Ultra-short-term forecasting model of power load based on fusion of power spectral density and Morlet wavelet
An accurate ultra-short-term time series prediction of a power load is an important guarantee for power dispatching and the safe operation of power systems. Problems of the current ultra-short-term time series prediction algorithms include low prediction accuracy, difficulty capturing the local muta...
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| Format: | Article |
| Language: | English |
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AIMS Press
2024-02-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2024150?viewType=HTML |
| _version_ | 1797293239385980928 |
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| author | Lihe Liang Jinying Cui Juanjuan Zhao Yan Qiang Qianqian Yang |
| author_facet | Lihe Liang Jinying Cui Juanjuan Zhao Yan Qiang Qianqian Yang |
| author_sort | Lihe Liang |
| collection | DOAJ |
| description | An accurate ultra-short-term time series prediction of a power load is an important guarantee for power dispatching and the safe operation of power systems. Problems of the current ultra-short-term time series prediction algorithms include low prediction accuracy, difficulty capturing the local mutation features, poor stability, and others. From the perspective of series decomposition, a multi-scale sequence decomposition model (TFDNet) based on power spectral density and the Morlet wavelet transform is proposed that combines the multidimensional correlation feature fusion strategy in the time and frequency domains. By introducing the time-frequency energy selection module, the "prior knowledge" guidance module, and the sequence denoising decomposition module, the model not only effectively delineates the global trend and local seasonal features, completes the in-depth information mining of the smooth trend and fluctuating seasonal features, but more importantly, realizes the accurate capture of the local mutation seasonal features. Finally, on the premise of improving the forecasting accuracy, single-point load forecasting and quantile probabilistic load forecasting for ultra-short-term load forecasting are realized. Through the experiments conducted on three public datasets and one private dataset, the TFDNet model reduces the mean square error (MSE) and mean absolute error (MAE) by 19.80 and 11.20% on average, respectively, as compared with the benchmark method. These results indicate the potential applications of the TFDNet model. |
| first_indexed | 2024-03-07T20:09:58Z |
| format | Article |
| id | doaj.art-7dccc02e2957476880cc77811f0ad5a7 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-03-07T20:09:58Z |
| publishDate | 2024-02-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-7dccc02e2957476880cc77811f0ad5a72024-02-28T01:30:14ZengAIMS PressMathematical Biosciences and Engineering1551-00182024-02-012123391342110.3934/mbe.2024150Ultra-short-term forecasting model of power load based on fusion of power spectral density and Morlet waveletLihe Liang0Jinying Cui1Juanjuan Zhao2Yan Qiang 3Qianqian Yang 41. College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030600, China1. College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030600, China2. School of Software, Taiyuan University of Technology, Taiyuan 030600, China 3. College of Information, Jinzhong College of Information, Jinzhong 030800, China1. College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030600, China3. College of Information, Jinzhong College of Information, Jinzhong 030800, ChinaAn accurate ultra-short-term time series prediction of a power load is an important guarantee for power dispatching and the safe operation of power systems. Problems of the current ultra-short-term time series prediction algorithms include low prediction accuracy, difficulty capturing the local mutation features, poor stability, and others. From the perspective of series decomposition, a multi-scale sequence decomposition model (TFDNet) based on power spectral density and the Morlet wavelet transform is proposed that combines the multidimensional correlation feature fusion strategy in the time and frequency domains. By introducing the time-frequency energy selection module, the "prior knowledge" guidance module, and the sequence denoising decomposition module, the model not only effectively delineates the global trend and local seasonal features, completes the in-depth information mining of the smooth trend and fluctuating seasonal features, but more importantly, realizes the accurate capture of the local mutation seasonal features. Finally, on the premise of improving the forecasting accuracy, single-point load forecasting and quantile probabilistic load forecasting for ultra-short-term load forecasting are realized. Through the experiments conducted on three public datasets and one private dataset, the TFDNet model reduces the mean square error (MSE) and mean absolute error (MAE) by 19.80 and 11.20% on average, respectively, as compared with the benchmark method. These results indicate the potential applications of the TFDNet model.https://www.aimspress.com/article/doi/10.3934/mbe.2024150?viewType=HTMLultra-short-term time series predictionseries decompositionglobal trend featureslocal seasonal featuresquantile probabilistic load forecasting |
| spellingShingle | Lihe Liang Jinying Cui Juanjuan Zhao Yan Qiang Qianqian Yang Ultra-short-term forecasting model of power load based on fusion of power spectral density and Morlet wavelet Mathematical Biosciences and Engineering ultra-short-term time series prediction series decomposition global trend features local seasonal features quantile probabilistic load forecasting |
| title | Ultra-short-term forecasting model of power load based on fusion of power spectral density and Morlet wavelet |
| title_full | Ultra-short-term forecasting model of power load based on fusion of power spectral density and Morlet wavelet |
| title_fullStr | Ultra-short-term forecasting model of power load based on fusion of power spectral density and Morlet wavelet |
| title_full_unstemmed | Ultra-short-term forecasting model of power load based on fusion of power spectral density and Morlet wavelet |
| title_short | Ultra-short-term forecasting model of power load based on fusion of power spectral density and Morlet wavelet |
| title_sort | ultra short term forecasting model of power load based on fusion of power spectral density and morlet wavelet |
| topic | ultra-short-term time series prediction series decomposition global trend features local seasonal features quantile probabilistic load forecasting |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2024150?viewType=HTML |
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