Bayesian-Neural-Network-Based Approach for Probabilistic Prediction of Building-Energy Demands

Akash Mahajan, Srijita Das, Wencong Su () and Bui Van-Hai ()
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Akash Mahajan: Department of Computer and Information Science, College of Engineering and Computer Science, University of Michigan-Dearborn, Dearborn, MI 48128, USA
Srijita Das: Department of Computer and Information Science, College of Engineering and Computer Science, University of Michigan-Dearborn, Dearborn, MI 48128, USA
Wencong Su: Department of Electrical and Computer Engineering, College of Engineering and Computer Science, University of Michigan-Dearborn, Dearborn, MI 48128, USA
Bui Van-Hai: Department of Electrical and Computer Engineering, College of Engineering and Computer Science, University of Michigan-Dearborn, Dearborn, MI 48128, USA

Sustainability, 2024, vol. 16, issue 22, 1-21

Abstract: Reliable prediction of building-level energy demand is crucial for the building managers to optimize and regulate energy consumption. Conventional prediction models omit the uncertainties associated with demand over time; hence, they are mostly inaccurate and unreliable. In this study, a Bayesian neural network (BNN)-based probabilistic prediction model is proposed to tackle this challenge. By quantifying the uncertainty, BNNs provide probabilistic predictions that capture the variations in the energy demand. The proposed model is trained and evaluated on a subset of the building operations dataset of Lawrence Berkeley National Laboratory (LBNL), Berkeley, California, which includes diverse attributes related to climate and key building-performance indicators. We have performed thorough hyperparameter tuning and used fixed-horizon validation to evaluate trained models on various test data to assess generalization ability. To validate the results, quantile random forest (QRF) was used as a benchmark. This study compared BNN with LSTM, showing that BNN outperformed LSTM in uncertainty quantification.

Keywords: Bayesian neural network; deep learning; building systems; energy demand; probabilistic prediction; time series data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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