A novel multi-task learning model based on Transformer-LSTM for wind power forecasting
The integration of multi-task learning into multi-step deterministic and probabilistic prediction frameworks plays a pivotal role in augmenting the accuracy of wind power forecasts and mitigating associated operational uncertainties. Current wind power forecasting research usually focuses on single-...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525002832 |
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| author | Rongquan Zhang Siqi Bu Yuxia Zheng Gangqiang Li Xiupeng Wan Qiangqiang Zeng Min Zhou |
| author_facet | Rongquan Zhang Siqi Bu Yuxia Zheng Gangqiang Li Xiupeng Wan Qiangqiang Zeng Min Zhou |
| author_sort | Rongquan Zhang |
| collection | DOAJ |
| description | The integration of multi-task learning into multi-step deterministic and probabilistic prediction frameworks plays a pivotal role in augmenting the accuracy of wind power forecasts and mitigating associated operational uncertainties. Current wind power forecasting research usually focuses on single-task deterministic forecasting, but there is less research on multi-task learning for multi-step deterministic and probabilistic forecasting. To achieve this, a novel multi-task learning model based on Transformer-Long short-term memory (LSTM) for multi-step deterministic and probabilistic wind power forecasting. First, a novel deep learning hybrid approach (DLH) based on the dilated causal convolutional network, Transformer, LSTM, and L2 regularization is proposed to extract multi-dimensional and complex nonlinear features of wind power time series. Then, the DLH is introduced into the task-sharing layer of the multi-task learning model for multi-step deterministic prediction. In addition, a probabilistic forecasting model that integrates the proposed multi-task learning model and quantile regression is formulated to describe the forecast uncertainty of wind power. Finally, to improve the prediction accurateness, a new heuristic algorithm, namely hybrid Cauchy mutation-based Elk herd and sand cat swarm optimization, is proposed to optimize the model hyperparameters. The proposed deterministic and probabilistic forecasting models have been applied to operational datasets from a northwest China wind farm. Compared to 23 state-of-the-art deterministic models, the proposed model reduces the mean absolute error by a minimum of 0.3174 and a maximum of 9.190, with an average reduction of 2.278. Experimental outcomes also substantiate that the proposed probabilistic model exhibits superior interval sharpness when juxtaposed against six advanced benchmarks. |
| format | Article |
| id | doaj-art-ce8df1cc4a2c4c3b83e7b32b9fcd39e5 |
| institution | DOAJ |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-ce8df1cc4a2c4c3b83e7b32b9fcd39e52025-08-20T03:21:42ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-08-0116911073210.1016/j.ijepes.2025.110732A novel multi-task learning model based on Transformer-LSTM for wind power forecastingRongquan Zhang0Siqi Bu1Yuxia Zheng2Gangqiang Li3Xiupeng Wan4Qiangqiang Zeng5Min Zhou6College of Transportation, Nanchang JiaoTong Institute, Nanchang, 330044, China; Department of Electrical and Electronic Engineering, Hong Kong Polytechnic University, 999077, Hong KongDepartment of Electrical and Electronic Engineering, Hong Kong Polytechnic University, 999077, Hong KongCollege of Transportation, Nanchang JiaoTong Institute, Nanchang, 330044, ChinaDepartment of Electrical and Electronic Engineering, Hong Kong Polytechnic University, 999077, Hong KongSino-German Colledge of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen, 518118, ChinaDepartment of Electrical and Electronic Engineering, Hong Kong Polytechnic University, 999077, Hong KongSino-German Colledge of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen, 518118, China; Corresponding author.The integration of multi-task learning into multi-step deterministic and probabilistic prediction frameworks plays a pivotal role in augmenting the accuracy of wind power forecasts and mitigating associated operational uncertainties. Current wind power forecasting research usually focuses on single-task deterministic forecasting, but there is less research on multi-task learning for multi-step deterministic and probabilistic forecasting. To achieve this, a novel multi-task learning model based on Transformer-Long short-term memory (LSTM) for multi-step deterministic and probabilistic wind power forecasting. First, a novel deep learning hybrid approach (DLH) based on the dilated causal convolutional network, Transformer, LSTM, and L2 regularization is proposed to extract multi-dimensional and complex nonlinear features of wind power time series. Then, the DLH is introduced into the task-sharing layer of the multi-task learning model for multi-step deterministic prediction. In addition, a probabilistic forecasting model that integrates the proposed multi-task learning model and quantile regression is formulated to describe the forecast uncertainty of wind power. Finally, to improve the prediction accurateness, a new heuristic algorithm, namely hybrid Cauchy mutation-based Elk herd and sand cat swarm optimization, is proposed to optimize the model hyperparameters. The proposed deterministic and probabilistic forecasting models have been applied to operational datasets from a northwest China wind farm. Compared to 23 state-of-the-art deterministic models, the proposed model reduces the mean absolute error by a minimum of 0.3174 and a maximum of 9.190, with an average reduction of 2.278. Experimental outcomes also substantiate that the proposed probabilistic model exhibits superior interval sharpness when juxtaposed against six advanced benchmarks.http://www.sciencedirect.com/science/article/pii/S0142061525002832Wind powerProbabilistic predictionMulti-task learningDilated causal convolutionalTransformer-LSTMElk herd optimizer |
| spellingShingle | Rongquan Zhang Siqi Bu Yuxia Zheng Gangqiang Li Xiupeng Wan Qiangqiang Zeng Min Zhou A novel multi-task learning model based on Transformer-LSTM for wind power forecasting International Journal of Electrical Power & Energy Systems Wind power Probabilistic prediction Multi-task learning Dilated causal convolutional Transformer-LSTM Elk herd optimizer |
| title | A novel multi-task learning model based on Transformer-LSTM for wind power forecasting |
| title_full | A novel multi-task learning model based on Transformer-LSTM for wind power forecasting |
| title_fullStr | A novel multi-task learning model based on Transformer-LSTM for wind power forecasting |
| title_full_unstemmed | A novel multi-task learning model based on Transformer-LSTM for wind power forecasting |
| title_short | A novel multi-task learning model based on Transformer-LSTM for wind power forecasting |
| title_sort | novel multi task learning model based on transformer lstm for wind power forecasting |
| topic | Wind power Probabilistic prediction Multi-task learning Dilated causal convolutional Transformer-LSTM Elk herd optimizer |
| url | http://www.sciencedirect.com/science/article/pii/S0142061525002832 |
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