Multi-source domain generalization deep neural network model for predicting energy consumption in multiple office buildings

Jiang, Ben, Li, Yu, Rezgui, Yacine ORCID: https://orcid.org/0000-0002-5711-8400, Zhang, Chengyu, Wang, Peng and Zhao, Tianyi 2024. Multi-source domain generalization deep neural network model for predicting energy consumption in multiple office buildings. Energy 299 , 131467. 10.1016/j.energy.2024.131467 Item availability restricted.

PDF - Accepted Post-Print Version Restricted to Repository staff only until 30 April 2025 due to copyright restrictions. Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (2MB)

Abstract

The effective prediction of building energy consumption can be used to optimize building operating modes and reduce the overall energy consumption and carbon emission of the building. To predict the energy consumption of the same type of building in the same area, it is often necessary to train separate prediction models for different buildings, which is usually costly computationally in order to get better prediction results. Therefore, this study will combine deep neural networks and multi-source domain generalization in an encoder-decoder architecture to train a model that can be directly applied to predict the energy consumption of multiple buildings in same type. In order to validate the predictive effectiveness of the model, it will be tested on real office building energy consumption dataset and different comparative experiments will be designed on the source and target domains. The results show that the constructed multi-source domain generalization model is able to accurately predict the energy consumption trend of different buildings 1 h in advance. It also has some energy consumption prediction ability for the unknow training set of buildings.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0360-5442
Date of First Compliant Deposit:	30 April 2024
Date of Acceptance:	27 April 2024
Last Modified:	10 Nov 2024 08:45
URI:	https://orca.cardiff.ac.uk/id/eprint/168503

Actions (repository staff only)

Edit Item