans. Geo-sci. Remote Sens., 2017, Vol. 55, pp. 4597–4609.
13. Fan, W., Chen, J.M., Ju, W., Nesbitt, N. Hybrid geometric optical – radiative transfer model suitable for forests on slopes // IEEE Trans. Geo-sci. Remote Sens., 2014, Vol. 52, pp. 5579–5586.
14. Belov, V.V., Tarasenkov, M.V. On the Accuracy and Operation Speed of RTM Algorithms for Atmospheric Correction of Satellite Images in the Visible and UV Ranges. // Atmospheric and Oceanic Optics, 2014, Vol. 27, # 1, pp. 54–61.
15. Tarasenkov, M.V., Belov, V.V., Engel, M.V., Zimovaya, A.V., Zonov, M.N., Bogdanova, A.S. Algorithm for the Reconstruction of the Ground Surface Reflectance in the Visible and Near IR Ranges from MODIS Satellite Data with Allowance for the Influence of Ground Surface Inhomogeneity on the Adjacency Effect and of Multiple Radiation Reflection // Remote Sens., 2023, Vol. 15, p. 2655.
16. Tarasenkov, M.V., Belov, V.V. & Engel, M.V. Retrieval of Ground Surface Reflectance from MODIS Satellite Data with Allowance for Reflectance Heterogeneity and Rugged Topography. //Atmos. Ocean Opt., 2024, Vol. 37, # 1, pp. S48 – S58.
17. Tarasenkov, M.V., Zonov, M.N., Engel, M.V., Belov, V.V. A Method for Estimating the Cloud Adjacency Effect on the Ground Surface Reflectance Reconstruction from Passive Satellite Observations through Gaps in Cloud Fields // Atmosphere, 2021, Vol. 12, N 1512.
18. Digital Landscape Model // URL: https://www.ngdc.noaa.gov (date of access: 30.05.2025).
19. Kneizys, F.X., Shettle, E.P., Anderson, G.P., Abreu, L.W., Chetwynd, J.H., Selby, J.E.A., Clough, S.A., Gallery, W.O. User Guide to LOWTRAN‑7. ARGLTR‑86–0177. ERP 1010 / Hansom AFB. MA 01731,1988, 137 p.
20. Bucholtz, A. Rayleigh-scattering calculations for the terrestrial atmosphere // Applied optics, 1995, Vol. 34, # 15, pp. 2765–2773.
21. Leroy, M., Roujean, J. Sun and view angle corrections on reflectance derived from NOAA/AVHRR data // IEEE Transactions on Geoscience and Remote Sensing, 1994, Vol. 32, pp. 684–697.
22. Roujean, J.-L., Tanre, D., Breon, F.-M., Deuze, J.-L. Retrieval of land surface parameters from airborne POLDER bidirectional reflectance distribution function during HAPEX-Sahel // Journal of Geophysical Research, 1997, Vol. 102, # D10, pp. 11,201–11,218.
23. Zimovaya, A.V. Atmospheric correction of satellite images of the earth’s surface taking into account the polarization of radiation in the visible and near IR ranges [Atmosfernaya korrektsiya sputnikovykh snimkov zemnoy poverkhnosti s uchetom polyarizatsii izlucheniya v vidimom i blizhnem IK-diapazonakh] / Diss. Cand. Phys.-Math. Sciences / Tomsk: IAO SB RAS, 2020, 152 p.
24. Dubovik, O., Smirnov, A., Holben, B.N., King, M.D., Kaufman, Y.J., Eck, T.F., Slusker, I. Accuracy assessments of aerosol optical properties retrieved from AERONET sun and sky radiance measurements // J. Geophys. Res., 2000, Vol. 105, pp. 9791–9806.

The relevance of the study is determined by the strategic role of improving energy efficiency in achieving climate goals and ensuring energy security, as well as the need to develop effective mechanisms of state control and supervision of product compliance with established requirements in the Eurasian Economic Union (EAEU). A comparative legal analysis of the institutional and procedural foundations of control and supervisory activities in the field of energy efficiency of lighting products in the EAEU and the European Union (EU) was conducted. The regulatory framework for the study was formed by technical regulations of the EAEU (TR EAEU 048/2019, etc.) and secondary law acts of the EU (Regulations 2019/2020, 2019/2015).
It has been established that the EU’s monitoring system, developed over 40 years of product-based energy efficiency policy evolution, is characterized by a comprehensive assessment methodology (considering service life and maintainability), harmonized risk-based market surveillance institutions, and a sanctioning mechanism focused on proportionality and prevention. Its political foundation is the “Energy Efficiency First” principle, reinforced by the integration of social aspects (combatting energy poverty and a just transition).
In contrast, the EAEU system exhibits methodological lag, fragmentation of institutional powers, a focus on post-control, and a non-harmonized sanctions regime, which creates conditions for regulatory arbitrage and reduces the effectiveness of oversight. The identified differences are systemic and reflect the different stages of development of the philosophy of technical regulation: from narrow standardization to the integration of economic, environmental, and social objectives.
Based on the analysis, recommendations wereformulated for harmonizing regulatory and supervisory activities within the EAEU, including: modernizing the energy efficiency assessment methodology in line with modern technologies; implementing a risk-based approach to inspection planning; creating a unified supervisory information system; harmonizing sanctions; and initiating a discussion on incorporating the social aspects of energy efficiency into the Union’s political agenda.

1. Paris Agreement, Dec. 12, 2015 [Electronic resource]. URL: https://unfccc.int/sites/default/files/english_paris_agreement.pdf (Data of accessed: 12/02/2024).
2. International Energy Agency (IEA). Energy Efficiency Market Report 2013 / Paris: IEA, 2013, 247 p.
3. Treaty on the Eurasian Economic Union (Signed in Astana on May 29, 2014) (as amended on May 29, 2024) [Electronic resource] // ConsultantPlus, URL: http://www.consultant.ru/document/cons_doc_LAW_163855/ (Date of accessed: December 2, 2024).
4. Consolidated version of the Treaty on the Functioning of the European Union // Official Journal of the European Union, 2012, C 326/47, pp. 47–390.
5. European Commission. The European Green Deal: COM (2019) 640 final / Brussels, 2019, 24 p.
6. Gonzalez-Torres, M., Bertoldi, P., Castellazzi, L., Perez-Lombard, L. Review of EU product energy efficiency policies: What have we achieved in 40 years? // Journal of Cleaner Production, 2023, Vol. 421, p. 138442. DOI: 10.1016/j.jclepro.2023.138442
7. Regulation (EU) 2023/1791 of the European Parliament and of the Council of 13 September 2023 on energy efficiency and amending Regulation (EU) 2023/955 (recast) // Official Journal of the European Union, 2023, L 231, pp. 1–111.
8. von Malmborg, F. First and last and always: Politics of the ‘energy efficiency first’ principle in EU energy and climate policy // Energy Research & Social Science, 2023, Vol. 101, p. 103126. DOI: 10.1016/j.erss.2023.103126
9. International Energy Agency (IEA). Multiple Benefits of Energy Efficiency: From “Hidden Fuel” to “First Fuel” / Paris: IEA, 2019, 165 p.
10. Nordensvärd, J., Bjorklund, M., von Malmborg, F., La Fleur, L., Skogsmo, E., Gamez, D.H.B. Reviewing the EU policy nexus of energy efficiency and social policy // Renewable and Sustainable Energy Reviews, 2025 Vol. 224, pp. 116128. DOI: 10.1016/j.rser.2025.116128
11. von Malmborg, F., Bjorklund, M., Nordensvärd, J. Framing the benefits of European Union policy expansion on energy efficiency of buildings: A Swiss knife or a Trojan horse? // European Policy Analysis, 2023, Vol. 9, # 3, pp. 219–243, DOI: 10.1002/epa2.1184
12. Technical Regulations of the Eurasian Economic Union “On Requirements for the Energy Efficiency of Energy-Consuming Devices” (EAEU TR 048/2019) (approved by the Decision of the EEC Council of 18.10.2019 No. 114) [Electronic resource] // Electronic fund of legal and regulatory-technical documents. URL: https://docs.cntd.ru/document/564716947 (date of access: 02.12.2024).
13. Shevchenko L.I. Legal problems of forming a common market of energy resources of the EAEU // Law. Journal Higher school economics, 2020, # 1, pp. 185–208.
14. Zweigert, K., Kötz, H. An Introduction to Comparative Law / Transl. from German by T. Weir, 3rd rev. ed. Oxford: Oxford University Press, 1998, 744 p.
15. Heffe, O. Justice: A Philosophical Introduction / Translated from German, Moscow: Praxis, 2007, 192 p.
16. Commission Regulation (EU) 2019/2020 of 1 October 2019 laying down eco-design requirements for light sources and separate control gears pursuant to Directive 2009/125/EC of the European Parliament and of the Council and repealing Commission Regulations (EC) No 244/2009, (EC) No 245/2009 and (EU) No 1194/2012 // Official Journal of the European Union, 2019, L 315/209, pp. 209–277.
17. Commission Regulation (EU) 2019/2015 of 11 March 2019 laying down eco-design requirements for refrigerating appliances pursuant to Directive 2009/125/EC of the European Parliament and of the Council and repealing Commission Regulation (EC) No 643/2009 // Official Journal of the European Union, 2019, L 315/227, pp. 227–272.
18. Review of the practice of monitoring compliance with the requirements of technical regulations of the EAEU for 2021–2023 // Analytical report of the Eurasian Economic Commission, Moscow, 2024, 87 p.
19. European Court of Auditors. Special Report – EU action on Eco-design and Energy Labelling: important contribution to greater energy efficiency reduced by significant delays and non-compliance / Luxembourg: Publications Office of the European Union, 2020, 70 p. DOI: 10.2865/911158
20. Rosenow, J., Cowart, R., Bayer, E., Fabbri, M. Assessing the European Union’s energy efficiency policy: Will the winter package deliver on ‘efficiency first’? // Energy Research & Social Science, 2017, Vol. 26, pp. 72–79. DOI: 10.1016/j.erss.2017.01.022
21. Regulatory Assistance Project (RAP). Efficiency First: A new paradigm for the European energy system: Driving competitiveness, energy security and decarbonization through increased energy productivity / Brussels, 2016, 44 p.
22. Hajer, M.A. The Politics of Environmental Discourse:Ecological Modernization and the Policy Process, Oxford: Clarendon Press, 1995, 344 p.
23. Schmidt, V.A. Discursive institutionalism: The explanatory power of ideas and discourse // Annual Review of Political Science, 2008, Vol. 11, pp. 303–326. DOI: 10.1146/annurev.polisci.11.060606.135342
24. European Court of Auditors. Special Report – Combating food waste: an opportunity for the EU to improve the resource-efficiency of the food supply chain / Luxembourg: Publications Office of the European Union, 2016, 68 p. DOI: 10.2865/333089
25. Bertoldi, P. Policies for energy conservation and sufficiency: Review of existing policies and recommendations for new and effective policies in OECD countries // Energy and Buildings, 2022, Vol. 264, pp. 112075. DOI: 10.1016/j.enbuild.2022.112075
26. Rosenow, J., Kern, F. Chapter 28: EU energy innovation policy: the curious case of energy efficiency // In: M.M. Roggenkamp, C. Banet (eds.). Research Handbook on EU Energy Law and Policy; Cheltenham, UK: Edward Elgar Publishing, 2017, pp. 528–551.
27. Chlechowitz, M., Reuter, M., Eichhammer, W. How first comes energy efficiency? Assessing the energy efficiency first principle in the EU using a comprehensive indicator-based approach // Energy Efficiency, 2022, Vol. 15, pp. 59. DOI: 10.1007/s12053-022-10063-8
28. Romanova, V.V. Legal problems of harmonization of legislation in the field of technical regulation in the EAEU [Pravovyye voprosy garmonizatsii zakonodatel’stva v oblasti tekhnicheskogo regulirovaniya v EAEU] // Journal of Russian Law, 2019, # 4, pp. 68–79.
29. Recital 64, Regulation (EU) 2018/1999 of the European Parliament and of the Council of 11 December 2018 on the Governance of the Energy Union and Climate Action // Official Journal of the European Union, 2018, L 328/1, pp. 1–77.
30. European Commission. Commission Staff Working Document – Assessment of progress towards the objectives of the energy union and climate action: SWD (2023) 646 final / Brussels, 2023, 178 p.
31. Regulation (EU) 2017/1369 of the European Parliament and of the Council of 4 July 2017 setting a framework for energy labelling and repealing Directive 2010/30/EU // Official Journal of the European Union, 2017, L 198, pp. 1–23.

