Optimizing Photosensor Placement for Energy-Efficient Lighting in Sustainable Building Design based on Multivariate Long Short-Term Memory Models

In sustainable building design, optimizing lighting systems is essential to reducing energy consumption while maintaining occupant comfort. Photosensors, which guide the lighting control system in adjusting illumination based on ambient light levels, are critical in achieving energy efficiency and visual comfort. However, the optimal placement of these sensors is a complex task due to the dynamic and multidimensional nature of lighting conditions within a building. This study presents a novel approach for optimizing photosensor placement using multivariate Long Short-Term Memory (LSTM) models. Unlike conventional methods, LSTM models leverage historical data on indoor light patterns from photosensors and sunlight factors to capture long-term dependencies in time-series data. The proposed method is distinguished by its capacity to anticipate future lighting conditions, thereby enabling the system to adopt a proactive approach to environmental variations, representing a notable advancement over traditional reactive models. This approach allows for more accurate forecasts by accounting for past fluctuations in light conditions and associated environmental variables. The proposed method seeks to determine the optimal sensor placement, maximizing energy savings by ensuring efficient use of natural light while minimizing artificial lighting and maintaining visual comfort. Simulation results demonstrate significant improvements in energy efficiency compared to traditional sensor placement strategies, making this approach a promising solution for sustainable building design. The study highlights the importance of integrating advanced machine learning techniques like LSTM to enhance energy performance and sustainability in modern buildings, also looking at user satisfaction regarding visual comfort.