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

Chengyu Zhang, Liangdong Ma, Zhiwen Luo, Xing Han and Tianyi Zhao

Energy, 2024, vol. 288, issue C

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.

Keywords: Building plug load; Plug-load electricity consumption prediction; Socket-related occupant behavior; Bidirectional long short-term memory; Swarm intelligent optimization (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:288:y:2024:i:c:s0360544223030451

DOI: 10.1016/j.energy.2023.129651

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