Currently, the range of LED retrofitting lamps is constantly expanding. The aim of this study is to investigate the influence of the design of optical elements within the optical system of LED lamps on their photometric characteristics. Seven samples of LED lamps, incorporating secondary lens-type optical elements responsible for forming the light distribution curve, were used as test objects. Four samples featured a candle-shaped bulb, while three had a globe-shaped bulb.
The photometric characteristics (luminous flux, power, correlated colour temperature, luminous intensity distribution curve, and luminous efficacy) of these samples were determined and compared with the manufacturers’ specifications. As a result, it was demonstrated, in particular, that the luminous intensity distribution curve depends on both the bulb design and the lens design of the LED lamp. An analysis of the lens designs identified the mechanism of light distribution formation. The dependence of the luminous flux reduction level caused by the lens on its design was examined. It was shown that the luminous intensity distribution curve of an LED lamp depends on the specific features of these optical elements and the position of the LEDs relative to the lamp’s lens.

1. Sun, С.-C., Ma, Sh.-H., and Nguyen, Q.-K. Advanced LED Solid-State Lighting Optics // Crystals 2020, 10, 758; doi:10.3390/cryst10090758. URL: https://www.researchgate.net/publication/343930581_Advanced_LED_Solid-State_Lighting_Optics (accessed: 29.05.2025).
2. An Economical Omnidirectional A19 LED Light Bulb by the Industrial Technology Research Institute // LpR Article | Jul 01, 2016, URL: https://www.led-professional.com/resources‑1/articles/an-economical-omnidirectional-a19‑led-light-bulb-by-the-industrial-technology-research-institute (accessed: 29.05.2025).
3. Lishik, S.I., Pautino, A.A., Posedko, V.S., Trofimov, Yu.V., Tsvirko, V.I. Structural and technological solutions for light-emitting diode lamps of direct replacement // Light & Engineering, 2010, Vol. 18, # 3, pp. 57–63.
4. Employing the latest in injection moulding technology // LED professional Review, 2013, # 36, p. 5. URL: https://www.led-professional.com/downloads/LpR36_543794.pdf. (accessed: 29.05.2025).
5. MASTER LED candle Diamond Spark. URL: https://www.lighting.philips.co.uk/api/assets/v1/file/Signify/content/929002491299_EU.en_GB.PROF.FP/Localized_commercial_leaflet_929002491299_en_GB.pdf (accessed: 29.05.2025).
6. Aslanov, E.R., Doskolovich, L.L., Moiseev, M.A., Bezus, E.A.,. Kazanskiy, N.L. Design of an optical element forming an axial line segment for efficient LED lighting systems. URL: https://www.researchgate.net/publication/260152866_Design_of_an_optical_element_forming_an_axial_line_segment_for_efficient_LED_lighting_systems (accessed: 29.05.2025).
7. Optical element – diffuser [Electronic resource]. URL: http://www.pvsm.ru/images/2015/12/24/svetodiodnye-lampy-X-Flash‑12.jpg (accessed: 29.05.2025).
8. Lishik, S.I., Pautino, A.A., Posedko, V.S., Trofimov, Yu.V., Tsvirko, V.I. On Direct Replacement LED Lamps [Pryamaya zamena LED lampami] // Svetotekhnika, 2010, # 1, pp. 48–54.
9. Ashryatov, A.A. Research of the Influence of Optical Elements of a LED Retrofit Lamp on Its Light Parameters // Instrument making and automated electric drive in the fuel and energy complex and housing and communal services: Proceedings of the VIII National Scientific and Practical Conference, Kazan, 2023, pp. 637–639. URL: https://www.elibrary.ru/item.asp?id=55175271 (accessed: 29.05.2025).
10. Mikaeva, S.A., Zheleznikova, O.E., Sinitsyna, L.V. A Complex of Modern Research Equipment for Light Measurements // Automation and modern technologies, 2012, # 12, pp. 33–36.
11. Ashryatov, A.A., Kuznetsov, E.A., Smolin, K.A. Study of the Lighting Characteristics of Uniel LED Retrofit Lamps // Bulletin of MSTU, 2023, Vol. 26, # 4, pp. 349–360. DOI: https://doi.org/10.21443/1560-9278-2023-26-4-349-360.
12. Makаrоvа, N.V., Ashryatov, A.A. Study of the Lighting Characteristics of LED Retrofit Lamps for Domestic Lighting // Energy Security and Energy Saving, 2019, # 3, pp. 28–32. DOI: https://doi.org/10.18635/2071-2219-2019-3-28-32
13. Nesterkina, N.P., Kuznetsov, E.A. Study of the Characteristics of LED Lamps with Variable Emission Spectrum // Problems and Prospects of Development of Electric Power Industry and Electrical Engineering: II All-Russian Scientific-Practical Conference, Kazan, March 18–19, 2020: Conference Proceedings, Vol. 2, Kazan: KGEU, 2020, pp. 246–252.
14. Nesterkina, N.P., Kuznetsov, E.A., Zhuravleva, Yu.A. Research of Changes in the Lighting Characteristics of LED Retrofit Lamps as a Result of Long-Term Tests // Bulletin of MSTU, 2022, Vol. 25, # 4, pp. 305–312. URL: http://vestnik.mstu.edu.ru/v25_4_n93/03_Nesterkina_305–312.pdf (accessed: 29.05.2025).
15. Ashryatov, A.A., Kalmykov, V.V., Kuznetsov, E.A. Research of Constructions of Modern LED Analogues of Fluorescent Lamps // Light & Engineering, 2024, Vol. 32, # 6, pp. 9–16.
16. TIR Lens – Shanghai Optics Shanghai Optics URL: https://www.shanghai-optics.com/components/ (accessed: 29.05.2025).
17. Reference Book on Light Engineering / Ed. by Yu. B. Ayzenberg. 3rd ed., revised and enlarged, Moscow: Znak, 2006, 972 p.

