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Assessment of indoor illuminance and study on best photosensors' position for design and commissioning of Daylight Linked Control systems. A new method based on artificial neural networks

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  • Beccali, M.
  • Bonomolo, M.
  • Ciulla, G.
  • Lo Brano, V.

Abstract

Artificial lighting systems have to ensure appropriate illuminance with high energy efficiency according to best design practice and technical standards. These aims can be tackled, by incorporating a Daylight linked control system. However, the system behaviour is strongly influenced by several factors and, in particular, by the sensors' position. Indeed, very often the illuminance on work-plane is not fully correlated with illuminance measured by the photo-sensor used to control the luminaires. This fact leads to wrong information for the Daylight linked control systems affecting its efficacy. The artificial intelligence of Neural Networks can be exploited to provide a method for finding good relationships between the illuminance on workplane and the one measured in another surface. Artificial Neural Networks are able to process complex data set and to give as output the illuminance in a point. By the use of measured values in an experimental set up, the output of several Artificial Neural Networks related to different sensors placements have been analysed. In this way it was possible to find the position of the photo-sensor associated to the best forecast of the workplane illuminance with a mean square error of 2.20 E−3 and R2 of 0.9583.

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  • Beccali, M. & Bonomolo, M. & Ciulla, G. & Lo Brano, V., 2018. "Assessment of indoor illuminance and study on best photosensors' position for design and commissioning of Daylight Linked Control systems. A new method based on artificial neural networks," Energy, Elsevier, vol. 154(C), pages 466-476.
    • Handle: RePEc:eee:energy:v:154:y:2018:i:c:p:466-476
    DOI: 10.1016/j.energy.2018.04.106
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    Cited by:

    1. Aris Tsangrassoulis & Lambros Doulos & Angelos Mylonas, 2021. "Simulating the Impact of Daytime Calibration in the Behavior of a Closed Loop Proportional Lighting Control System," Energies, MDPI, vol. 14(21), pages 1-22, October.
    2. Evangelos-Nikolaos D. Madias & Lambros T. Doulos & Panagiotis A. Kontaxis & Frangiskos V. Topalis, 2022. "Multicriteria decision aid analysis for the optimum performance of an ambient light sensor: methodology and case study," Operational Research, Springer, vol. 22(2), pages 1333-1361, April.
    3. Bonomolo, Marina & Zizzo, Gaetano & Ferrari, Simone & Beccali, Marco & Guarino, Stefania, 2021. "Empirical BAC factors method application to two real case studies in South Italy," Energy, Elsevier, vol. 236(C).
    4. Babak Zandi & Adrian Eissfeldt & Alexander Herzog & Tran Quoc Khanh, 2021. "Melanopic Limits of Metamer Spectral Optimisation in Multi-Channel Smart Lighting Systems," Energies, MDPI, vol. 14(3), pages 1-16, January.

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