Forecasting building plug load electricity consumption employing occupant-building interaction input features and bidirectional LSTM with improved swarm intelligent algorithms

Zhang, Chengyu, Ma, Liangdong, Luo, Zhiwen ORCID: https://orcid.org/0000-0002-2082-3958, Han, Xing and Zhao, Tianyi 2024. Forecasting building plug load electricity consumption employing occupant-building interaction input features and bidirectional LSTM with improved swarm intelligent algorithms. Energy 288 , 129651. 10.1016/j.energy.2023.129651

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Abstract

Building energy consumption prediction is an essential foundation for energy supply-demand regulation. Among them, plug-load energy consumption in buildings accounts for approximately 12–50 % of the total energy consumption, making plug-load energy consumption prediction crucial. However, accurately predicting plug-load electricity consumption is challenging due to the influence of random human behaviors. This study presents a comprehensive plug-load electricity consumption prediction system. First, the conventional input system based on influence factors and the novel input system based on occupant behavior probability were proposed. Second, long short-term memory (LSTM) and its improvement (Bi-LSTM) are used as the fundamental algorithm. Finally, the whale algorithm (WO), a swarm intelligent algorithm, is utilized to improve the prediction accuracy. The results show that the prediction system proposed performs better with R increased by 0.70%–23.97 %, MAPE decreased by 5.33%–40.92 %, and CV-RMSE decreased by 1.10%–21.08 %, compared to the traditional prediction system. The combination of two input systems and four algorithms can accommodate different prediction accuracy requirements, data collection conditions, building functions, and time requirements.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Architecture
Additional Information:	License information from Publisher: LICENSE 1: Title: This article is under embargo with an end date yet to be finalised.
Publisher:	Elsevier
ISSN:	0360-5442
Date of First Compliant Deposit:	27 November 2023
Date of Acceptance:	11 November 2023
Last Modified:	24 Nov 2024 02:45
URI:	https://orca.cardiff.ac.uk/id/eprint/164403

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