Power Load Prediction Algorithm Based on Wavelet Transform
To address the environmental impact, low efficiency, and poor accuracy of existing power load prediction methods, this study innovatively proposes a power load prediction system that combines wavelet transform with digital twin technology. Compared with similar power load prediction methods, the pro...
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| Format: | Article |
| Language: | English |
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University of Zagreb Faculty of Electrical Engineering and Computing
2024-01-01
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| Series: | Journal of Computing and Information Technology |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/471971 |
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| _version_ | 1841551898378764288 |
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| author | Xu Chen Haomiao Zhang Chao Zhang Zhiqiang Cheng Yinzhe Xu |
| author_facet | Xu Chen Haomiao Zhang Chao Zhang Zhiqiang Cheng Yinzhe Xu |
| author_sort | Xu Chen |
| collection | DOAJ |
| description | To address the environmental impact, low efficiency, and poor accuracy of existing power load prediction methods, this study innovatively proposes a power load prediction system that combines wavelet transform with digital twin technology. Compared with similar power load prediction methods, the proposed method achieved the highest power load prediction accuracy rate of 97.26%, with the lowest MAPE and RMSE being only 3.96% each. Our proposed method has good noise resistance and overcomes the disadvantage of traditional power load prediction methods that are easily affected by the environment. Moreover, the false detection rate of the load information data obtained from the power system in the Fuxin area from 2022 to 2023 was less than 5%, further verifying the reliability of the proposed method. This achievement is attributed to the powerful signal processing capabilities of the discrete wavelet transform, advanced pattern recognition and prediction capabilities of these three deep learning network algorithms, and the intelligence of digital twin technology. The combination of these three elements has brought new technological breakthroughs to the field of power load prediction. |
| format | Article |
| id | doaj-art-1d1be45f5b2c4a6287a626e716e1ef38 |
| institution | Kabale University |
| issn | 1846-3908 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | University of Zagreb Faculty of Electrical Engineering and Computing |
| record_format | Article |
| series | Journal of Computing and Information Technology |
| spelling | doaj-art-1d1be45f5b2c4a6287a626e716e1ef382025-01-09T14:17:06ZengUniversity of Zagreb Faculty of Electrical Engineering and ComputingJournal of Computing and Information Technology1846-39082024-01-0132314315810.20532/cit.2024.1005854Power Load Prediction Algorithm Based on Wavelet TransformXu Chen0Haomiao Zhang1Chao Zhang2Zhiqiang Cheng3Yinzhe Xu4State Grid Ningxia Marketing Service Center, State Grid Ningxia Metrology Center, Yinchuan, Ningxia, ChinaState Grid Ningxia Marketing Service Center, State Grid Ningxia Metrology Center, Yinchuan, Ningxia, ChinaState Grid Ningxia Marketing Service Center, State Grid Ningxia Metrology Center, Yinchuan, Ningxia, ChinaState Grid Ningxia Marketing Service Center, State Grid Ningxia Metrology Center, Yinchuan, Ningxia, ChinaState Grid Ningxia Marketing Service Center, State Grid Ningxia Metrology Center, Yinchuan, Ningxia, ChinaTo address the environmental impact, low efficiency, and poor accuracy of existing power load prediction methods, this study innovatively proposes a power load prediction system that combines wavelet transform with digital twin technology. Compared with similar power load prediction methods, the proposed method achieved the highest power load prediction accuracy rate of 97.26%, with the lowest MAPE and RMSE being only 3.96% each. Our proposed method has good noise resistance and overcomes the disadvantage of traditional power load prediction methods that are easily affected by the environment. Moreover, the false detection rate of the load information data obtained from the power system in the Fuxin area from 2022 to 2023 was less than 5%, further verifying the reliability of the proposed method. This achievement is attributed to the powerful signal processing capabilities of the discrete wavelet transform, advanced pattern recognition and prediction capabilities of these three deep learning network algorithms, and the intelligence of digital twin technology. The combination of these three elements has brought new technological breakthroughs to the field of power load prediction.https://hrcak.srce.hr/file/471971wavelet transformensemble learningdeep learning networkspowerload prediction |
| spellingShingle | Xu Chen Haomiao Zhang Chao Zhang Zhiqiang Cheng Yinzhe Xu Power Load Prediction Algorithm Based on Wavelet Transform Journal of Computing and Information Technology wavelet transform ensemble learning deep learning networks power load prediction |
| title | Power Load Prediction Algorithm Based on Wavelet Transform |
| title_full | Power Load Prediction Algorithm Based on Wavelet Transform |
| title_fullStr | Power Load Prediction Algorithm Based on Wavelet Transform |
| title_full_unstemmed | Power Load Prediction Algorithm Based on Wavelet Transform |
| title_short | Power Load Prediction Algorithm Based on Wavelet Transform |
| title_sort | power load prediction algorithm based on wavelet transform |
| topic | wavelet transform ensemble learning deep learning networks power load prediction |
| url | https://hrcak.srce.hr/file/471971 |
| work_keys_str_mv | AT xuchen powerloadpredictionalgorithmbasedonwavelettransform AT haomiaozhang powerloadpredictionalgorithmbasedonwavelettransform AT chaozhang powerloadpredictionalgorithmbasedonwavelettransform AT zhiqiangcheng powerloadpredictionalgorithmbasedonwavelettransform AT yinzhexu powerloadpredictionalgorithmbasedonwavelettransform |