Influencing factors of power load forecasting based on grey correlation analysis

Ke Xu; Weijun Sun; Fangya Li; Fan Yang; Jing Li; Min Gu

doi:10.1117/12.3034439

16 October 2024 Influencing factors of power load forecasting based on grey correlation analysis

Ke Xu, Weijun Sun, Fangya Li, Fan Yang, Jing Li, Min Gu

Proceedings Volume 13291, Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024); 1329132 (2024) https://doi.org/10.1117/12.3034439
Event: Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), 2024, Changchun, China

Abstract

Power load forecasting is a key problem in power system operation and planning. As an effective method, grey correlation analysis has the advantages of overcoming data fuzziness and establishing incomplete models and can help determine the main factors affecting power load and improve the accuracy and reliability of prediction. Therefore, it has important background and significance in electric load forecasting research. This study uses the grey correlation analysis method to predict and determine the main factors affecting the power load by collecting power load data and possible influencing factors data. It is found that weather conditions, seasonal factors, holidays, and policy factors are the most influential factors on power load, and they have a high correlation with power load. In particular, seasonal factors have the most significant impact on power load. The results of this study provide important references for power system operation and planning and have guiding significance for the rational arrangement of power supply and demand and formulation of dispatching strategy.

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

Citation Download Citation

Ke Xu, Weijun Sun, Fangya Li, Fan Yang, Jing Li, and Min Gu "Influencing factors of power load forecasting based on grey correlation analysis", Proc. SPIE 13291, Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), 1329132 (16 October 2024); https://doi.org/10.1117/12.3034439

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