Day-Ahead Electricity Price Probabilistic Forecasting Based on SHAP Feature Selection and LSTNet Quantile Regression

Huixin Liu, Xiaodong Shen (), Xisheng Tang and Junyong Liu
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Huixin Liu: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Xiaodong Shen: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Xisheng Tang: Institute of Electrical Engineer, University of Chinese Academy of Sciences, Beijing 100190, China
Junyong Liu: College of Electrical Engineering, Sichuan University, Chengdu 610065, China

Energies, 2023, vol. 16, issue 13, 1-17

Abstract: Electricity prices are a central element of the electricity market, and accurate electricity price forecasting is critical for market participants. However, in the context of increasingly integrated economic markets, the complexity of the electricity system has increased. As a result, the number of factors required to consider in electricity price forecasting is growing. In addition, the high percentage of renewable energy penetration has increased the volatility of electricity generation, making it more challenging to predict prices accurately. In this paper, we propose a probabilistic forecasting method based on SHAP (SHapley Additive exPlanation) feature selection and LSTNet (long- and short-term time-series network) quantile regression. First, to reduce feature redundancy and overfitting, we use the SHAP method to perform feature selection in a high-dimensional input feature set, and specifically analyze the magnitude and manner in which features affect electricity prices. Second, we apply the LSTNet quantile regression model to predict the electricity value under different quantiles. Finally, the probability density function and the prediction interval of the predicted electricity prices are obtained by kernel density estimation. The case of the Danish electricity market validates the effectiveness and accuracy of our proposed method. The accuracy of the proposed method is better than that of other methods, and we assess the importance and direction of the impact of features on electricity prices.

Keywords: probabilistic forecasting; SHAP; feature selection; LSTNet; quantile regression (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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