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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| Vydané v: | Energies (Basel) Ročník 18; číslo 1; s. 106 |
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| Hlavní autori: | , , , , , , |
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| Jazyk: | English |
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01.01.2025
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| ISSN: | 1996-1073, 1996-1073 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Audience | Academic |
| Author | Xing, Rongxin Gao, Junwei Liu, Huabo Fan, Zengliang Cui, Jiqiang Hua, Qingbo Mu, Wei |
| Author_xml | – sequence: 1 givenname: Qingbo surname: Hua fullname: Hua, Qingbo – sequence: 2 givenname: Zengliang surname: Fan fullname: Fan, Zengliang – sequence: 3 givenname: Wei surname: Mu fullname: Mu, Wei – sequence: 4 givenname: Jiqiang surname: Cui fullname: Cui, Jiqiang – sequence: 5 givenname: Rongxin surname: Xing fullname: Xing, Rongxin – sequence: 6 givenname: Huabo orcidid: 0000-0002-4182-8934 surname: Liu fullname: Liu, Huabo – sequence: 7 givenname: Junwei surname: Gao fullname: Gao, Junwei |
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| Cites_doi | 10.1016/j.apenergy.2022.118801 10.1109/TSG.2018.2805723 10.1016/j.compchemeng.2021.107513 10.1016/j.enconman.2020.112766 10.3390/en16104060 10.1016/j.heliyon.2023.e20468 10.3390/en11082163 10.1049/gtd2.12394 10.1016/j.apenergy.2024.123319 10.1016/j.comcom.2022.11.018 10.1109/TNNLS.2023.3262541 10.1016/j.apenergy.2017.12.051 10.2174/2666782701666210614223415 10.1080/15567036.2022.2053250 10.1016/j.resourpol.2022.102906 10.1016/j.dsm.2022.08.001 10.1088/1742-6596/2592/1/012067 10.1016/j.energy.2021.120480 10.1016/j.energy.2023.129753 10.1109/ICMIAM54662.2021.9715210 10.35833/MPCE.2020.000647 10.1109/EIConRus.2017.7910859 10.1016/j.energy.2021.120162 10.1038/s41598-024-73076-6 10.1016/j.egyr.2023.05.090 10.1109/TPWRS.2019.2924294 10.1016/j.neucom.2021.02.046 10.3390/en14237952 10.1109/ACCESS.2022.3190892 10.1109/TIM.2024.3381699 |
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| SubjectTerms | Accuracy Algorithms attention mechanism convolutional neural network Deep learning Energy consumption Environmental protection Forecasting Machine learning Neural networks power load forecasting Power supply Prediction theory Support vector machines Time series |
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| Title | A Short-Term Power Load Forecasting Method Using CNN-GRU with an Attention Mechanism |
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