1. Laxmi, G., Daljeet, P.S., Neeraj, S., and Kuldeep S.K. Comparative Analysis of P & O and INC MPPT Algorithms for PV System under Variable Irradiance // E3S Web of Conferences ICPES, 2023, 540, 12003, https://doi.org/10.1051/e3sconf/202454012003
2. Priyanka, M. Modelling and Control of a Battery for Renewable Energy Sources // International Journal of New Technologies in Science and Engineering (IJNTSE), April. 2022, Vol. 8, Issue. 4, pp. 1–5.
3. Rath, B.B. et al. Photovoltaic Partial Shading Performance Evaluation with a DSTATCOM Controller // IEEE Access, Vol. 10, pp. 69041–69052, 2022, doi: 10.1109/ACCESS.2022.3186906
4. Malla, S.G., Malla, Jagan M.R., Malla, P., Ramasamy, S., Doniparthi, S.K., Sahu, M.K., Subudhi, P.K., and Awad, H. Coordinated power management and control of renewable energy sources based smart grid // International Journal of Emerging Electric Power Systems, 2022, Vol. 23, # 2, pp. 261–276. https://doi.org/10.1515/ijeeps‑2021–0113
5. Chandrasekhar, N. Modified Grey Wolf Optimization Algorithm for MPPT of PV System under Partial Shading Conditions // International Journal of New Technologies in Science and Engineering (IJNTSE), 2022, Vol. 8, # 5, pp. 1–6.
6. Priyanka, M. Novel Control Technique for MPPT of PV Standalone System with TSK Fuzzy controller // International Journal of New Technologies in Science and Engineering (IJNTSE), 2022, Vol. 8, # 8, pp. 1–7.
7. Malla, S.G. et al., Whale Optimization Algorithm for PV Based Water Pumping System Driven by BLDC Motor Using Sliding Mode Controller // IEEE Journal of Emerging and Selected Topics in Power Electronics, 2022, Vol. 10, # 4, pp. 4832–4844, doi: 10.1109/JESTPE.2022.3150008
8. Dash, D.P., Bagarty, P.K., Hota, R.K. Behera Muduli, U.R. and Al Hosani, K. DC Offset Compensation for Three-Phase Grid-Tied SPV-DSTATCOM Under Partial Shading Condition with Improved PR Controller // IEEE Access, 2021, Vol. 9, pp. 132215–132224, doi: 10.1109/ACCESS.2021.3115122
9. Malla, S.G. Dadi P.K. and Dadi J. Wind and photovoltaic based hybrid stand-alone power generation system // 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), Chennai, India, pp. 3718–3725, doi: 10.1109/ICECDS.2017.8390158
10. Keerthana, K., Singaravelu, S. Enhancing the Robustness of P and O Algorithm-Based MPPT Control in Stand-Alone PV Systems through Fine Tuned PI Controller for Dynamic Load Variations // SSRG International Journal of Electronics and Communication Engineering, 2024, Vol. 11, # 6, pp. 9–19.
11. Adithya, B. et al. Energy Efficient Perturb and Observe Maximum Power Point Algorithm with Moving Average Filter for Photovoltaic Systems // International Journal of Renewable Energy Research, 2019, Vol. 9, # 1, pp. 207–214.
12. Rajesh, K., Ananyo, B., Aanchal S.S., Vardhan. Fuzzy Logic Controller and P&O-Based MPPT Techniques for Stand-Alone PV Systems: A Comparison // Research Square, 2024. https://doi.org/10.21203/ rs.3.rs‑4926323/v1
13. Ahmed, J.Y., Maan, A.A., Raed, S.A., Heider, N.B. Renewable Energy Sources in International Energy Reality and Prospects // International Journal of Innovation, Creativity and Change, 2020, (www.ijicc.net), Vol. 11, # 3.
14. Singh, S., Singh, P. and Said, Z. Solar Energy Applications // Nanotechnology Applications for Solar Energy Systems // Wiley, 2023, pp. 1–23, doi:10.1002/9781119791232.ch1
15. Daryaei, M., Esteki, M. and Khajehoddin, S.A. High Efficiency and Full MPPT Range Partial Power Processing PV Module-Integrated Converter // IEEE Trans. Power Electron., 2023, Vol. 38, # 5, pp. 6627–6641, doi: 10.1109/TPEL.2023.3243174
16. Khodair, D., Salem, M.S., Shaker, A., El Munim, E.A., and Abouelatta, M. Application of Modified MPPT Algorithms: A Comparative Study between Different Types of Solar Cells // Appl. Sol. Energy, Vol. 56, # 5, pp. 309–323, doi: 10.3103/S0003701X20050084
17. Manoharan, P. et al., Improved Perturb and Observation Maximum Power Point Tracking Technique for Solar Photovoltaic Power Generation Systems // IEEE Syst. J., 2021, Vol. 15, # 2, pp. 3024–3035, doi: 10.1109/JSYST.2020.3003255
18. Zebraoui, O. and Bouzi, M. Improved MPPT controls for a standalone PV/wind/battery hybrid energy system // Int. J. Power Electron. Drive Syst., 2020, Vol. 11, # 2, pp. 988–1001, doi: 10.11591/ijpeds.v11.i2
19. Khokhar, S.D., Peng, Q., Asif, A., Noor, M.Y., and Inam, A.A. Simple Tuning Algorithm of Augmented Fuzzy Membership Functions // IEEE Access, 2020, Vol. 8, pp. 35805–35814, doi: 10.1109/ACCESS.2020.2974533
20. Sharma, A.K. et al. Role of Metaheuristic Approaches for Implementation of Integrated MPPT-PV Systems: A Comprehensive Study // Mathematics, vol. 11, no. 2, p. 269, Jan. 2023, doi: 10.3390/math11020269
21. Subramanian, V., Indragandhi, V., Kuppusamy, R., and Teekaraman, Y. Modelling and Analysis of PV System with Fuzzy Logic MPPT Technique for a DC Microgrid under Variable Atmospheric Conditions // Electronics, 2021, Vol. 10, # 20, p. 2541, doi: 10.3390/electronics10202541
22. Nordin, N.A. et al. Integrating Photovoltaic (PV) Solar Cells and Supercapacitors for Sustainable Energy Devices: A Review // Energies, 2021, Vol. 14, # 21, p. 7211, doi: 10.3390/en14217211
23. Murtaza, A.F., Sher, H., Al-Haddad, A. K., and Spertino, F. Module Level Electronic Circuit Based PV Array for Identification and Reconfiguration of Bypass Modules // IEEE Trans. Energy Convers., 2021, Vol. 36, # 1, pp. 380–389, doi: 10.1109/TEC.2020.3002953
25. Barbosa, E.J., Cavalcanti M.C, de Souza Azevedo, G.M., Barbosa, A.O., Bradaschia, F. and Limongi, L.R., Global Hybrid Maximum Power Point Tracking for PV Modules Based on a Double-Diode Model // IEEE Access, 2021, Vol. 9, pp. 158440–158455, doi: 10.1109/ACCESS.2021.3131096
25. Motahhir, S., Abdelilah, C., Abdelaziz, G., Aziz D. Development of a Low-cost PV System using an improved INC algorithm and a PV panel Proteus model // Journal of Cleaner Production, 2018, doi: 10.1016/j.jclepro.2018.08.246
26. Mensia, N., Talbi, M. & Bouaicha, M. New Adaptive PI Controller for Photovoltaic Systems // Iran J. Sci. Technol. Trans. Electr. Eng., 2024, 48, pp. 1099–1110, https://doi.org/10.1007/s40998–024–00734‑w
27. Éric Schiller “ le pompage photovoltaïque”. Manuel de cours, Université d’Ottawa, Canada, https://www.pseau.org/outils/ouvrages/iepf_pompage_photovoltaique.pdf.
28. https://www.newport.com/medias/sys_master/images/images/h92/h23/8797062955038/Calibrating-Photovoltaic-Cells.pdf.

Intelligent systems for monitoring microclimate parameters represent a promising and rapidly evolving segment of the Russian market for measuring equipment. Their use is legally mandated in certain spheres. Strategic placement of sensors optimized using microclimate parameter maps and artificial intelligence enables efficient monitoring and analysis of temperature, humidity, light intensity, and other parameters based on large volumes of data. The objective of this study is to develop and test a microclimate monitoring system suitable for practical application in museums, exhibition spaces, and libraries. The presented system employs microclimate mapping and AI algorithms for monitoring and maintaining optimal conditions for the preservation and display of valuable objects. Particular attention is paid to monitoring UV irradiance and illuminance. Museum climatology classifies these parameters as microclimate, as they are critical to the preservation of exhibits and require special attention in lighting design and technology. The process of mapping microclimate parameters within museum, exhibition, and archive spaces involves several stages, including the examination of design documentation, the layout of engineering systems, and the specific characteristics of exhibits, particularly their sensitivity to light. The next step involves scanning the premises with measuring equipment. The collected data is then analysed and visualized as maps, illustrating the distribution of microclimate parameters within the room layout. This allows for the identification of problem areas and the implementation of measures to address them. For reliability and reduced time costs in analysing and constructing microclimate parameter distribution maps, it is advisable to employ AI algorithms. The article is dedicated to a new intelligent system developed to enhance the efficiency of specialists responsible for the preservation of exhibits. The results of the methodology validation and preliminary data from a pilot study are presented, confirming that the system significantly improves awareness and increases the accuracy of microclimate control.

1. Order of the Ministry of Culture of the Russian Federation of 23.07.2020 No. 827, “On the Approval of the Unified Rules for the Organization of the Acquisition, Registration, Storage, and Use of Museum Objects and Museum Collections”.
2. Dorokhov, V.B. Light and Climate: Regulatory Requirements and Practice of Cultural Valuables Preservation // Light & Engineering, 2023, Vol. 31, # 1, pp. 29–33.
3. Fedorova, I.V. Thermal and Humidity Conditions of the Object of Historical and Architectural Heritage in St. Petersburg [Teplovyye i vlazhnostnyye usloviya ob”yekta istoriko-arkhitekturnogo naslediya v Sankt-Peterburge] // Engineering Bulletin of the Don: electronic scientific journal, 2021, # 2.
4. Standard RF, GOST R 70835–2023. Museum lighting. LED lighting. Requirements / Moscow, Russian Standardization Institute, 2023. 24 p.
5. Bolotov, E.N. To Preserve the Heritage: Microclimate of Museums // АВОК, 2018, # 1, pp. 4–13.
6. Morzhukhin, D.E., Isaev, A.V., Balakhnina, E.E. Museum Lighting: Key Parameters and Their Control / Guidelines (Methodological Recommendations) / Ed. by A.V. Bogdanov, Saint Petersburg, 2024. 20 p.
7. CIE 157:2004. Control of damage to museum objects by optical radiation / Vienna Commission Internationale de l’Eclairage, 2004, 42 p.
8. Standard RF, GOST R 70836–2023. Museum lighting. LED lighting. Methods of normative performance measurements. Moscow, Russian Standardization Institute, 2023. 20 p.
9. Baev, S.S., Nikolaev, S.E., Barbar, Yu.A., Tomsky, K.A. Russian-Made Wireless Lighting and Microclimate Monitoring and Control Systems Accounting for Features of Museum Premises // Light & Engineering, 2023, Vol. 31, # 1, pp. 18–23.
10. Thomson, H. The Museum Environment // SKIFIA (SPb), 2005, 288 p.
11. Vitkovskaya, S.V., Kopytova, M.P., Tolstova, A.A. Environmental Approach in Designing and Expert Evaluation of the Museum Environment [Ekologicheskiy podkhod v proyektirovanii i ekspertize muzeynoy sredy] // Academic Bulletin of the UralNIIproekt RAASN, 2021, # 2, pp. 75–80.
12. Loose, V.V. Opportunities of the new remote monitoring system TVR “TKA Museum” for preventive conservation of museum objects in the conditions of estate exhibition and storage / Report at the Scientific and Practical Conference on Museum Activities and Scientific Work of the State Museum-Reserve “Ostanikino and Kuskovo”, 2022.

Aiming to realize the demand of healthy, comfortable, biorhythms, personalized and adaptive lighting environments, human-centric lighting has become hot-spots issue. Fortunately, Large Language Model (LLM) technology have already presented its prominent advantages and great application prospect in many fields. This paper proposes a novel indoor lighting control framework with context-aware-based LLM and domain knowledge base. The multi-source information perception, fusion and decision-making mechanism is established, which collects occupant behaviour and environment parameter, and enable dynamic modelling of lighting environment and indoor lighting intelligent control. The domain knowledge base is constructed by integrating national standard files in the field of lighting, and also Building Information Model (BIM). The context-aware prompting strategy is developed to achieve indoor lighting dynamic control with domain knowledge base. An experiment was implemented in an office scenario, which employed to validate the feasibility and effectiveness of the proposed ideas. This research can provide new ideas to improve human-centric lighting system.

