Load recognition method based on convolutional neural network and attention mechanism
Non-intrusive load monitoring (NILM) of residential houses is an important research content of the user demand side of smart grids, and the energy consumption analysis and power consumption management of residential loads are key steps in achieving energy conservation, emission reduction, and sustai...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Electric Power Engineering Technology
2025-01-01
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| Series: | 电力工程技术 |
| Subjects: | |
| Online Access: | https://www.epet-info.com/dlgcjsen/article/abstract/231229592 |
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| _version_ | 1823865265010507776 |
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| author | ZHAO Yitao LI Zhao LIU Xinglong LUO Zhao WANG Gang SHEN Xin |
| author_facet | ZHAO Yitao LI Zhao LIU Xinglong LUO Zhao WANG Gang SHEN Xin |
| author_sort | ZHAO Yitao |
| collection | DOAJ |
| description | Non-intrusive load monitoring (NILM) of residential houses is an important research content of the user demand side of smart grids, and the energy consumption analysis and power consumption management of residential loads are key steps in achieving energy conservation, emission reduction, and sustainable development. Aiming at the problems of poor recognition performance of traditional algorithms and difficulty in adapting to the current complex electricity environment, a NILM load recognition method integrating convolutional neural network (CNN)-self-attention mechanism is proposed from the optimization idea of enhancing the feature extraction performance of classification algorithms. Firstly, the power data of eight different household appliances are collected to establish a U-I trajectory curve database. Secondly, the feature aggregation ability of CNN is improved by using squeeze-and-excitation network (SENet) attention mechanism to complete the feature extraction and load identification of U-I trajectory curves of different electrical appliances. Finally, the private dataset and PLAID dataset are tested, and the example results show that the proposed method has high recognition accuracy and good generalization performance in different operational scenarios. |
| format | Article |
| id | doaj-art-8ec101035e0f4cbcbf24e17d4fbf61bd |
| institution | Kabale University |
| issn | 2096-3203 |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | Editorial Department of Electric Power Engineering Technology |
| record_format | Article |
| series | 电力工程技术 |
| spelling | doaj-art-8ec101035e0f4cbcbf24e17d4fbf61bd2025-02-08T08:40:18ZzhoEditorial Department of Electric Power Engineering Technology电力工程技术2096-32032025-01-0144122723510.12158/j.2096-3203.2025.01.023231229592Load recognition method based on convolutional neural network and attention mechanismZHAO Yitao0LI Zhao1LIU Xinglong2LUO Zhao3WANG Gang4SHEN Xin5Yunnan Power Grid Co., Ltd., Kunming 650217, ChinaFaculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, ChinaYunnan Power Grid Co., Ltd., Kunming 650217, ChinaFaculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, ChinaYunnan Power Grid Co., Ltd., Kunming 650217, ChinaNon-intrusive load monitoring (NILM) of residential houses is an important research content of the user demand side of smart grids, and the energy consumption analysis and power consumption management of residential loads are key steps in achieving energy conservation, emission reduction, and sustainable development. Aiming at the problems of poor recognition performance of traditional algorithms and difficulty in adapting to the current complex electricity environment, a NILM load recognition method integrating convolutional neural network (CNN)-self-attention mechanism is proposed from the optimization idea of enhancing the feature extraction performance of classification algorithms. Firstly, the power data of eight different household appliances are collected to establish a U-I trajectory curve database. Secondly, the feature aggregation ability of CNN is improved by using squeeze-and-excitation network (SENet) attention mechanism to complete the feature extraction and load identification of U-I trajectory curves of different electrical appliances. Finally, the private dataset and PLAID dataset are tested, and the example results show that the proposed method has high recognition accuracy and good generalization performance in different operational scenarios.https://www.epet-info.com/dlgcjsen/article/abstract/231229592non-intrusive load monitoring (nilm)load identificationconvolutional neural network (cnn)squeeze-and-excitation network (senet)attention mechanismfeature extractionu-i trajectory |
| spellingShingle | ZHAO Yitao LI Zhao LIU Xinglong LUO Zhao WANG Gang SHEN Xin Load recognition method based on convolutional neural network and attention mechanism 电力工程技术 non-intrusive load monitoring (nilm) load identification convolutional neural network (cnn) squeeze-and-excitation network (senet) attention mechanism feature extraction u-i trajectory |
| title | Load recognition method based on convolutional neural network and attention mechanism |
| title_full | Load recognition method based on convolutional neural network and attention mechanism |
| title_fullStr | Load recognition method based on convolutional neural network and attention mechanism |
| title_full_unstemmed | Load recognition method based on convolutional neural network and attention mechanism |
| title_short | Load recognition method based on convolutional neural network and attention mechanism |
| title_sort | load recognition method based on convolutional neural network and attention mechanism |
| topic | non-intrusive load monitoring (nilm) load identification convolutional neural network (cnn) squeeze-and-excitation network (senet) attention mechanism feature extraction u-i trajectory |
| url | https://www.epet-info.com/dlgcjsen/article/abstract/231229592 |
| work_keys_str_mv | AT zhaoyitao loadrecognitionmethodbasedonconvolutionalneuralnetworkandattentionmechanism AT lizhao loadrecognitionmethodbasedonconvolutionalneuralnetworkandattentionmechanism AT liuxinglong loadrecognitionmethodbasedonconvolutionalneuralnetworkandattentionmechanism AT luozhao loadrecognitionmethodbasedonconvolutionalneuralnetworkandattentionmechanism AT wanggang loadrecognitionmethodbasedonconvolutionalneuralnetworkandattentionmechanism AT shenxin loadrecognitionmethodbasedonconvolutionalneuralnetworkandattentionmechanism |