Proceedings of the 2023 European Conference on Computing in Construction and the 40th International CIB W78 Conference
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Lighting energy load prediction framework using agent-based simulation and artificial neural network models
Sorena Vosoughkhosravi, Seddigheh Norouziasl, Amirhosein JafariLouisiana State University, Baton Rouge, LA, United States of AmericaDOI: 10.35490/EC3.2023.163Abstract: Lighting is responsible for 17% of the total electricity consumption in commercial buildings in the United States. Investigating and better understanding the lighting energy load provides the potential for more energy-saving in commercial buildings. 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. The results illustrated that the accuracy of the prediction model could be as high as 92.8%. Keywords: Lighting, ANN, Agent-Based, Modeling, PredictionPaper:EC32023_163