1. Ahn, M., Brohan, A., Brown, N., Chebotar, Y. et al. Do As I Can, Not As I Say: Grounding Language in Robotic Affordances // Proc. of 6th Conf. on Robot Learning (CoRL), Auckland, New Zealand, 2022.
2. Baharudin, N.H., Mansur, T.M.N.T., Ali, R., Sobri, N.F.A. Smart lighting system control strategies for commercial buildings: a review // Int. J. of Advanced Technology and Engineering Exploration, 2021, Vol. 8, # 74, pp. 45–53.
3. Bellia, L., Fragliasso, F. New parameters to evaluate the capability of a daylight-linked control system in complementing daylight // Building and Environment, 2017, Vol. 123, pp. 223–242.
4. Bellia, L., Fragliasso, F. Evaluating performance of daylight-linked building controls during preliminary design // Automation in Construction, 2018, Vol. 93, pp. 293–314.
5. Boyce, P.R., Fotios, S., Richards, M. Roadmap for human-centric lighting research // LEUKOS – J. Illuminating Eng. Soc., 2019, Vol. 15, # 2–3, pp. 93–105.
6. Chinchero, H.F., Alonso, J.M., Ortiz, H. LED lighting systems for smart buildings: a review // IET Smart Cities, 2020, Vol. 2, # 3, pp. 126–134.
7. Cespedes-Cubides, A.S., Jradi, M. A review of building digital twins to improve energy efficiency in the building operational stage // Energy Informatics, 2024, Vol. 7, Art. 11.
8. Driess, D., Xia, F., Sajjadi, M., Lynch, C. et al. PaLM-E: An embodied multimodal language model // arXiv preprint arXiv:2303.03378, 2023.
9. Fang, P., Wang, M., Li, J., Zhao, Q., Zheng, X., Gao, H. A distributed intelligent lighting control system based on deep reinforcement learning // Applied Sciences, 2023, Vol. 13, # 16, Art. 9057.
10. Figueiro, M.G., Steverson, B., Heerwagen, B.W. et al. The impact of daytime light exposures on sleep and mood in office workers // Sleep Health, 2017, Vol. 3, # 3, pp. 204–215.
11. Houser, K.W., Esposito, T. Human-centric lighting: foundational considerations and a five-step design process // Frontiers in Neurology, 2021, Vol. 12, Art. 627554.
12. Hauer, M., Hammes, S., Zech, P., Geisler-Moroder, D., Plörer, D., Miller, J., van Karsbergen, V., Pfluger, R. Integrating digital twins with BIM for enhanced building control strategies: a systematic literature review focusing on daylight and artificial lighting systems // Buildings, 2024, Vol. 14, # 3, Art. 805.
13. Jalali, M.S., Jones, J.R., Tural, E., Gibbons, R.B. Human-centric lighting design: a framework for supporting healthy circadian rhythm grounded in established knowledge in interior spaces // Buildings, 2024, Vol. 14, # 4, Art. 1125.
14. Golda Jeyasheeli, P., Johnson Selva, J.V. An IoT design for smart lighting in green buildings based on environmental factors // Proc. of 4th Int. Conf. on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2017.
15. Kang, T.W., Mo, Y. A comprehensive digital twin framework for building environment monitoring with emphasis on real-time data connectivity and predictability // Journal of Building Engineering, 2022, Vol. 51, Art. 104767.
16. Liang, J., Huang, W., Xia, F., Xu, P., Hausman, K., Ichter, B., Florence, P., Zeng, A. Code as Policies: Language model programs for embodied control // Proc. of IEEE Int. Conf. on Robotics and Automation (ICRA), London, UK, 2023.
17. Linhart, F., Scartezzini, J.-L. Occupant preference of lighting environment in offices with daylighting controls // Energy and Buildings, 2006, Vol. 38, # 7, pp. 728–741.
18. Mahdavi, A., Berberidou, I., Lam, K.P. Artificial intelligence methods for building performance simulation: a lighting case study // J. of Building Performance Simulation, 2020, Vol. 13, # 5, pp. 575–585.
19. Mon-Williams, R., Li, G., Long, R., Du, W., Lucas, C. Embodied large language models enable robots to complete complex tasks in unpredictable environments // Nature Machine Intelligence, 2025, # 7, 592–601.
20. Ochoa, C.E., Aries, M.B.C., van Loenen, E.J., Hensen, J.L.M. Considerations on design optimization criteria for windows providing low energy consumption and high visual comfort // Applied Energy, 2012, Vol. 95, pp. 238–245.

This study investigates the impact of illumination levels on classification performance in microscopic cell images and proposes a novel approach for the deep learning-based diagnosis of white blood cells. The proposed method integrates statistical texture features derived from grayscale images based on GLCM 1 and LBP 2 with the Convolutional Block Attention Module (CBAM) in a hybrid architecture built upon the VGG16 model. In this architecture, both visual (RGB) and structural (texture) information are processed simultaneously, aiming to obtain deeper and more meaningful representations for classification. Within this scope, the effect of varying illumination levels on microscopic images has been evaluated in detail for the first time, and the model’s performance was tested under multiple lighting conditions.
As part of the study, nine different illumination conditions were created by adjusting light levels from –80 to +80, and the model’s classification performance under each condition was comprehensively evaluated. According to the findings, only 11 misclassifications occurred at the +40 light level, identifying this setting as the most optimal illumination condition, under which the model achieved the highest accuracy. When considering average performance metrics, the model achieved remarkable success with 99.46 % accuracy, 99.33 % F1‑score, 99.27 % recall, and 99.87 % specificity. Notably, the model performed almost flawlessly in classifying eosinophil and basophil cells.
It was concluded that hybridizing the CBAM attention mechanism with texture features significantly improved classification performance. Furthermore, the model’s robustness against illumination variability was supported by confusion matrices and error counts, emphasizing the critical role of lighting conditions in microscopic image analysis. In this regard, the study offers an innovative contribution to the literature both in terms of illumination optimization and multi-feature integration.

1. Kutlu, H., Avci, E., Özyurt, F. White blood cells detection and classification based on regional convolutional neural networks // Med Hypotheses, Churchill Livingstone, 2020, Vol. 135, p. 109472.
2. Sengur, A. et al. White Blood Cell Classification Based on Shape and Deep Features // 2019, International Conference on Artificial Intelligence and Data Processing Symposium, IDAP 2019, Institute of Electrical and Electronics Engineers Inc.
3. Navya, K.T., Prasad, K., Singh, B.M.K. Classification of blood cells into white blood cells and red blood cells from blood smear images using machine learning techniques // 2021, 2nd Global Conference for Advancement in Technology, GCAT 2021, Institute of Electrical and Electronics Engineers Inc.
4. Alkafrawi, I.M.I., Dakhell, Z.A. Blood Cells Classification Using Deep Learning Technique // Proceedings of 2022 International Conference on Engineering and MIS, ICEMIS 2022. Institute of Electrical and Electronics Engineers Inc.
5. Al-Dulaimi, K. et al. Segmentation of White Blood Cell, Nucleus and Cytoplasm in Digital Haematology Microscope Images: A Review-Challenges, Current and Future Potential Techniques // IEEE Rev Biomed Eng. IEEE Rev Biomed Eng, 2021. Vol. 14, pp. 290–306.
6. Khouani, A. et al. Automated recognition of white blood cells using deep learning // Biomed Eng Lett. Springer Verlag, 2020. Vol. 10, # 3, pp. 359–367.
7. Jiang, M. et al. White Blood Cells Classification with Deep Convolutional Neural Networks // Intern J. Pattern Recognit Artif Intell. World Scientific Publishing Co. Pte Ltd, 2018, Vol. 32, # 9.
8. Khan, S. et al. Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network // Comput Biol Med. Elsevier Ltd, 2024, Vol. 174, p. 108146.
9. Fırat, H. Classification of microscopic peripheral blood cell images using multibranch lightweight CNNbased model // Neural Comput Appl. Springer Science and Business Media Deutschland GmbH, 2024. Vol. 36, # 4, pp. 1599–1620.
10. Vogado, L. et al. Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification // Elsevier LHS, Aires Engineering Applications of Artificial Intelligence, 2018, Elsevier.
11. Palta, Olcay, Çıbuk, Musa; Güldemir, H. Image Enhancement in Whıte Blood Cells Usıng Hybrıd (Chale+Gauss) Fılter and Flask-Based Web Servıce Integratıon // 7 Internatıonal Medıterranean Congress. 2025, pp. 897–906.
12. Palta, Olcay; Çıbuk, Musa; Güldemir, H. Deep Learning-Based Classification of White Blood Cells: An Ensemble Model Approach // 7 Internatıonal Medıterranean Congress, 2025, pp. 262–270.
13. Cengiz, M.S. and Cengiz, C. Numerıcal Analysıs of Tunnel LED Lıghtıng Maıntenance Factor // IIUMEJ, Dec. 2018, Vol. 19, # 2, pp. 154–163. https://doi.org/10.31436/iiumej.v19i2.1007
14. Cengiz, Ç. Relationship of Braking Distance and Rear Headlight on Highways // Light & Engineering, 2024, Vol. 32, # 2, pp. 29–36.
15. Cengiz, Ç., Aydoğdu, H., GAMMA renewal functions in censored data // Bitlis Eren Universty Journal Science Technolgy, 2015, 5 (2), pp. 97–101.
16. Efe, S.B., Varhan, D. Interior Lighting of a Historical Building by Using LED Luminaires A Case Study of Fatih Pasha Mosque // Light & Engineering, 2020, Vol. 28, # 4, pp. 77–83.
17. Cengiz, M.S. Effects of Luminaire Angle and Illumination Topology on Illumination Parameters in Road Lighting // Light & Engineering, 2020, Vol. 28, # 4, pp. 47–56.
18. S. Rüstemli, S. and M.S. Cengiz, M.S. Actıve Fılter Solutıonsın Energy Systems // Turk. J. Elec. Eng. & Comp. Sci., 2015, Vol. 23, # 6, pp. 1587–1607, Nov. 2015.
19. Cengiz, M.S. The Relationship between Maintenance Factor and Lighting Level in Tunnel Lighting // Light & Engineering, 2019, Vol. 27, # 3, pp. 75–88.
20. Guler, O., Onaygil, S. The Effect of Luminance Uniformity on Visibility Level in Road Lighting // Lighting Research & Technology, 2003, Vol. 35, # 3, pp. 199–215.
21. Cengiz, M.S. and Rüstemli, S. Passive Filter Solutions and Simulation Performance in Industrial Plants // Bitlis Eren University Journal of Science and Technology, 2016, Vol. 6, # 1, pp. 39–43, doi: 10.17678/beujst.94574.https://doi.org/10.3906/elk-1402-212.
22. Wu, P. et al. Spectral-level assessment of light pollution from urban façade lighting // Sustain Cities Soc. Elsevier, 2023. Vol. 98, p. 104827.
23. Duranay, Z.B. and Guldemir, H. Power Prediction in Photovoltaic Systems with Neural Networks: A Multi-Parameter Approach // Applied Sciences, 2025, Vol. 15, # 7, p. 3615, doi: 10.3390/APP15073615
24. Dişli, F., Gedikpınar, M., Fırat, H., Şengür, A., Güldemir, H., and Koundal, D. Epilepsy Diagnosis from EEG Signals Using Continuous Wavelet Transform-Based Depthwise Convolutional Neural Network Model // Diagnostics 2025, Vol. 15, # 1, p. 84, doi: 10.3390/DIAGNOSTICS15010084
25. Aliser, A. and Duranay, Z.B. Fire/Flame Detection with Attention-Based Deep Semantic Segmentation // Iranian Journal of Science and Technology – Transactions of Electrical Engineering, 2024, Vol. 48, # 2, pp. 705–717, doi: 10.1007/S40998-024-00697-Y/TABLES/3
26. Praharsha, C.H., Poulose, A. CBAM VGG16: An efficient driver distraction classification using CBAM embedded VGG16 architecture // Comput. Biol. Med., Elsevier Ltd, 2024, Vol. 180.
27. Li, H. et al. Reducing Video Coding Complexity Based on CNN-CBAM in HEVC // Multidisciplinary Digital Publishing Institute, Applied Sciences, 2023, Vol. 13, p. 10135.
28. Cömert, Z., Efil, F., Türkoğlu, M. Convolutional Block Attention Module and Parallel Branch Architectures for Cervical Cell Classification // Int. J. Imaging Syst. Technol., 2025, Vol. 35, # 2.
29. Göçmen, R., Çıbuk, M., Akin, E. Comparative Analysis of Deep Learning Algorithms in Fire Detection // Balkan Journal of Electrical and Computer Engineering. MUSA YILMAZ, 2024, Vol. 12, # 3, pp. 255–261.
30. Erdem, B., Karabatak, M. Cheating Detection in Online Exams Using Deep Learning and Machine Learning // Applied Sciences (Switzerland), Multidisciplinary Digital Publishing Institute (MDPI), 2025. Vol. 15, # 1, p. 400.
31. Wang, S., Zhang, X., Zhao, R. Lightweight CNNCBAM-BiLSTM EEG emotion recognition based on multiband DE features // Biomed Signal Process Control, Elsevier Ltd, 2025, Vol. 103.
32. Acevedo, A. et al. Recognition of peripheral blood cell images using convolutional neural networks // Comput Methods Programs Biomed, Elsevier, 2019, Vol. 180, p. 105020.
33. Kaynaklı, M., Palta, O., Cengiz, Ç. Solar Radiation and Temperature Effects on Agricultural Irrigation Systems // Bitlis Eren University Journal of Science and Technology, Bitlis Eren University, 2016, Vol. 6, # 1, pp. 53–58.
34. Cengiz, M.S. Simulation and Design Study for Interior Zone Luminance in Tunnel Lighting // Light & Engineering, 2019, Vol. 27, # 2, pp. 42–51.
35.Yurci, Y., Kaynaklı, M., Palta, O., Efe, S. B., & Cengiz, Ç. The performance-cost effect of the scada system on distribution networks. //IOSR Journal of Electrical and Electronics Engineering, 2016, 11(6), pp. 32–38.
36. Cengiz, Ç. (2024). The Relationship Between Electricity Consumption from Outdoor Lighting and Economic Growth // Light & Engineering, 2024, Vol. 32, # 4, pp. 14–21
37.Macawile, M.J. et al. White blood cell classification and counting using convolutional neural network // 3rd International Conference on Control and Robotics Engineering, ICCRE 2018, Institute of Electrical and Electronics Engineers Inc., pp. 259–263.
38. Çınar, A., Tuncer, S.A. Classification of lymphocytes, monocytes, eosinophils, and neutrophils on white blood cells using hybrid Alexnet-GoogleNet-SVM // SN Appl. Sci., Springer Nature, 2021. Vol. 3, # 4, pp. 1–11.
39. Habibzadeh Motlagh, M. et al. Automatic white blood cell classification using pre-trained deep learning models: ResNet and Inception // SPIE, 2018, Vol. 10696, pp. 274–281, https://doi.org/10.1117/12.2311282
40. Vogado, L.H.S. et al. Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification // Eng. Appl. Artif. Intell., Pergamon, 2018, Vol. 72, pp. 415–422.
41. Hegde, R.B. et al. Feature extraction using traditional image processing and convolutional neural network methods to classify white blood cells: a study // Australas Phys. Eng. Sci/ Med., Springer Netherlands, 2019. Vol. 42, # 2, pp. 627–638.
42. Sharma, S. et al. [Retracted] Deep Learning Model for the Automatic Classification of White Blood Cells // Comput. Intell. Neurosci., John Wiley & Sons, Ltd, 2022, Vol. 2022, # 1, p. 7384131.
43. Chen, Y. et al. DAFFNet: A dual attention feature fusion network for classification of white blood cells // Biomed Signal Process Control, Elsevier, 2025. Vol. 106, p. 107699.
44. Fan, H. et al. LeukocyteMask: An automated localization and segmentation method for leukocyte in blood smear images using deep neural networks // J. Biophotonics, Wiley-VCH Verlag, 2019. Vol. 12, # 7, p. e201800488.
45. Khalil, A.J., Abu-Naser, S.S. Diagnosis of Blood Cells Using Deep Learning // International Journal of Academic Engineering Research (IJAER), 2022, Vol. 6, pp. 69–84.

