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Building Energy System Modeling using Machine Learning

Predicting the future state and energy consumption of office buildings with an interpretable model can be highly useful for both study by designers of buildings and control systems alike. We present a comparison of different modeling approaches, from recurrent neural networks to a hybrid modeling approach which combines an accurate deep state predictor with an easily interpretable linear energy predictor.

Team Members: 

Sameer Bibikar

Rohan Koripalli

Srinjoy Majumdar

Christopher Mao

Vivian Nguyen

Kunpeng Qin