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Investigating Periodic Dependencies to Improve Short-Term Load Forecasting

Jialin Yu1,*, Xiaodi Zhang2, Qi Zhong1, Jian Feng1

1 State Grid Zhejiang Jiaxing Electric Power Co., Ltd., Jiaxing, 314000, China
2 State Grid Zhejiang Electric Power Co., Ltd., Hangzhou, 310000, China

* Corresponding Author: Jialin Yu. Email: email

Energy Engineering 2024, 121(3), 789-806. https://doi.org/10.32604/ee.2023.043299

Abstract

With a further increase in energy flexibility for customers, short-term load forecasting is essential to provide benchmarks for economic dispatch and real-time alerts in power grids. The electrical load series exhibit periodic patterns and share high associations with metrological data. However, current studies have merely focused on point-wise models and failed to sufficiently investigate the periodic patterns of load series, which hinders the further improvement of short-term load forecasting accuracy. Therefore, this paper improved Autoformer to extract the periodic patterns of load series and learn a representative feature from deep decomposition and reconstruction. In addition, a novel multi-factor attention mechanism was proposed to handle multi-source metrological and numerical weather prediction data and thus correct the forecasted electrical load. The paper also compared the proposed model with various competitive models. As the experimental results reveal, the proposed model outperforms the benchmark models and maintains stability on various types of load consumers.

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APA Style
Yu, J., Zhang, X., Zhong, Q., Feng, J. (2024). Investigating periodic dependencies to improve short-term load forecasting. Energy Engineering, 121(3), 789-806. https://doi.org/10.32604/ee.2023.043299
Vancouver Style
Yu J, Zhang X, Zhong Q, Feng J. Investigating periodic dependencies to improve short-term load forecasting. Energ Eng. 2024;121(3):789-806 https://doi.org/10.32604/ee.2023.043299
IEEE Style
J. Yu, X. Zhang, Q. Zhong, and J. Feng, “Investigating Periodic Dependencies to Improve Short-Term Load Forecasting,” Energ. Eng., vol. 121, no. 3, pp. 789-806, 2024. https://doi.org/10.32604/ee.2023.043299



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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