Light pipes are one of the most effective methods for transmitting daylight into deep interior spaces. To adapt to complex building structures, it is recommended to use one or more elbows to redirect the light. However, it is essential to determine the light losses caused by adding elbows to the system, especially when compared to straight pipes of the same length. This study primarily aims to test different elbow configurations on light pipes. Additionally, it seeks to evaluate the efficiency trends associated with varying lengths of both straight and elbowed light pipes under the same configuration. Validated simulation tools and plugins, known for their accuracy, are employed as the primary method in this analysis. The core software suite includes Rhinoceros for 3D modelling, Grasshopper for algorithmic graphic editing, and Ladybug and Honeybee plugins for daylight simulation. Different configurations were analysed under clear and overcast sky conditions, based on maximum and average illuminance levels on the working plane. The results revealed that straight pipes with a short aspect ratio provided the highest levels of illuminance but caused glare on the working plane under clear sky conditions. For double-elbowed pipes, light losses increased significantly with pipe length compared to other typologies. But it was found that under clear sky conditions and in spaces where lower illumination levels are sufficient, the system performs adequately when the light transmission direction is deliberately altered.

1. Katia, R., Rakha, T. Evaluating Annual Sun Exposure in LEED v4 for Commercial Office Buildings: Inclusion of Annual Glare to Enhance the Occupant Visual Performance and Comfort // LEUKOS, 2024, 1–18. https://doi.org/10.1080/15502724.2024.2405490
2. Hosenuzzaman, M., Rahim, N. A., Selvaraj, J., Hasanuzzaman, M., Malek, A. A., Nahar, A. Global prospects, progress, policies, and environmental impact of solar photovoltaic power generation // Elsevier, Renewable and sustainable energy reviews, 2015, # 41, pp. 284–297. https://doi.org/10.1016/j.rser.2014.08.046
3. Goharian, A., Mahdavinejad, M. A novel approach to multi-apertures and multi-aspects ratio light pipe // Journal of Daylighting, 2020, 7 (2), pp. 86–200. https://doi.org/10.15627/jd.2020.17
4. Sharp, F., Lindsey, D., Dols, J., Coker, J. The use and environmental impact of daylighting // Journal of Cleaner Production, 2014, 85, pp. 462–471. https://doi.org/10.1016/j.jclepro.2014.03.092
5. Spacek, A.D., Neto, J.M., Biléssimo, L.D., Ando Junior O.H., Santana, M.V.F.D., Malfatti, C.D.F. Proposal of the tubular daylight system using acrylonitrile butadiene styrene (abs) metalized with aluminium for reflective tube structure // Energies, 2018, 11(1):199. https://doi.org/10.3390/en110101
6. Aizenberg, Y.B., Budak, V.P. The science of light engineering, fields of application and theoretical foundations // Light & Engineering, 2018, Vol. 26, # 3, pp. 4–6.
7. Budak, V.P., Aizenberg, J.B. The light field and the scope of light science // Light & Engineering, 2021, Vol. 29, # 1, pp. 4–10.
8. Zhang, X., Muneer, T, Kubie, J. A design guide for performance assessment of solar light-pipes // Lighting Research & Technology, 2002, Vol. 34, # 2, pp. 149–168. https://doi.org/10.1191/1365782802li041oa
9. Tsang, E.K., Kocifaj, M., Li, D.H., Kundracik, F., Mohelníková, J. 2018. Straight light pipes’ daylighting: A case study for different climatic zones // Solar Energy, 2018, # 170, pp. 56–63. https://doi.org/10.1016/j.solener.2018.05.042
10. Mayhoub, M.S. Innovative daylighting systems’ challenges: A critical study // Energy and Buildings, 2014, # 80, pp. 394–405. https://doi.org/10.1016/j.enbuild.2014.04.019
11. Aizenberg, J.B. Hollow Light Guides // Znack Publishing House, 2009, 208 p.
12. Aizenberg, J.B. International activity in field of light and engineering: creative report // Light & Engineering, 2020, Vol. 28, # 3, pp. 4–9.
13. Mahawan, J., Thongtha, A. Experimental investigation of illumination performance of hollow light pipe for energy consumption reduction in buildings // Energies, 2021, 1 (2), 260. https://doi.org/10.3390/en14020260
14. Sharma, L., Ali, S.F., Rakshit, D. Performance evaluation of a top lighting light-pipe in buildings and estimating energy saving potential // Energy and Buildings, 2018, # 179, pp. 57–72. https://doi.org/10.1016/j.enbuild.2018.09.022
15. Shin, J.Y., Yun, G.Y., Kim, J.T. Evaluation of daylighting effectiveness and energy saving potentials of light-pipe systems in buildings // Indoor and built environment, 2012, Vol. 21, # 1, pp. 129–136. https://doi.org/10.1177/1420326x11420011
16. Salam Azad, A., Salman, M., Kaushik, S. C., Rakshit, D. Energy saving potential of tubular light pipe system with different colours on internal surfaces // International Journal of Energy Sector Management, 2020, Vol. 14, # 4, pp. 793–837. https://doi.org/10.1108/ijesm‑12–2018–0001
17. Von Wachenfelt, H., Vakouli, V., Diéguez, A.P., Gentile, N., Dubois, M.C., Jeppsson, K.H. Lighting energy saving with light pipe in farm animal production // Journal of Daylighting, 2015, 2(2), pp. 21–31. https://doi.org/10.15627/jd.2015.5
18. Jenkins, D., Muneer, T. Modelling light-pipe performances – a natural daylighting solution // Building and environment, 2003, Vol. 38, # 7, pp. 965–972. https://doi.org/10.1016/s0360–1323(03)00061–1
19. Li, D.H., Tsang, E.K., Cheung, K.L., Tam, C.O. An analysis of light-pipe system via full-scale measurements // Applied Energy, 2010, Vol. 87, # 3, pp. 799–805. https://doi.org/10.1016/j.apenergy.2009.09.008
20. Garcia-Hansen, V., Edmonds, I. Methods for the illumination of multilevel buildings with vertical light pipes // Solar Energy, 2015, # 117, pp. 74–88. https://doi.org/10.1016/j.solener.2015.04.017
21. Darula, S., Kocifaj, M., Mohelníková, J. Hollow light guide efficiency and illuminance distribution on the light-tube base under overcast and clear sky conditions // Optik-International Journal for Light and Electron Optics, 2013, 124 (17), pp. 3165–3169. https://doi.org/10.1016/j.ijleo.2012.09.052
22. Malet-Damour, B., Guichard, S., Bigot, D., Boyer, H. Study of tubular daylight guide systems in buildings: Experimentation, modelling and validation // Energy and buildings, 2016, # 129, pp. 308–321. https://doi.org/10.1016/j.enbuild.2016.08.019
23. Garcia Hansen, V., Edmonds, I. Natural illumination of deep-plan office buildings: light pipe strategies // In ISES Solar World Congress 2003.
24. Mohelnikova. J.,, Vajkay F. Study of tubular light guides illuminance simulations // Leukos, 2009, 5 (4), pp. 267–277. https://doi.org/10.1582/LEUKOS.2008.05.04.001
25. Carter, D. LRT Digest 2 Tubular daylight guidance systems // Lighting Research & Technology, 2014, Vol. 46, # 4, pp. 369–387. https://doi.org/10.1177/1477153514526081
26. Baglivo, C., Bonomolo, M., Beccali, M., Congedo, P.M. Sizing analysis of interior lighting using tubular daylighting devices // Energy Procedia, 2017, 126, pp. 179–186. https://doi.org/10.1016/j.egypro.2017.08.138
27. Baglivo, C., Bonomolo, M., Congedo, P.M. Modelling of light pipes for the optimal disposition in buildings. Energies, 2019, 12(22):4323. https://doi.org/10.3390/en12224323
28. Kocifaj, M., Kundracik, F., Darula, S., Kittler, R. Availability of luminous flux below a bended lightpipe: design modelling under optimal daylight conditions // Solar Energy, 2012, 86(9), pp. 2753–2761. https://doi.org/10.1016/j.solener.2012.06.017
29. CIE 2006. CIE‑173 Technical Report: Tubular Daylight Guidance Systems. Vienna, Austria. https://doi.org/10.25039/tr.173.2006
30. Kocifaj. M., Kundracik, F., Darula, S., Kittler, R. Theoretical solution for light transmission of a bended hollow light guide // Solar Energy, 2010, 84(8), pp. 1422–1432. https://doi.org/10.1016/j.solener.2010.05.002
31. Wang, C., Gao, Q., Gao, W., Ouyang, J. Discussion about calculation method of light transmission efficiencies of elbows in cylindrical light pipes // Solar Energy, 2022, 238, pp. 39–43. https://doi.org/10.1016/j.solener.2022.04.024
32. Samuhatananon, S., Chirarattananon, S., Chirarattananon, P. An experimental and analytical study of transmission of daylight through circular light pipes // Leukos, 2011, 7 (4), pp. 203–219. https://doi.org/10.1080/15502724.2011.10732147
33. Jenkins, D., Muneer, T., Kubie, J. A design tool for predicting the performances of light pipes // Energy and buildings, 2005, Vol. 37, # 5, pp. 485–492. https://doi.org/10.1016/j.enbuild.2004.09.014
34. Carter, D.J. Tubular guidance systems for daylight: UK case studies // Building Research & Information, 2008, 36 (5), pp. 520–535. https://doi.org/10.1080/09613210801987855
35. Kocifaj, M., Darula. S., Kittler, R. HOLIGILM: Hollow light guide interior illumination method – An analytic calculation approach for cylindrical light-tubes // Solar energy, 2008, 82 (3), pp. 247–259. https://doi.org/10.1016/j.solener.2007.07.003
36. Verso, V.R.L., Pellegrino, A., Serra, V. Light transmission efficiency of daylight guidance systems: An assessment approach based on simulations and measurements in a sun/sky simulator // Solar Energy, 2011, 85 (11), pp. 2789–2801. https://doi.org/10.1016/j.solener.2011.08.017
37. Solatube, 2024a. Tubular Skylight. [accessed 2024 June 12] https://solatube.com/residential/tubular-skylights/.
38. Monodraught, 2024a. Sunpipe. [accessed 2024 June 12], from https://www.monodraught.com/products/natural-lighting.
39. Atalay, F., Kurtay, C. Daylight Performance of Elbow Geometry in Light Pipe Models // Periodica Polytechnica Architecture, 2023, 54 (3), pp. 207–214. https://doi.org/10.3311/PPar.22503
40. Rahmani, Asl. M., Zarrinmehr, S., Yan, W. Towards BIM-based parametric building energy performance optimization // In Proc. of the 33rd Annual Conference of the Association for Computer Aided Design in Architecture, Cambridge, 2013. https://doi.org/10.52842/conf.acadia.2013.101
41. Grasshopper, 2024. Grasshopper, Algorithmic Modelling for Rhino. [accessed 2023 June 12]. https://www.grasshopper3d.com/.
42. Roudsari, M.S., Pak, M. Ladybug: a parametric environmental plugin for grasshopper to help designers create an environmentally-conscious design // 2013, https://doi.org/10.26868/25222708.2013.249
43. Pilechiha, P., Mahdavinejad, M., Rahimian, F.P., Carnemolla, P., Seyedzadeh, S. Multi-objective optimisation framework for designing office windows: quality of view, daylight and energy efficiency // Applied Energy, 2019, # 261, p. 114356. https://doi.org/10.1016/j.apenergy.2019.114356
44. Kharvari, F. An empirical validation of daylighting tools: Assessing radiance parameters and simulation settings in Ladybug and Honeybee against field measurements // Solar Energy, 2020, 207, pp. 1021–1036. https://doi.org/10.1016/j.solener.2020.07.054
45. Shirzadnia, Z., Goharian A, Mahdavinejad M. Designerly approach to skylight configuration based on daylight performance; Toward a novel optimization process. Energy and Buildings, 2023, 286, p. 112970. https://doi.org/10.1016/j.enbuild.2023.112970
46. Reinhart, C., Breton, P. F. 2009. Experimental validation of Autodesk® 3ds Max® Design 2009 and DAYSIM 3.0 // Leukos, 2009, 6 (1), pp. 7–35. https://doi.org/10.1582/leukos.2009.06.01001
47. Ladybug. 2024a. EPWMap. [accessed 2023 June 12] https://www.ladybug.tools/epwmap/.
48. Ladybug. 2024b. Point-in-time Grid Based. [accessed 2024 June 1] https://docs.ladybug.tools/hb-radiance-primer/components/3_recipes/point-intime_grid-based.
49. IES. 2011. The Illuminating Engineering Society of North America. The Lighting Handbook 10th Edition, Reference and Application. IESNA,1328. https://doi.org/10.1177/1365782807079518

