Self‐paced learning long short‐term memory based on intelligent optimization for robust wind power prediction
Abstract Given the unpredictable and intermittent nature of wind energy, precise forecasting of wind power is crucial for ensuring the safe and stable operation of power systems. To reduce the influence of noise data on the robustness of wind power prediction, a wind power prediction method is propo...
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| Format: | Article |
| Language: | English |
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Wiley
2024-11-01
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| Series: | IET Control Theory & Applications |
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| Online Access: | https://doi.org/10.1049/cth2.12644 |
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| author | Shun Yang Xiaofei Deng Dongran Song |
| author_facet | Shun Yang Xiaofei Deng Dongran Song |
| author_sort | Shun Yang |
| collection | DOAJ |
| description | Abstract Given the unpredictable and intermittent nature of wind energy, precise forecasting of wind power is crucial for ensuring the safe and stable operation of power systems. To reduce the influence of noise data on the robustness of wind power prediction, a wind power prediction method is proposed that leverages an enhanced multi‐objective sand cat swarm algorithm (MO‐SCSO) and a self‐paced long short‐term memory network (spLSTM). First, the actual wind power data is processed into time series as input and output. Then, the progressive advantage of self‐paced learning is used to effectively solve the instability caused by noisy data during long short‐term memory network (LSTM) training. Following this, the improved MO‐SCSO is employed to iteratively optimize the hyperparameters of spLSTM. Ultimately, a combined MO‐SCSO‐spLSTM model is constructed for wind power prediction. This model is validated with the data of onshore wind farms in Austria and offshore wind farms in Denmark. The experimental results show that compared with the traditional LSTM prediction method, the proposed method has better prediction accuracy and robustness. Specifically, in the onshore and offshore wind power prediction experiments, the proposed method reduces the minimum MAE by 5.44% and 4.96%, respectively, and reduces the MAE range by 4.45% and 17.21%, respectively, which could be conducive to the safe and stable operation of power system. |
| format | Article |
| id | doaj-art-dc6f3559a8614f7bacc8dcb8a0f3b580 |
| institution | OA Journals |
| issn | 1751-8644 1751-8652 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Control Theory & Applications |
| spelling | doaj-art-dc6f3559a8614f7bacc8dcb8a0f3b5802025-08-20T02:23:08ZengWileyIET Control Theory & Applications1751-86441751-86522024-11-0118172239225510.1049/cth2.12644Self‐paced learning long short‐term memory based on intelligent optimization for robust wind power predictionShun Yang0Xiaofei Deng1Dongran Song2School of Computer Science and Engineering Central South University Changsha ChinaSchool of Information Technology and Management Hunan University of Finance and Economics Changsha ChinaSchool of Automation Central South University Changsha ChinaAbstract Given the unpredictable and intermittent nature of wind energy, precise forecasting of wind power is crucial for ensuring the safe and stable operation of power systems. To reduce the influence of noise data on the robustness of wind power prediction, a wind power prediction method is proposed that leverages an enhanced multi‐objective sand cat swarm algorithm (MO‐SCSO) and a self‐paced long short‐term memory network (spLSTM). First, the actual wind power data is processed into time series as input and output. Then, the progressive advantage of self‐paced learning is used to effectively solve the instability caused by noisy data during long short‐term memory network (LSTM) training. Following this, the improved MO‐SCSO is employed to iteratively optimize the hyperparameters of spLSTM. Ultimately, a combined MO‐SCSO‐spLSTM model is constructed for wind power prediction. This model is validated with the data of onshore wind farms in Austria and offshore wind farms in Denmark. The experimental results show that compared with the traditional LSTM prediction method, the proposed method has better prediction accuracy and robustness. Specifically, in the onshore and offshore wind power prediction experiments, the proposed method reduces the minimum MAE by 5.44% and 4.96%, respectively, and reduces the MAE range by 4.45% and 17.21%, respectively, which could be conducive to the safe and stable operation of power system.https://doi.org/10.1049/cth2.12644wind powerwind turbines |
| spellingShingle | Shun Yang Xiaofei Deng Dongran Song Self‐paced learning long short‐term memory based on intelligent optimization for robust wind power prediction IET Control Theory & Applications wind power wind turbines |
| title | Self‐paced learning long short‐term memory based on intelligent optimization for robust wind power prediction |
| title_full | Self‐paced learning long short‐term memory based on intelligent optimization for robust wind power prediction |
| title_fullStr | Self‐paced learning long short‐term memory based on intelligent optimization for robust wind power prediction |
| title_full_unstemmed | Self‐paced learning long short‐term memory based on intelligent optimization for robust wind power prediction |
| title_short | Self‐paced learning long short‐term memory based on intelligent optimization for robust wind power prediction |
| title_sort | self paced learning long short term memory based on intelligent optimization for robust wind power prediction |
| topic | wind power wind turbines |
| url | https://doi.org/10.1049/cth2.12644 |
| work_keys_str_mv | AT shunyang selfpacedlearninglongshorttermmemorybasedonintelligentoptimizationforrobustwindpowerprediction AT xiaofeideng selfpacedlearninglongshorttermmemorybasedonintelligentoptimizationforrobustwindpowerprediction AT dongransong selfpacedlearninglongshorttermmemorybasedonintelligentoptimizationforrobustwindpowerprediction |