Short-term load forecasting method considering user load component characteristics

Xiaolei Yang; Jiajia Hu; Yi Lu; Jun Cai; Jian Zhou; Yi Luo; Zugang Li

doi:10.1117/12.2646808

7 September 2022 Short-term load forecasting method considering user load component characteristics

Xiaolei Yang, Jiajia Hu, Yi Lu, Jun Cai, Jian Zhou, Yi Luo, Zugang Li

Proceedings Volume 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022); 123290U (2022) https://doi.org/10.1117/12.2646808
Event: Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 2022, Changsha, China

Abstract

To improve the prediction accuracy of the system load, this paper couples k-means clustering algorithm and neural network to predict the system load by taking into account the component characteristics of user load. Firstly, the typical curve of each 10kV distribution line is calculated. Secondly, the distribution lines are clustered into different categories through kmeans clustering according to the typical curves so that the user-side load can be effectively decomposed. Finally, the system load is predicted by using neural network with the load forecast result of different components as features. The experimental results show that the system load prediction considering the user-side load component analysis is beneficial to improve the prediction accuracy of the system load. This research has certain engineering application value.

Citation Download Citation

Xiaolei Yang, Jiajia Hu, Yi Lu, Jun Cai, Jian Zhou, Yi Luo, and Zugang Li "Short-term load forecasting method considering user load component characteristics", Proc. SPIE 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 123290U (7 September 2022); https://doi.org/10.1117/12.2646808

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