Version 1
: Received: 26 September 2024 / Approved: 27 September 2024 / Online: 29 September 2024 (03:42:44 CEST)
How to cite:
Mahajan, A.; Das, S.; Su, W.; Bui, V.-H. Bayesian Neural Network-Based Approach for Probabilistic Prediction of Building Energy Demands. Preprints2024, 2024092211. https://doi.org/10.20944/preprints202409.2211.v1
Mahajan, A.; Das, S.; Su, W.; Bui, V.-H. Bayesian Neural Network-Based Approach for Probabilistic Prediction of Building Energy Demands. Preprints 2024, 2024092211. https://doi.org/10.20944/preprints202409.2211.v1
Mahajan, A.; Das, S.; Su, W.; Bui, V.-H. Bayesian Neural Network-Based Approach for Probabilistic Prediction of Building Energy Demands. Preprints2024, 2024092211. https://doi.org/10.20944/preprints202409.2211.v1
APA Style
Mahajan, A., Das, S., Su, W., & Bui, V. H. (2024). Bayesian Neural Network-Based Approach for Probabilistic Prediction of Building Energy Demands. Preprints. https://doi.org/10.20944/preprints202409.2211.v1
Chicago/Turabian Style
Mahajan, A., Wencong Su and Van-Hai Bui. 2024 "Bayesian Neural Network-Based Approach for Probabilistic Prediction of Building Energy Demands" Preprints. https://doi.org/10.20944/preprints202409.2211.v1
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. The 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
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
