Application of Improved Deep Learning Method in Intelligent Power System
In view of the inaccurate short-term power load prediction in the power system, where the smart grid cannot effectively coordinate the production, transportation, and distribution of electric energy, the authors propose the application of improved deep learning methods in intelligent power systems....
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/2022/6788668 |
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| _version_ | 1832567707230273536 |
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| author | HuiJie Liu Yang Liu ChengWen Xu |
| author_facet | HuiJie Liu Yang Liu ChengWen Xu |
| author_sort | HuiJie Liu |
| collection | DOAJ |
| description | In view of the inaccurate short-term power load prediction in the power system, where the smart grid cannot effectively coordinate the production, transportation, and distribution of electric energy, the authors propose the application of improved deep learning methods in intelligent power systems. The method uses the convolutional neural network to establish the energy prediction calculation model, uses CNN adaptive data features to mine characteristics, quantifies power uncertainty, uses drop regularization to optimize the deep network structure, uses the deep forest to learn the extracted data features, and builds a prediction model, in order to achieve accurate prediction of power load and solve the problem that the accuracy of existing forecasting methods decreases due to random fluctuations of power. The results showed the following: in the power load forecast results over the weekend, the random forest and the LSTM algorithm forecast results were relatively close and the RMSEs were 17.3 and 17.1, respectively, while the SVM predicted a larger RMSE error of 27.5. The authors’ method predicts the best with 14.8. Conclusion. After verification based on actual load data, in the case of uncertain fluctuations in power load, this method can accurately predict the power load, and the accuracy is higher than that of the more popular methods at present, and it is expected to become an important technical support for solving the core problems of smart grid. |
| format | Article |
| id | doaj-art-1655c6d8d9f44056bc9b54d3a0d4b69a |
| institution | Kabale University |
| issn | 2050-7038 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj-art-1655c6d8d9f44056bc9b54d3a0d4b69a2025-02-03T01:00:43ZengWileyInternational Transactions on Electrical Energy Systems2050-70382022-01-01202210.1155/2022/6788668Application of Improved Deep Learning Method in Intelligent Power SystemHuiJie Liu0Yang Liu1ChengWen Xu2ShiJiaZhuang Institute of Railway TechnologyShiJiaZhuang Institute of Railway TechnologyShiJiaZhuang Institute of Railway TechnologyIn view of the inaccurate short-term power load prediction in the power system, where the smart grid cannot effectively coordinate the production, transportation, and distribution of electric energy, the authors propose the application of improved deep learning methods in intelligent power systems. The method uses the convolutional neural network to establish the energy prediction calculation model, uses CNN adaptive data features to mine characteristics, quantifies power uncertainty, uses drop regularization to optimize the deep network structure, uses the deep forest to learn the extracted data features, and builds a prediction model, in order to achieve accurate prediction of power load and solve the problem that the accuracy of existing forecasting methods decreases due to random fluctuations of power. The results showed the following: in the power load forecast results over the weekend, the random forest and the LSTM algorithm forecast results were relatively close and the RMSEs were 17.3 and 17.1, respectively, while the SVM predicted a larger RMSE error of 27.5. The authors’ method predicts the best with 14.8. Conclusion. After verification based on actual load data, in the case of uncertain fluctuations in power load, this method can accurately predict the power load, and the accuracy is higher than that of the more popular methods at present, and it is expected to become an important technical support for solving the core problems of smart grid.http://dx.doi.org/10.1155/2022/6788668 |
| spellingShingle | HuiJie Liu Yang Liu ChengWen Xu Application of Improved Deep Learning Method in Intelligent Power System International Transactions on Electrical Energy Systems |
| title | Application of Improved Deep Learning Method in Intelligent Power System |
| title_full | Application of Improved Deep Learning Method in Intelligent Power System |
| title_fullStr | Application of Improved Deep Learning Method in Intelligent Power System |
| title_full_unstemmed | Application of Improved Deep Learning Method in Intelligent Power System |
| title_short | Application of Improved Deep Learning Method in Intelligent Power System |
| title_sort | application of improved deep learning method in intelligent power system |
| url | http://dx.doi.org/10.1155/2022/6788668 |
| work_keys_str_mv | AT huijieliu applicationofimproveddeeplearningmethodinintelligentpowersystem AT yangliu applicationofimproveddeeplearningmethodinintelligentpowersystem AT chengwenxu applicationofimproveddeeplearningmethodinintelligentpowersystem |