VMD-WSLSTM Load Prediction Model Based on Shapley Values

Bilin Shao, Yichuan Yan and Huibin Zeng
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Bilin Shao: School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China
Yichuan Yan: School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China
Huibin Zeng: School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China

Energies, 2022, vol. 15, issue 2, 1-18

Abstract: Accurate short-term load forecasting can ensure the safe operation of the grid. Decomposing load data into smooth components by decomposition algorithms is a common approach to address data volatility. However, each component of the decomposition must be modeled separately for prediction, which leads to overly complex models. To solve this problem, a VMD-WSLSTM load prediction model based on Shapley values is proposed in this paper. First, the Shapley value is used to select the optimal set of special features, and then the VMD decomposition method is used to decompose the original load into several smooth components. Finally, WSLSTM is used to predict each component. Unlike the traditional LSTM model, WSLSTM can simplify the prediction model and extract common features among the components by sharing the parameters among the components. In order to verify the effectiveness of the proposed model, several control groups were used for experiments. The results show that the proposed method has higher prediction accuracy and training speed compared with traditional prediction methods.

Keywords: short-term load forecasting; long short-term memory network; nonlinear feature selection; weight sharing; electric load; Shapley value (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: 2022
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