Energy Services Demand Forecasting Combined with Feature Preferences and Bidirectional Long- and Short-Term Memory Networks
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 algorith...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Shanghai Jiao Tong University
2025-07-01
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| Series: | Shanghai Jiaotong Daxue xuebao |
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| Online Access: | https://xuebao.sjtu.edu.cn/article/2025/1006-2467/1006-2467-59-7-1007.shtml |
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| _version_ | 1850061542575308800 |
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| author | KANG Feng, TAN Huochao, SU Liwei, JIAN Donglin, WANG Shuai, QIN Hao, ZHANG Yongjun |
| author_facet | KANG Feng, TAN Huochao, SU Liwei, JIAN Donglin, WANG Shuai, QIN Hao, ZHANG Yongjun |
| author_sort | KANG Feng, TAN Huochao, SU Liwei, JIAN Donglin, WANG Shuai, QIN Hao, ZHANG Yongjun |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0c5b908c6c57425db669d02686bb49b6 |
| institution | DOAJ |
| issn | 1006-2467 |
| language | zho |
| publishDate | 2025-07-01 |
| publisher | Editorial Office of Journal of Shanghai Jiao Tong University |
| record_format | Article |
| series | Shanghai Jiaotong Daxue xuebao |
| spelling | doaj-art-0c5b908c6c57425db669d02686bb49b62025-08-20T02:50:12ZzhoEditorial Office of Journal of Shanghai Jiao Tong UniversityShanghai Jiaotong Daxue xuebao1006-24672025-07-015971007101810.16183/j.cnki.jsjtu.2023.458Energy Services Demand Forecasting Combined with Feature Preferences and Bidirectional Long- and Short-Term Memory NetworksKANG Feng, TAN Huochao, SU Liwei, JIAN Donglin, WANG Shuai, QIN Hao, ZHANG Yongjun01. Customer Service Center of Guangdong Power Grid Co., Ltd., Foshan 528000, Guangdong, China;2. School of Electric Power Engineering, South China University of Technology, Guangzhou 510000, ChinaAccurate 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.https://xuebao.sjtu.edu.cn/article/2025/1006-2467/1006-2467-59-7-1007.shtmlenergy servicesdemand forecastingclass imbalanceautomatic encoderfeature optimizationmulti label classification |
| spellingShingle | 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 Shanghai Jiaotong Daxue xuebao energy services demand forecasting class imbalance automatic encoder feature optimization multi label classification |
| title | Energy Services Demand Forecasting Combined with Feature Preferences and Bidirectional Long- and Short-Term Memory Networks |
| title_full | Energy Services Demand Forecasting Combined with Feature Preferences and Bidirectional Long- and Short-Term Memory Networks |
| title_fullStr | Energy Services Demand Forecasting Combined with Feature Preferences and Bidirectional Long- and Short-Term Memory Networks |
| title_full_unstemmed | Energy Services Demand Forecasting Combined with Feature Preferences and Bidirectional Long- and Short-Term Memory Networks |
| title_short | Energy Services Demand Forecasting Combined with Feature Preferences and Bidirectional Long- and Short-Term Memory Networks |
| title_sort | energy services demand forecasting combined with feature preferences and bidirectional long and short term memory networks |
| topic | energy services demand forecasting class imbalance automatic encoder feature optimization multi label classification |
| url | https://xuebao.sjtu.edu.cn/article/2025/1006-2467/1006-2467-59-7-1007.shtml |
| work_keys_str_mv | AT kangfengtanhuochaosuliweijiandonglinwangshuaiqinhaozhangyongjun energyservicesdemandforecastingcombinedwithfeaturepreferencesandbidirectionallongandshorttermmemorynetworks |