Affiliation:
1. School of Architecture, Harbin Institute of Technology, Harbin 150001, China
2. Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, 66 Xi Dazhi Street, Harbin 150006, China
Abstract
Building energy consumption prediction models are powerful tools for optimizing energy management. Among various methods, artificial neural networks (ANNs) have become increasingly popular. This paper reviews studies since 2015 on using ANNs to predict building energy use and demand, focusing on the characteristics of different ANN structures and their applications across building phases—design, operation, and retrofitting. It also provides guidance on selecting the most appropriate ANN structures for each phase. Finally, this paper explores future developments in ANN-based predictions, including improving data processing techniques for greater accuracy, refining parameterization to better capture building features, optimizing algorithms for faster computation, and integrating ANNs with other machine learning methods, such as ensemble learning and hybrid models, to enhance predictive performance.
Funder
the National Natural Science Foundation of China
Reference244 articles.
1. Trees vs Neurons: Comparison between Random Forest and ANN for High-Resolution Prediction of Building Energy Consumption;Ahmad;Energy Build.,2017
2. Application and Characterization of Metamodels Based on Artificial Neural Networks for Building Performance Simulation: A Systematic Review;Roman;Energy Build.,2020
3. Building Energy Prediction Using Artificial Neural Networks: A Literature Survey;Lu;Energy Build.,2022
4. Overview of the Use of Artificial Neural Networks for Energy-Related Applications in the Building Sector;Guyot;Int. J. Energy Res.,2019
5. Md Ramli, S.S., Nizam Ibrahim, M., Mohamad, A., Daud, K., Saidina Omar, A.M., and Ahmad, N.D. (2023, January 6–7). Review of Artificial Neural Network Approaches for Predicting Building Energy Consumption. Proceedings of the 2023 IEEE 3rd International Conference in Power Engineering Applications (ICPEA), Putrajaya, Malaysia.