Document Type
Conference Proceeding
Publication Date
1-1-2023
Abstract
Lighting is responsible for 17% of the total electricity consumption in commercial buildings in the United States. Investigating the lighting energy load provides the potential for more energy-saving in commercial buildings. Nonetheless, the development of lighting load prediction models has received limited attention in extant literature. This study proposes a framework to predict the lighting schedule and load in office buildings by integrating an agent-based model into an artificial neural network model. A small office building is used as a case study to simulate lighting load based on occupancy information using an agent-based model. Then, an artificial neural network model is developed to predict the simulated lighting energy load. The results illustrated that the accuracy of the prediction model could be as high as 92.8%. The developed model can be used by facility managers and engineers to accurately predict the lighting energy load in office environments.
Publication Source (Journal or Book title)
Proceedings of the European Conference on Computing in Construction
Recommended Citation
Vosoughkhosravi, S., Norouziasl, S., & Jafari, A. (2023). LIGHTING ENERGY LOAD PREDICTION FRAMEWORK USING AGENT-BASED SIMULATION AND ARTIFICIAL NEURAL NETWORK MODELS. Proceedings of the European Conference on Computing in Construction https://doi.org/10.35490/EC3.2023.163