Experimental studies of the method of detection of oil pollution on the earth’s surface in the near-infrared range at different projective soil coverages by vegetation were carried out. It has been shown that with the increase of the projective coverage of vegetation, the spectral dips caused by absorption of radiation by oil pollution on the surface become less visible in the reflection spectra (both about 1.73 μm and 2.3 μm). However, when projective coverage of vegetation is even ~ 60 % these spectral dips are still visible and it is possible to detect contamination of the soil with petroleum products. With a very large (~ 100 %) projective coverage of the soil by the grass, the spectral dips caused by the absorption of radiation by oil pollution on the surface disappear almost completely and the probability of detecting soil contamination by oil products becomes very low. The best methods for detecting oil contamination in grass cover conditions is using a hydrocarbon index and using 20 spectral channels with a width of 10 nm in range (1.6–1.8) μm and 29 spectral channels with a width of 10 nm in the range (2.1–2.39) μm.

1. Karasik, V.E., Fedotov, Yu.V., Belov, M.L., Zhivotovsky, I.V., Sakharov, A.A. Selection and Justification of Optimal Spectral Wavelengths for Control of Methane Emission from an Advanced Nanosatellite // Light & Engineering, 2023, Vol. 31, # 5, pp. 143–152.
2. Kataev, M. Yu., Lukyanov, A.K. Simulation of Reflected Solar Radiation for Atmosphere Gas Composition Evaluation for Optical Remote Sensing from Space. // Light & Engineering, 2018, Vol. 26, # 3, pp. 14–21.
3. Belov, M.L., Vsyakova, Yu.I., Gorodnichev, V.A. Optical Method of Detection of Oil Contamination on Water Surface in UV Spectral Range. // Light & Engineering, 2019, Vol. 27, # 5, pp. 88–96.
4. Raputa, V.F., Kokovkin, V.V., Amikishieva, R.A. The ground and satellite monitoring of snow cover contamination in a cement plant environs [Nazemnyy i sputnikovyy monitoring zagryazneniya snezhnogo pokrova v rayone tsementnogo zavoda] // Optics of the Atmosphere and Ocean [Optika Atmosfery i Okeana], 2022, Vol. 35, # 6, pp. 495–499. DOI: 10.15372/AOO20220610
5. Nwakanma, N. M. C., Ikegwu, E., Osaigbovo, E.J. Genotoxic Effects of Spent Engine Oil (SEO) – Polluted Soils on Vernonia Amygdalina Del // International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2018, 6 (12), pp. 564–570.
6. Yuniati, M D. Bioremediation of petroleum-contaminated soil: A Review // IOP Conf. Ser.: Earth Environ. Sci., 2018, 118 012063, pp. 1–7.
7. Najoui, Z., Amoussou, N., Riazanoff, S., Aure, G., Frappart, F. Oil slicks in the Gulf of Guinea – 10 years of Envisat Advanced Synthetic Aperture Radar observations // Earth Syst. Sci. Data, 2022, # 14, pp. 4569–4588.
8. Roche, B. H. R., King, M.D. Quantifying the effects of background concentrations of crude oil pollution on sea ice albedo // The Cryosphere, 2022, 16, pp. 3949–3970.
9. Huettel, M. Oil pollution of beaches // Current Opinion in Chemical Engineering, 2022, 36 100803, 1–10.
10. Ismailova, N. M., Nadjafova, S.I. Experience in Assessing Environmental Risks of Main Oil Pipelines in Azerbaijan through the Prism of Soil Bio-geo-resistance to Crude Oil Pollution // Moscow University Soil Science Bulletin, 77(3), pp. 196–202.
11. Komene, G.L., Remi, C.O. Oil pollution crisis and relationship marketing approach of oil firms in Niger delta // British Journal of Management and Marketing Studies, 2022, 5(1), pp. 39–78.
12. Adegboye, M.A., Fung, W.-K., Karnik A. Recent advances in pipeline monitoring and oil leakage detection technologies: Principles and approaches // Sensors, 2019, 19, 2548, 1–36.
13. Sivokon, S., Andreev, N.N. Laboratory assessment of the efficiency of corrosion inhibitors at oilfield pipelines of the West Siberia region I. Objective setting I // Int. J. Corros. Scale Inhib., 2012, 1 (1), pp. 65–79.
14. Kühn, F., Oppermann, K., Hörig, B. Hydrocarbon Index – an algorithm for hyperspectral detection of hydrocarbons // International Journal of Remote Sensing, 2004, 25(12), pp. 2467–2473.
15. Andreoli, G., Bulgarelli, B., Hosgood, B., Tarchi, D. Hyperspectral Analysis of Oil and Oil-Impacted Soils for Remote Sensing Purposes // European commission joint Research centre, 2007, https://www.ugpti.org/smartse/research/citations/downloads/Andreoli-HSI_for_Oil_and_Spills‑2007.pdf (data of access 22.01.2024).
16. Allen, C.S., Satterwhite, M.B. Reflectance spectra of three liquid hydrocarbons on a common sand type // Proc. SPIE, 2006, Vol. 6233. pp. 62331M (1–12).
17. Brum, J., Schlegel, C., Chappell, C., Burke, M., and Krekeler, M.P.S. Reflective spectra of gasoline, diesel, and jet fuel on the sand substrates under ambient and cold conditions: Implications for detection using hyperspectral remote sensing and development of age estimation models // Environmental Earth Sciences, 2020, 79 463, pp. 1–14.
18. Allen, C.S., Krekeler, M.P.S. Reflectance spectra of crude oils and refined petroleum products on a variety of common substrates // Proc. SPIE., 2010, Vol. 7687, pp. 76870L (1–13).
19. Tian, Q. Study on oil-gas reservoir detecting methods using hyperspectral remote sensing // International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIXB7. XXII ISPRS Congress, Melbourne, Australia, 2012, pp. 157–162.
20. Pelta, R., Ben-Dor, E. An Exploratory Study on the Effect of Petroleum Hydrocarbon on Soils Using Hyperspectral Longwave Infrared Imagery // Remote Sensing, 2019, Vol. 11(569), pp. 1–15.
21. Del’Papa, R., Scafutto, M., de Souza Filho, C.R., de Oliveira, W.J. Hyperspectral remote sensing detection of petroleum hydrocarbons in mixtures with mineral substrates: Implications for onshore exploration and monitoring // ISPRS Journal of Photogrammetry and Remote Sensing, 2017, Vol. 128, pp. 146–157.
22. Keskin, G., Teutsch, C.D., Lenz, A., Middelmann, W. Concept of an advanced hyperspectral remote sensing system for pipeline monitoring // Proc. SPIE., 2015, Vol. 9644, pp. 96440H (1–9).
23. Achard, V., Foucher, P.Y., Dubucq, D. Hydrocarbon Pollution Detection and Mapping Based on the Combination of Various Hyperspectral Imaging Processing Tools // Remote Sensing, 2021, Vol. 13, # 5, 1020, pp. 1–28.
24. Cooled –20℃ NIR512–2.5 Spectrometer [Electronic resource]. URL: https://www.optosky.net/atp8000. html. Language English (accessed date 18,06,2025).
25. Nguyen, B.M., Fedotov, Yu.V., Baryshnikov, N.V., Belov, M.L. Influence of Soil Type on the Detection of Oil Spills in the Near Infrared Range // AIP Conf. Proc., 2025, Vol. 3268, 030016, pp. 030016 (1–6).
26. Nguyen, B.M., Fedotov, Yu.V., Baryshnikov, N.V., Belov, M.L. Experimental studies of a method for detecting oil pollution on the earth’s surface in the near-infrared range // Proc. SPIE., 2024, Vol. 13217, pp. 132170P (1–6).

