A Multi-Step Prediction Method for Wind Power Based on Improved TCN to Correct Cumulative Error
Wind power generation is likely to hinder the safe and stable operations of power systems for its irregularity, intermittency, and non-smoothness. Since wind power is continuously connected to power systems, the step length required for predicting wind power is increasingly extended, thereby causing...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-09-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2021.723319/full |
| _version_ | 1818358723224010752 |
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| author | Haifeng Luo Xun Dou Rong Sun Shengjun Wu |
| author_facet | Haifeng Luo Xun Dou Rong Sun Shengjun Wu |
| author_sort | Haifeng Luo |
| collection | DOAJ |
| description | Wind power generation is likely to hinder the safe and stable operations of power systems for its irregularity, intermittency, and non-smoothness. Since wind power is continuously connected to power systems, the step length required for predicting wind power is increasingly extended, thereby causing an increasing cumulative error. Correcting the cumulative error to predict wind power in multi-step is an urgent problem that needs to be solved. In this study, a multi-step wind power prediction method was proposed by exploiting improved TCN to correct the cumulative error. First, multi-scale convolution (MSC) and self-attentiveness (SA) were adopted to optimize the problem that a single-scale convolution kernel of TCN is difficult to extract temporal and spatial features at different scales of the input sequence. The MSC-SA-TCN model was built to recognize and extract different features exhibited by the input sequence to improve the accuracy and stability of the single-step prediction of wind power. On that basis, the multi-channel time convolutional network with multiple input and multiple output codec technologies was adopted to build the nonlinear mapping between the output and input of the TCN multi-step prediction. The method improved the problem that a single TCN is difficult to tap the different nonlinear relationships between the multi-step prediction output and the fixed input. The MMED-TCN multi-step wind power prediction model was developed to separate linearity and nonlinearity between input and output to reduce the multi-step prediction error. An experimental comparative analysis was conducted based on the measured data from two wind farms in Shuangzitai, Liaoning, and Keqi, Inner Mongolia. As revealed from the results, the MAE and RMSE of the MMED-TCN-based multi-step prediction model achieved the cumulative mean values of 0.0737 and 0.1018. The MAE and RMSE metrics outperformed those of the VMD-AMS-TCN and MSC-SA-TCN models. It can be seen that the wind power prediction method proposed in this study could improve the feature extraction ability of TCN for input sequences and the ability of mining the mapping relationship between multiple inputs and multiple outputs. The method is superior in terms of the accuracy and stability of wind power prediction. |
| first_indexed | 2024-12-13T20:33:32Z |
| format | Article |
| id | doaj.art-d7c8b9a54fee48f7a834954b384a74f5 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-12-13T20:33:32Z |
| publishDate | 2021-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-d7c8b9a54fee48f7a834954b384a74f52022-12-21T23:32:22ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2021-09-01910.3389/fenrg.2021.723319723319A Multi-Step Prediction Method for Wind Power Based on Improved TCN to Correct Cumulative ErrorHaifeng Luo0Xun Dou1Rong Sun2Shengjun Wu3Nanjing Tech University, Nanjing, ChinaNanjing Tech University, Nanjing, ChinaState Grid Jiangsu Electric Power Co. Ltd. Research Institute, Nanjing, ChinaState Grid Jiangsu Electric Power Co. Ltd. Research Institute, Nanjing, ChinaWind power generation is likely to hinder the safe and stable operations of power systems for its irregularity, intermittency, and non-smoothness. Since wind power is continuously connected to power systems, the step length required for predicting wind power is increasingly extended, thereby causing an increasing cumulative error. Correcting the cumulative error to predict wind power in multi-step is an urgent problem that needs to be solved. In this study, a multi-step wind power prediction method was proposed by exploiting improved TCN to correct the cumulative error. First, multi-scale convolution (MSC) and self-attentiveness (SA) were adopted to optimize the problem that a single-scale convolution kernel of TCN is difficult to extract temporal and spatial features at different scales of the input sequence. The MSC-SA-TCN model was built to recognize and extract different features exhibited by the input sequence to improve the accuracy and stability of the single-step prediction of wind power. On that basis, the multi-channel time convolutional network with multiple input and multiple output codec technologies was adopted to build the nonlinear mapping between the output and input of the TCN multi-step prediction. The method improved the problem that a single TCN is difficult to tap the different nonlinear relationships between the multi-step prediction output and the fixed input. The MMED-TCN multi-step wind power prediction model was developed to separate linearity and nonlinearity between input and output to reduce the multi-step prediction error. An experimental comparative analysis was conducted based on the measured data from two wind farms in Shuangzitai, Liaoning, and Keqi, Inner Mongolia. As revealed from the results, the MAE and RMSE of the MMED-TCN-based multi-step prediction model achieved the cumulative mean values of 0.0737 and 0.1018. The MAE and RMSE metrics outperformed those of the VMD-AMS-TCN and MSC-SA-TCN models. It can be seen that the wind power prediction method proposed in this study could improve the feature extraction ability of TCN for input sequences and the ability of mining the mapping relationship between multiple inputs and multiple outputs. The method is superior in terms of the accuracy and stability of wind power prediction.https://www.frontiersin.org/articles/10.3389/fenrg.2021.723319/fullimproved TCNcumulative errormulti-step wind power predictionself-attentivenessmulti-scale convolution |
| spellingShingle | Haifeng Luo Xun Dou Rong Sun Shengjun Wu A Multi-Step Prediction Method for Wind Power Based on Improved TCN to Correct Cumulative Error Frontiers in Energy Research improved TCN cumulative error multi-step wind power prediction self-attentiveness multi-scale convolution |
| title | A Multi-Step Prediction Method for Wind Power Based on Improved TCN to Correct Cumulative Error |
| title_full | A Multi-Step Prediction Method for Wind Power Based on Improved TCN to Correct Cumulative Error |
| title_fullStr | A Multi-Step Prediction Method for Wind Power Based on Improved TCN to Correct Cumulative Error |
| title_full_unstemmed | A Multi-Step Prediction Method for Wind Power Based on Improved TCN to Correct Cumulative Error |
| title_short | A Multi-Step Prediction Method for Wind Power Based on Improved TCN to Correct Cumulative Error |
| title_sort | multi step prediction method for wind power based on improved tcn to correct cumulative error |
| topic | improved TCN cumulative error multi-step wind power prediction self-attentiveness multi-scale convolution |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2021.723319/full |
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