A Short-Term Power Load Forecasting Method Using CNN-GRU with an Attention Mechanism
This paper proposes a short-term electric load forecasting method combining convolutional neural networks and gated recurrent units with an attention mechanism. By integrating CNNs and GRUs, the method can fully leverage the strengths of CNNs in feature extraction and the advantages of GRUs in seque...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/1/106 |
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| _version_ | 1841549297473028096 |
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| author | Qingbo Hua Zengliang Fan Wei Mu Jiqiang Cui Rongxin Xing Huabo Liu Junwei Gao |
| author_facet | Qingbo Hua Zengliang Fan Wei Mu Jiqiang Cui Rongxin Xing Huabo Liu Junwei Gao |
| author_sort | Qingbo Hua |
| collection | DOAJ |
| description | This paper proposes a short-term electric load forecasting method combining convolutional neural networks and gated recurrent units with an attention mechanism. By integrating CNNs and GRUs, the method can fully leverage the strengths of CNNs in feature extraction and the advantages of GRUs in sequence modeling, enabling the model to comprehend signal data more comprehensively and effectively extract features from sequential data. The introduction of the attention mechanism allows the traditional model to dynamically focus on important parts of the input data while ignoring the unimportant parts. This capability enables the model to utilize input information more efficiently, thereby enhancing model performance. This paper applies the proposed model to a dataset comprising regional electric load and meteorological data for experimentation. The results show that compared with other common models, the proposed model effectively reduces the mean absolute error and root mean square error (121.51 and 263.43, respectively) and accurately predicts the short-term change in regional power load. |
| format | Article |
| id | doaj-art-c41535e5917f4c68b9404085f7525d4c |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-c41535e5917f4c68b9404085f7525d4c2025-01-10T13:17:07ZengMDPI AGEnergies1996-10732024-12-0118110610.3390/en18010106A Short-Term Power Load Forecasting Method Using CNN-GRU with an Attention MechanismQingbo Hua0Zengliang Fan1Wei Mu2Jiqiang Cui3Rongxin Xing4Huabo Liu5Junwei Gao6Qingdao Elink Information Technology Co., Ltd., Qingdao 266033, ChinaQingdao Elink Information Technology Co., Ltd., Qingdao 266033, ChinaQingdao Elink Information Technology Co., Ltd., Qingdao 266033, ChinaSchool of Automation, Qingdao University, Qingdao 266100, ChinaSchool of Automation, Qingdao University, Qingdao 266100, ChinaSchool of Automation, Qingdao University, Qingdao 266100, ChinaSchool of Automation, Qingdao University, Qingdao 266100, ChinaThis paper proposes a short-term electric load forecasting method combining convolutional neural networks and gated recurrent units with an attention mechanism. By integrating CNNs and GRUs, the method can fully leverage the strengths of CNNs in feature extraction and the advantages of GRUs in sequence modeling, enabling the model to comprehend signal data more comprehensively and effectively extract features from sequential data. The introduction of the attention mechanism allows the traditional model to dynamically focus on important parts of the input data while ignoring the unimportant parts. This capability enables the model to utilize input information more efficiently, thereby enhancing model performance. This paper applies the proposed model to a dataset comprising regional electric load and meteorological data for experimentation. The results show that compared with other common models, the proposed model effectively reduces the mean absolute error and root mean square error (121.51 and 263.43, respectively) and accurately predicts the short-term change in regional power load.https://www.mdpi.com/1996-1073/18/1/106attention mechanismconvolutional neural networkpower load forecasting |
| spellingShingle | Qingbo Hua Zengliang Fan Wei Mu Jiqiang Cui Rongxin Xing Huabo Liu Junwei Gao A Short-Term Power Load Forecasting Method Using CNN-GRU with an Attention Mechanism Energies attention mechanism convolutional neural network power load forecasting |
| title | A Short-Term Power Load Forecasting Method Using CNN-GRU with an Attention Mechanism |
| title_full | A Short-Term Power Load Forecasting Method Using CNN-GRU with an Attention Mechanism |
| title_fullStr | A Short-Term Power Load Forecasting Method Using CNN-GRU with an Attention Mechanism |
| title_full_unstemmed | A Short-Term Power Load Forecasting Method Using CNN-GRU with an Attention Mechanism |
| title_short | A Short-Term Power Load Forecasting Method Using CNN-GRU with an Attention Mechanism |
| title_sort | short term power load forecasting method using cnn gru with an attention mechanism |
| topic | attention mechanism convolutional neural network power load forecasting |
| url | https://www.mdpi.com/1996-1073/18/1/106 |
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