Comparative analysis of medium and short term power load forecasting machine learning methods

Jing Wang; Jian Yao; Huayi Chen; Xiaoqiang Li; Jianbin Lin

doi:10.1117/12.2674638

23 May 2023 Comparative analysis of medium and short term power load forecasting machine learning methods

Jing Wang, Jian Yao, Huayi Chen, Xiaoqiang Li, Jianbin Lin

Proceedings Volume 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022); 126042H (2023) https://doi.org/10.1117/12.2674638
Event: 2nd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 2022, Guangzhou, China

Abstract

High precision medium and short term load forecasting is a reliable guarantee for optimizing power grid operation strategy and improving power grid operation efficiency. This paper introduces the machine learning algorithms commonly used in power load forecasting. In order to improve the prediction accuracy of these algorithms, this paper applies parameter optimization to these models. The experimental results show that the prediction accuracy of the optimized model is better. In addition, the prediction accuracy of LSTM model outperforms than XGBoost and LightGBM models greatly.

Citation Download Citation

Jing Wang, Jian Yao, Huayi Chen, Xiaoqiang Li, and Jianbin Lin "Comparative analysis of medium and short term power load forecasting machine learning methods", Proc. SPIE 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 126042H (23 May 2023); https://doi.org/10.1117/12.2674638

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