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Energy Services Demand Forecasting Combined with Feature Preferences and Bidirectional Long- and Short-Term Memory Networks
Received date: 2023-09-11
Revised date: 2023-10-31
Accepted date: 2023-11-08
Online published: 2023-11-17
Accurate and efficient demand forecasting of customer energy services is crucial for quality and risk management in grid customer service. Therefore, this paper proposes a user energy service demand prediction model based on feature selection. The methodology includes introducing a sampling algorithm to solve the class imbalance problem in the data on the basis of analysing the user energy service data, reducing the dimensionality of the data based on an autoencoder to ensure efficient clustering of the K-mean algorithm, constructing a feature selection algorithm based on a lightweight gradient lifting machine to filter the effective features and improve the training efficiency of the prediction model, and establishing a bidirectional long- and short-term memory neural network multi-label predicting model based on an attentional mechanism to refine the user’s energy service demand. Through the analysis of 720 000 work order data from Guangdong Power Grid over three years, showing that the model proposed can effectively improve the prediction accuracy and speed.
KANG Feng , TAN Huochao , SU Liwei , JIAN Donglin , WANG Shuai , QIN Hao , ZHANG Yongjun . Energy Services Demand Forecasting Combined with Feature Preferences and Bidirectional Long- and Short-Term Memory Networks[J]. Journal of Shanghai Jiaotong University, 2025 , 59(7) : 1007 -1018 . DOI: 10.16183/j.cnki.jsjtu.2023.458
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