Multifeature Short-Term Power Load Forecasting Based on GCN-LSTM
With the construction of a new-type power system under the China “double carbon” target and the increasing diversification of the energy demand on the user side, the short-term load forecasting of the power system is facing new challenges. To fully exploit the massive information contained in data,...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/2023/8846554 |
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| _version_ | 1832547833080709120 |
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| author | Houhe Chen Mingyang Zhu Xiao Hu Jiarui Wang Yong Sun Jinduo Yang Baoju Li Xiangdong Meng |
| author_facet | Houhe Chen Mingyang Zhu Xiao Hu Jiarui Wang Yong Sun Jinduo Yang Baoju Li Xiangdong Meng |
| author_sort | Houhe Chen |
| collection | DOAJ |
| description | With the construction of a new-type power system under the China “double carbon” target and the increasing diversification of the energy demand on the user side, the short-term load forecasting of the power system is facing new challenges. To fully exploit the massive information contained in data, based on the graph convolutional network (GCN) and long short-term memory network (LSTM), this paper presents a new short-term load forecasting method for power systems considering multiple factors. The Spearman rank correlation coefficient was used to analyse the correlation between load and meteorological factors, and a model including meteorology, dates, and regions was established. Secondly, GCN and LSTM are jointly used to extract the spatial and temporal characteristics of massive data, respectively, and finally achieve short-term power load prediction. Historical electrical load data from 2020 to 2022 public data of a real industrial park in southern China were selected to verify the validity of the proposed method from the aspects of forecasting accuracy, feature dimension, and training time. |
| format | Article |
| id | doaj-art-348b18c4d98c484f89fe501cbd0c0de9 |
| institution | Kabale University |
| issn | 2050-7038 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj-art-348b18c4d98c484f89fe501cbd0c0de92025-02-03T06:43:03ZengWileyInternational Transactions on Electrical Energy Systems2050-70382023-01-01202310.1155/2023/8846554Multifeature Short-Term Power Load Forecasting Based on GCN-LSTMHouhe Chen0Mingyang Zhu1Xiao Hu2Jiarui Wang3Yong Sun4Jinduo Yang5Baoju Li6Xiangdong Meng7Northeast Electric Power UniversityNortheast Electric Power UniversityNortheast Electric Power UniversityState Grid Jilin Electric Power Research InstituteState Grid Jilinsheng Electric Power Supply CompanyNortheast Electric Power UniversityState Grid Jilinsheng Electric Power Supply CompanyState Grid Jilin Electric Power Research InstituteWith the construction of a new-type power system under the China “double carbon” target and the increasing diversification of the energy demand on the user side, the short-term load forecasting of the power system is facing new challenges. To fully exploit the massive information contained in data, based on the graph convolutional network (GCN) and long short-term memory network (LSTM), this paper presents a new short-term load forecasting method for power systems considering multiple factors. The Spearman rank correlation coefficient was used to analyse the correlation between load and meteorological factors, and a model including meteorology, dates, and regions was established. Secondly, GCN and LSTM are jointly used to extract the spatial and temporal characteristics of massive data, respectively, and finally achieve short-term power load prediction. Historical electrical load data from 2020 to 2022 public data of a real industrial park in southern China were selected to verify the validity of the proposed method from the aspects of forecasting accuracy, feature dimension, and training time.http://dx.doi.org/10.1155/2023/8846554 |
| spellingShingle | Houhe Chen Mingyang Zhu Xiao Hu Jiarui Wang Yong Sun Jinduo Yang Baoju Li Xiangdong Meng Multifeature Short-Term Power Load Forecasting Based on GCN-LSTM International Transactions on Electrical Energy Systems |
| title | Multifeature Short-Term Power Load Forecasting Based on GCN-LSTM |
| title_full | Multifeature Short-Term Power Load Forecasting Based on GCN-LSTM |
| title_fullStr | Multifeature Short-Term Power Load Forecasting Based on GCN-LSTM |
| title_full_unstemmed | Multifeature Short-Term Power Load Forecasting Based on GCN-LSTM |
| title_short | Multifeature Short-Term Power Load Forecasting Based on GCN-LSTM |
| title_sort | multifeature short term power load forecasting based on gcn lstm |
| url | http://dx.doi.org/10.1155/2023/8846554 |
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