This article explores the importance of energy performance contracts as a key tool for improving energy efficiency, demonstrating their special role in today’s economic and legal environment. The author identifies and analyses in detail the shortcomings of the current legislative definition of an energy performance contract, noting that the ambiguity and limitations of the regulatory definition have created legal uncertainty, contributing to conflicts between private and public interests, particularly in the context of government procurement. This paper substantiates the position that an energy performance contract, by its very nature, represents a special contractual structure, as energy performance activities can be implemented within contracts with various purposes – supply, contracting, leasing, and rental. The paper also substantiates the position on the need for legislative amendments to the definition of an energy performance contract. The article demonstrates that the proposed changes to the definition of an energy performance contract could create a more predictable and favourable regulatory environment for lighting equipment manufacturers. Shifting the emphasis from “actions aimed at energy conservation” to the civil law result (delivery of goods, performance of work, provision of services) removes the artificial narrowing of the contract to a contract for the provision of services for a fee and legitimizes the diversity of models, within which lighting modernization projects are actually implemented.

1. State report on the state of energy conservation and improvement of energy efficiency in the Russian Federation for 2023 [Electronic resource: https://www.economy.gov.ru/material/directions/investicionnaya_deyatelnost/povyshenie_energoeffektivnosti/gosudarstvennyy-doklad-o-sostoyanii-energosberezheniya-i-povyshenii-energeticheskoy-effektivnosti-v-rf-za‑2023‑god.html (date of access: 08.11.2025)].
2. Federal Law of 23.11.2009 No. 261-FZ “On Energy Saving and Improving Energy Efficiency and on Amendments to Certain Legislative Acts of the Russian Federation” // Collected Legislation of the Russian Federation, 2009, # 48, Art. 5711.
3. Resolution of the Arbitration Court of the Central District dated March 4, 2020 No. F10–557/2020 in case No. A48–2493/2019; Resolution of the Eleventh Arbitration Court of Appeal dated February 19, 2019, No. 11AP‑20978/2018 in case No. A72–9575/2018.
4. Decision of the Arbitration Court of Moscow dated November 23, 2020 in case No. A40–168626//2020–146–1244.
5. Resolution of the Twelfth Arbitration Court of Appeal dated November 16, 2020, No. 12AP‑8990/2020 in case No. A06–6382/2020; Resolution of the Twentieth Arbitration Court of Appeal dated December 7, 2021, No. 20AP‑1917/2021 in case No. A62–8544/2019.
6. Resolution of the Arbitration Court of the Central District dated October 19, 2021, No. F10–4360/2021 in case No. A09–7453/2017; Resolution of the Twentieth Arbitration Court of Appeal dated June 23, 2021, No. 20AP‑3060/2019 in case No. A09–7453/2017; Resolution of the Twentieth Arbitration Court of Appeal dated June 23, 2020, No. 20AP‑2813/2020 in case No. A62–8542/2019; Resolution of the Twentieth Arbitration Court of Appeal dated February 12, 2020 No. 20AP‑8633/2019 in case No. A09–7264/2015; Resolution of the Twentieth Arbitration Court of Appeal dated 09.04.2019 No. 20AP‑8472/2018 in case No. A09–13776/2015.
7. Resolution of the Seventeenth Arbitration Court of Appeal dated March 2, 2016, No. 17AP‑42/2016-GK in case No. A71–9967/2015.
8. Resolution of the Second Arbitration Court of Appeal dated February 11, 2016 No. 02AP‑11740/2015 in case No. A28–2830/2015; Decision of the Arbitration Court of Moscow dated December 9, 2020 in case No. A40–205802/20–72–1383.
9. Matveeva, E.Yu. On the issue of bank lending for energy service contracts // Banking law, 2021, # 5, pp. 21 – 27. DOI 10.18572//1812–3945–2021–5–21–27
10. Moles-Grueso, S., Bertoldi, P., Boza-Kiss, B. Energy Performance Contracting in the Public Sector of the EU – 2020 / JRC Science for Policy Report. Publications Office of the European Union, Luxembourg, 2021. DOI: 10.2760/751957
11. Resolution of the Government of the Russian Federation of September 26, 2016 No. 968 (as amended on May 15, 2019): On restrictions and conditions for the admission of certain types of radio-electronic products originating from foreign countries for the purposes of procurement to meet state and municipal needs.
12. Resolution of the Government of the Russian Federation of July 10, 2019 No. 878 (as amended on December 6, 2021): On measures to stimulate the production of radio-electronic products in the Russian Federation during the procurement of goods, works, and services to meet state and municipal needs, on amendments to Resolution of the Government of the Russian Federation of September 16, 2016 No. 925 and on the recognition of certain acts of the Government of the Russian Federation as invalid.
13. Letter of the Federal Antimonopoly Service of Russia dated September 16, 2020, No. IA/80326/20: On the application of legislation on the contract system in terms of procurement for energy efficiency measures.
14. Decision of the Supreme Court of the Russian Federation dated March 14, 2022, No. AKPI21–1068: On the refusal to satisfy the application to invalidate the letter of the FAS Russia dated September 16, 2020 No. IA/80326/20.
15. Appellate ruling of the Appellate Board of the Supreme Court of the Russian Federation dated July 26, 2022, No. APL22–245.
16. Matveeva, Elena Yu. Special contractual structures in the civil law of Russia (Dissertation for Doctor in Law degree, 5.1.3.) [Osobyye dogovornyye struktury v grazhdanskom prave Rossii] / Financial University under the Government of the Russian Federation, Moscow, 2024, 477 p.
17. Federal Law of 05.04.2013, No. 44-FZ (as amended on 16.04.2022): On the contract system in the sphere of procurement of goods, works, and services to meet state and municipal needs.
18. Federal Law of 27.12.2019, No. 449-FZ (as amended on 02.07.2021): On Amendments to the Federal Law “On the Contract System in the Sphere of Procurement of Goods, Works, and Services to Meet State and Municipal Needs”.
19. Matveeva, E. Yu. On the issue of bank lending for energy service contracts // Banking law, 2021, # 5, pp. 21–27. DOI 10.18572/1812–3945–2021–5–21–27.

Southern Algeria is distinguished by severe climatic conditions, notably persistent strong winds and extreme temperatures. These environmental factors frequently cause malfunctions in smart street lighting systems, leading to operational downtime that adversely affects the daily lives of citizens. During pilot testing of a smart street lighting system on a university campus, two critical challenges were identified: strong winds and heavy rainfall, both of which forced the system to suspend operation due to reduced performance under such conditions. To mitigate these issues, two deep learning models – Long Short-Term Memory (LSTM) and Convolutional Neural Network-LSTM (CNN-LSTM) – were trained and evaluated using a univariate dataset comprising only historical power output. The primary objective was to design a control strategy capable of dynamically regulating power levels to improve the system’s resilience to environmental variability. The findings indicate that both models delivered robust predictive performance; however, the LSTM model achieved superior accuracy compared to the CNN-LSTM, highlighting its effectiveness in optimizing smart street lighting systems under extreme climatic conditions while simultaneously promoting energy efficiency.

