Prediction of energy performance of residential buildings using regularized neural models

Siwach, Komal; Kumar, Harsh; Rawal, Nekram; Singh, Kuldeep; Rawat, Anubhav

Abstract

Human habitats are one of the major consumers of energy. Therefore, in the current age of increasing carbon footprints, analyzing energy efficiency of a building is imminent, which has been taken up in the current work. Machine learning based Artificial Neural Network-ANN approach is used in the current work to study building-energy-performance. Total eight parameters; relative compactness, surface area, wall area and roof area of the building, overall height, and orientation of the building, glazing area and its distribution are selected as the input parameters and heating and cooling loads as the output parameters. The network prediction capability was checked by comparing the predictions of the ANN architecture with the benchmark test case. A well trained and validated ANN is used to predict 96 conditions by varying glazing area and glazing area distribution. ANN is found to capture the physics efficiently. This study revealed that there is a significant potential to improve the energy efficiency of the building and the maximum saving in the cooling load can be as high as 20.67% for a fraction of the glazing areas equal to 0.15 if glazing area distribution is kept 32.5% in North, and 22.5% each in the East, South and West.

Citation

Siwach, K., Kumar, H., Rawal, N., Singh, K., & Rawat, A. (2024). Prediction of energy performance of residential buildings using regularized neural models. Proceedings of the Institution of Civil Engineers - Energy, 177(3), 98-117. https://doi.org/10.1680/jener.23.00017

• Journal Article Type: Article
• Acceptance Date: Nov 10, 2023
• Online Publication Date: Nov 10, 2023
• Publication Date: 2024-07
• Deposit Date: Nov 23, 2023
• Publicly Available Date: Nov 11, 2024
• Journal: Proceedings of the Institution of Civil Engineers - Energy
• Print ISSN: 1751-4223
• Electronic ISSN: 1751-4231
• Publisher: Institution of Civil Engineers (ICE)
• Peer Reviewed: Peer Reviewed
• Volume: 177
• Issue: 3
• Pages: 98-117
• DOI: https://doi.org/10.1680/jener.23.00017
• Keywords: General Energy, Renewable Energy, Sustainability and the Environment
• Public URL: https://nottingham-repository.worktribe.com/output/27583952
• Publisher URL: https://www.icevirtuallibrary.com/doi/10.1680/jener.23.00017