A novel medium- and long-term load forecasting method based on sensitivity analysis of multi meteorological indicators

Beibei Sun; Yiming Xue; Jie Wang; Ru Zhang; Yiping Rong; Miner Tan

doi:10.1117/12.2680011

31 May 2023 A novel medium- and long-term load forecasting method based on sensitivity analysis of multi meteorological indicators

Beibei Sun, Yiming Xue, Jie Wang, Ru Zhang, Yiping Rong, Miner Tan

Author Affiliations +

Proceedings Volume 12704, Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023); 127041D (2023) https://doi.org/10.1117/12.2680011
Event: 8th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2023), 2023, Hangzhou, China

Abstract

Traditional power load or electricity forecasting methods have used only limited meteorological information. Specifically, these methods either only select one of maximal, minimal and average temperature as the influencing indicator, or only capture the very basic meteorological information, instead of exploring the nonlinear relationship between temperature and loads in depth. To overcome the shortcomings of the traditional methods, this paper proposes a new medium- and long-term load forecasting method based on sensitivity analysis of multi meteorological indicators. This method constructs the encoder and decoder between weather features and loads. Moreover, this paper combines this method with XGBoost and SVR, which can improve the accuracy of medium- and long-term forecasting algorithm in the power grid field effectively. The simulation results of this synthesized model show that average daily prediction accuracy rate of the year is 95.37% and the average monthly prediction accuracy rate of the year is 98.02%.

Citation Download Citation

Beibei Sun, Yiming Xue, Jie Wang, Ru Zhang, Yiping Rong, and Miner Tan "A novel medium- and long-term load forecasting method based on sensitivity analysis of multi meteorological indicators", Proc. SPIE 12704, Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023), 127041D (31 May 2023); https://doi.org/10.1117/12.2680011

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KEYWORDS

Meteorology

Temperature metrology

Decision trees

Linear regression

Machine learning

Power grids

Artificial neural networks

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