1. M’hamed, R., Boussemaha, B., Mouaadh, Y., Youcef, H., Mohammed, D. Low cost of smart unilateral street lighting system with photovoltaic power plant using particle swarm optimization // Studies in Engineering and Exact Sciences, 2024, Vol. 5, # 2, p. e11658.
2. Agramelal, F., Sadik, M., Moubarak, Y., Abouzahir, S. Smart Street Light Control: A Review on Methods, Innovations, and Extended Applications // Energies, 2023, Vol. 16, # 21, pp. 1–42.
3. Jackett, M., Frith, W. Quantifying the impact of road lighting on road safety – A New Zealand Study // IATSS Research., 2013, Vol. 36, # 2, pp. 139–145.
4. Viswanathan, S., Momand, S., Fruten, M., Alcantar, A. A model for the assessment of energy-efficient smart street lighting – a case study // Energy Efficiency, 2021, Vol. 14, # 6.
5. Pizzuti, S., Annunziato, M., Moretti, F. Smart Street Lighting Management // Energy Efficiency, 2013. V6, # 3, pp. 607–616.
6. Beccali, M., et al. A multifunctional public lighting infrastructure, design and experimental test // Journal of Sustainable Development of Energy, Water, and Environment Systems, 2017, Vol. 5, # 4, pp. 608–625.
7. Rojek, I., Mikołajewski, D., Galas, K., Piszcz, A. Advanced Deep Learning Algorithms for Energy Optimization of Smart Cities // Energies, 2025, Vol. 18, # 2.
8. Raza, M.Q., Khosravi, A. A review on artificial intelligence-based load demand forecasting techniques for smart grid and buildings // Renewable and Sustainable Energy Reviews, 2015, Vol. 50, pp. 1352–1372.
9. Alam, S., et al. Intelligent Streetlight Control System Using Machine Learning Algorithms for Enhanced Energy Optimization in Smart Cities // Journal of Ecohumanism, 2025, Vol. 4, # 4.
10. Durand, D., Aguilar, J., R-Moreno, M.D. An analysis of the energy consumption forecasting problem in smart buildings using LSTM // Sustainability, 2022, Vol. 14, # 20.
11. Jouahri, M.A., Boulghasoul, Z., Tajer, A. Smart control of electrical power in an LED lighting network taking into account road flow and meteorological conditions // Electrical Engineering, 2024. Vol. 106, # 5, pp. 5655–5675.
12. Gopalakrishnan, B., Dharshini, M., Devi, S.R., Gayathri, K. Prediction of street lights in metro cities using time series analysis in machine learning algorithm // International Journal of Aquatic Science, 2021. Vol. 12, # 3, pp. 1336–1345.
13. Amiri, A.F., Chouder, A., Oudira, H., Silvestre, S., Kichou, S. Improving photovoltaic power prediction: insights through computational modelling and feature selection // Energies, 2024, Vol. 17, # 13.
14. Nadweh, S., Mohammed, N., Konstantinou, C., Ahmed, S. Operational performance assessment of PVpowered street lighting: a comparative study of different machine learning prediction models // IEEE Access, 2025, Vol. 13.
15. Yang, G., Luo, Q., Wu, J. Automatic control method for street lights in unideal lighting environments based on deep learning // Light & Engineering., 2023, Vol. 31, # 6, pp. 100–108.
16. Bian, J., Yang, J.J. Smart Street lighting powered by renewable energy: a multi-criteria, data-driven decision framework // Sustainability, 2025, Vol. 17, # 13.
17. Sujana, J.A.J., Raj, R.V., Priya, V.K.R. Fog intelligence for energy efficient management in smart street lamps // Computing, 2024, Vol. 106, # 12, pp. 4057–4082.
18. Mouaadh, Y., Bousmaha, B., Mhamed, R. Intelligent control and reduce energy consumption of smart street lighting system // International Journal of Power Electronics and Drive Systems (IJPEDS), 2022, Vol. 13, # 4, pp. 1966–1974.
19. Arpit, D., Kanuparthi, B., Kerg, G., Ke, N.R., Mitliagkas, I., Bengio, Y. h-detach: Modifying the LSTM gradient towards better optimization // arXiv Prepr., 2018. arXiv:1810.03023.
20. Elmaz, F., Eyckerman, R., Casteels, W., Latré, S., Hellinckx, P. CNN-LSTM architecture for predictive indoor temperature modelling // Building and Environment, 2021, Vol. 206, p.108327.
21. Kim, T.Y., Cho, S.B. Predicting residential energy consumption using CNN-LSTM neural networks // Energy, 2019, Vol. 182, pp. 72–81.
22. Lee, S.W., Kim, H.Y. Stock market forecasting with super-high dimensional time-series data using ConvLSTM, trend sampling, and specialized data augmentation // Expert Systems with Applications, 2020, Vol. 161, p.113704.
23. Tukymbekov, D., Saymbetov, A., Nurgaliyev, M., Kuttybay, N., Dosymbetova, G., Svanbayev, Y. Intelligent autonomous street lighting system based on weather forecast using LSTM // Energy, 2021, Vol. 231, p.120902.
24. Islam, M.Z., Islam, M.M., Asraf, A. A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID‑19) using X-ray images // Informatics in Medicine Unlocked, 2020, Vol. 20, p.100412.
25. Wang, J., Yu, L.C., Lai, K.R., Zhang, X. Dimensional sentiment analysis using a regional CNN-LSTM model // Proc. 54th Annu. Meet. Assoc. Comput. Linguist. (ACL), 2016, pp. 225–230.
26. Yaichi, M., Bouchiba, B., Rebhi, M. IoT-based smart street lighting system and enhanced energy saving using ESP-NOW protocol // Light & Engineering, 2024, Vol. 32, # 6, pp. 76–84.
27. Agga, A., Abbou, A., Labbadi, M., El Houm, Y. Short-term self-consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models // Renewable Energy, 2021, Vol. 177, pp. 101–112.
28. Tsalikidis, N., Mystakidis, A., Tjortjis, C., Koukaras, P., Ioannidis, D. Energy load forecasting: one-step ahead hybrid model utilizing ensembling // Computing, 2024. Vol. 106, # 1, pp. 1–33.

Facades, as a building component, directly impact indoor comfort conditions. Different facades systems have been developed not only to cope with thermal and visual comfort aspects, but also for energy efficiency, use of daylight, and natural ventilation. Designers consider Double Skin Facade (DSF) systems in educational buildings to improve the abovementioned performance aspects. Recent works deal with a fixed room depth for proposed parametric DSF models, considering fundamental versions of well-known optimisation algorithms. Since the educational buildings require various room depths based on the function of the building, optimum daylight performance may not be achieved with the same cavity gap utilizing only one well-known solver. This study proposes a computational framework to cope with this problem in DSFs for educational buildings. In this context, the study suggests a parametric educational space with a DSF system and optimised seven design parameters, including twelve room depth scenarios by using three single objective optimisation algorithms. The study suggests optimised design scenarios for spatial Daylight Autonomy (sDA) and Annual Sunlight Exposure (ASE) in the region between 35o – 40o N latitudes of the Mediterranean CSA climate type 1. Although previous works have frequently used the genetic algorithm (GA) to solve DSF design problems, results showed that GA is unsuitable for coping with sDA and ASE. The results indicated that a single opening is insufficient to provide optimum sDA and ASE in educational buildings with DSF systems, which room depth of more than 7 m.

1. Blanco, J. M., Arriaga, P., Rojí, E., Cuadrado, J. Investigating the thermal behaviour of double-skin perforated sheet facades: Part a: Model characterisation and validation procedure // Building and Environment, 2014, Vol. 82, pp. 50–62.
2. Safer, N., Woloszyn, M., Roux, J.J. Three-dimensional simulation with a cfd tool of the airflow phenomena in single floor double-skin facade equipped with a venetian blind // Solar Energy, 2005, Vol. 79, # 2, pp. 193–203.
3. Ghaffarianhoseini, A. Ghaffarianhoseini, A., Berardi, U. Tookey, J., Li, D. H. W., Kariminia, S. Exploring the advantages and challenges of double-skin facades (Dsfs) // Renewable And Sustainable Energy Reviews, 2016, Vol. 60, pp. 1052–1065.
4. WELL, Daylight Modelling, https://standard.wellcertified.com/light/daylight-modeling; 2024 (accessed on 3 April 2024).
5. BREEAM, Daylight 4a/c, https://climatestudiodocs.com/docs/daylightBREEAM4a.html; 2024 (accessed on 3 April 2024).
6. Gass, S.I. Making Decisions with Precision // Business Week October 30, 2000. https://www.bloomberg.com/news/articles/2000–10–29/making-decisions-withprecision; 2024 (accessed on 3 April 2024).
7. Winston, W.L. Operations research: applications and algorithms // International Thomson Publishing, Belmont, CA., 2003, Vol. 4.
8. Shameri, M. A., Alghoul, M. A., Elayeb, O., Zain, M. F. M., Alrubaih, M. S., Amir, H., Sopian, K. Daylighting characteristics of existing double-skin facade office buildings // Energy and Buildings, 2013, Vol. 59, pp. 279–286.
9. Ghonimi, I. Assessing Daylight performance of single vs. double skin facade in educational buildings: A comparative analysis of two case studies // Journal of Sustainable Development, 2017, Vol. 10, # 3, pp. 133–142.
10. Saad, M. M., Araji, M.T. Optimisation of double skin facades with integrated renewable energy source in cold climates // In ASHRAE Topical Conference Proceedings (pp. 366–373). American Society of Heating, Refrigeration and Air Conditioning Engineers, Inc., 2020.
11. Alakavuk, A. P. D.E. Energy and daylight performance analysis of double skin facade systems in hot arid climate: a case study in Kano, Nigeria // Engineering On Energy Materials, 2020. Vol. 97.
12. Dewi, O. C., Rahmasari, K., Hanjani, T. A., Ismoyo, A. D., Dugar, A.M. Window-to-wall ratio as a mode of daylight optimisation for an educational building with opaque double-skin façade // Journal of Sustainable Architecture and Civil Engineering, 2022, Vol. 30, # 1, pp. 142–152.
13. Lorenzo, V., Hendarti, R., Mariana, Y. Double skin facade effect on optimising daylighting in SOHO apartment south Jakarta // In AIP Conference Proceedings, 2023, Vol. 2594, # 1, AIP Publishing.
14. Wolpert, D. H., & Macready, W.G. No free lunch theorems for optimization // IEEE transactions on evolutionary computation, 2002, Vol. 1, # 1, pp. 67–82.
15. Goharian, A., Daneshjoo, K., Yeganeh, M. Standardisation of methodology for optimising the well aperture as device (reflector) for light-wells; a novel approach using Honeybee & Ladybug plug-ins // Energy Reports, 2022, Vol. 8, pp. 3096–3114.
16. Standard ANSI/IES LM‑83–12: Approved method: IES spatial Daylight autonomy (sDA) and annual sunlight exposure (ASE) / 2013 Illum. Eng. Soc.; https//www ies org/product/ies-spatial-daylight-autonomy-sda-andannual-sunlight-exposure-ase.
17. Kremelberg, D. Pearson’s, chi-square, t-test, and ANOVA. Practical statistics: A quick and easy guide to IBM® SPSS® statistics / STATA, and other statistical software, 2011, 119–204.
18. Opossum – Optimization Solver with Surrogate Models (by Opossum. Food4rhino. https://www.food4rhino.com/en/app/opossum-optimization-solver-surrogatemodels; 2024 (accessed on 6 April 2024).
19. Rutten, D. Galapagos: On the logic and limitations of generic solvers // Architectural Design, 2013, V0l. 83, # 2, pp. 132–135.
20. Wolpert, D. H., Macready, W.G. No free lunch theorems for optimization // IEEE Transactions on Evolutionary Computation, 1997, Vol. 1, # 1, pp. 67–82.
21. Coit, D. W., Smith, A.E. Penalty guided genetic search for reliability design optimization // Computers & Industrial Engineering, 1996, Vol. 30, # 4, pp. 895–904.
22. Vatin, N., Murgul, V., Penić, M. Double skin facades in energy efficient design // Applied Mechanics and Materials, 2014, Vol. 680, pp. 534–538.
23. Ekici, B., Cubukcuoglu, C., Turrin, M., & Sariyildiz, I.S. Performative computational architecture using swarm and evolutionary optimisation: A review // Building and environment, 2019, Vol. 147, pp. 356–371.

Mustafa Serhan Unluturk, Zehra Tugce Kazanasmaz, Berk Ekici, and Turkan Goksal Ozbalta A Computational Framework for Optimising Daylight Performance of Double Skin Facades in Educational Buildings: Case of Mediterranean Climate

Fatmanur Atalay and Cuneyt Kurtay Findings from Daylight Simulations on Straight and Elbowed Light Pipes

George V. Boos, Vladimir P. Budak, Ruzanna A. Delyan, Dmitry S. Zakhidov, and Artyom V. Pavlov Transformations of Mac-Adam Ellipses Across a Wide Range of Luminance Levels

Albert A. Ashryatov, Dinara K. Churakova, and Evgeny A. Kuznetsov Research into the Effect of LED Retrofit Lamps Optical System Design on their Luminous Characteristics

Mourad Talbi, Nawel Mensia, and Abdelmajid Zairi Real-Time Implementation of a Solar Photovoltaic System Employing Arduino Card