Multiload forecasting method for microgrid based on STSGCN

Shuchun Ju; Hao Meng; Bin Zhao; Yujia Chen; Bing Bai; Zhonghan Li

doi:10.1117/12.3049607

18 October 2024 Multiload forecasting method for microgrid based on STSGCN

Shuchun Ju, Hao Meng, Bin Zhao, Yujia Chen, Bing Bai, Zhonghan Li

Author Affiliations +

Proceedings Volume 13277, Sixth International Conference on Wireless Communications and Smart Grid (ICWCSG 2024); 1327714 (2024) https://doi.org/10.1117/12.3049607
Event: 2024 6th International Conference on Wireless Communications and Smart Grid, 2024, Sipsongpanna, China

Abstract

Amidst the growing diversification of user-side energy demands, accurate multi-load forecasting has emerged as a critical factor for effective planning and optimization of microgrids. As versatile distributed energy systems, microgrids necessitate adept management and dispatch of various loads - spanning electricity, heat, cooling, and energy storage. Nevertheless, these loads exhibit intricate spatiotemporal correlations and heterogeneities, posing formidable challenges for load forecasting. Current methods predominantly concentrate on time series correlations, neglecting the spatiotemporal data's heterogeneity, thereby inadequately serving practical applications. To tackle these challenges, this paper introduces a novel multi-load prediction model for microgrids, rooted in the Spatial-Temporal Synchronous Graph Convolutional Network (STSGCN). This model integrates Graph Convolutional Network (GCN) and Long Short-Term Memory (LSTM), synergistically capturing both the correlations and heterogeneities within spatiotemporal data. Simulation outcomes underscore the model's effectiveness, with the STSGCN model demonstrating a significant reduction in average absolute error - approximately 13% compared to traditional LSTM, 7% versus GCN, and 5% GCN-LSTM prediction models. This underscores the model's potential to revolutionize multi-load forecasting in microgrids.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shuchun Ju, Hao Meng, Bin Zhao, Yujia Chen, Bing Bai, and Zhonghan Li "Multiload forecasting method for microgrid based on STSGCN", Proc. SPIE 13277, Sixth International Conference on Wireless Communications and Smart Grid (ICWCSG 2024), 1327714 (18 October 2024); https://doi.org/10.1117/12.3049607

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KEYWORDS

Data modeling

Convolution

Education and training

Data storage

Performance modeling

Correlation coefficients

Energy efficiency

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