Application of artificial intelligence technology in real-time electricity price forecasting: window-based XGBoost

Dongwei Li; Weiyang You; Xiuna Wang

doi:10.1117/12.2680574

31 May 2023 Application of artificial intelligence technology in real-time electricity price forecasting: window-based XGBoost

Dongwei Li, Weiyang You, Xiuna Wang

Author Affiliations +

Proceedings Volume 12704, Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023); 127041C (2023) https://doi.org/10.1117/12.2680574
Event: 8th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2023), 2023, Hangzhou, China

Abstract

Building a new power system is an important measure taken by China to cope with climate change problems. Establishing a flexible and perfect electricity market and price mechanism is an important means to ensure the safe and stable operation of the power system. As one of the important electricity price mechanisms, the change in real-time electricity price (RTP) is a key factor in the operation of the electricity market, and each market participant can formulate a response strategy according to the RTP. However, the uncertain, stochastic, and fluctuant characteristics are definitely difficult problems for the RTP prediction. With the aim of solving this issue, this paper proposed a RTP prediction method based on a window-based XGBoost model. Through the input conversion of the proposed model, it can help to reduce the complexity and capture the autocorrelation effect of the RTP. The case study is conducted through the actual load data of a province in China and the superiority is proved by comparing with several state-of-art models. The result shows that the window-based XGBoost model applied in this paper can decrease the prediction error by 69.48%-95.67% and greatly enhance RTP's prediction performance.

Citation Download Citation

Dongwei Li, Weiyang You, and Xiuna Wang "Application of artificial intelligence technology in real-time electricity price forecasting: window-based XGBoost", Proc. SPIE 12704, Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023), 127041C (31 May 2023); https://doi.org/10.1117/12.2680574

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KEYWORDS

Data modeling

Performance modeling

Machine learning

Deep learning

Artificial intelligence

Error analysis

Autocorrelation

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