Synergistic Artificial Intelligence framework for robust multivariate medium-term wind power prediction with uncertainty envelopes
This paper proposes an innovative framework for medium-term wind power forecasting, employing a robust, multi-module Artificial Intelligence approach to improve prediction accuracy and reliability over extended horizons. The framework consists of three key components: an internal–external learning p...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Energy and AI |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266654682500045X |
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| author | Bo Wu Xiuli Wang Bangyan Wang Yaohong Xie Shixiong Qi Wenduo Sun Qihang Huang Xiang Ma |
| author_facet | Bo Wu Xiuli Wang Bangyan Wang Yaohong Xie Shixiong Qi Wenduo Sun Qihang Huang Xiang Ma |
| author_sort | Bo Wu |
| collection | DOAJ |
| description | This paper proposes an innovative framework for medium-term wind power forecasting, employing a robust, multi-module Artificial Intelligence approach to improve prediction accuracy and reliability over extended horizons. The framework consists of three key components: an internal–external learning process, a vertical–horizontal learning process, and a residual-based robust forecasting method. The internal–external process combines Variational Mode Decomposition with a stacked N-BEATS model, achieving stable and accurate forecasts across nearly 200 time steps. The vertical–horizontal process integrates the Polar Lights Optimizer with Joint Opposite Selection and a regression model based on the bidirectional long short-term memory and the gated recurrent unit, enabling efficient hyperparameter optimization and yielding a determination coefficient above 0.9996 for training data and a normalized root mean square error of 0.2448 for test data. We compared our proposed method with nine classical and state-of-the-art techniques and found that it delivers higher accuracy in medium-term prediction, extending to nearly 200 steps. The residual-based method addresses uncertainties by generating 95% confidence intervals, enhancing the model’s robustness in practical applications. By simulating real-world conditions, this framework provides reliable medium-term forecasts, making it an effective tool for renewable energy system dispatch and precise error control. |
| format | Article |
| id | doaj-art-a2a8aecdd92e4eff815f19adfe5d833a |
| institution | OA Journals |
| issn | 2666-5468 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj-art-a2a8aecdd92e4eff815f19adfe5d833a2025-08-20T02:18:47ZengElsevierEnergy and AI2666-54682025-05-012010051310.1016/j.egyai.2025.100513Synergistic Artificial Intelligence framework for robust multivariate medium-term wind power prediction with uncertainty envelopesBo Wu0Xiuli Wang1Bangyan Wang2Yaohong Xie3Shixiong Qi4Wenduo Sun5Qihang Huang6Xiang Ma7School of Electrical Engineering, Xi’an Jiaotong University, Xi’an, 710049, Shaanxi, China; Corresponding author.School of Electrical Engineering, Xi’an Jiaotong University, Xi’an, 710049, Shaanxi, ChinaSchool of Electrical Engineering, Xi’an Jiaotong University, Xi’an, 710049, Shaanxi, ChinaSchool of Electrical Engineering, Xi’an Jiaotong University, Xi’an, 710049, Shaanxi, ChinaNational Electric Power Dispatching and Control Center, State Grid Corporation of China, 100000, Beijing, ChinaState Grid Zhejiang Electric Power Dispatching and Control Center, State Grid Zhejiang Electric Power Co., Ltd., Hangzhou, 310000, Zhejiang, ChinaState Grid Zhejiang Electric Power Dispatching and Control Center, State Grid Zhejiang Electric Power Co., Ltd., Hangzhou, 310000, Zhejiang, ChinaJinhua Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd., Jinhua, 321000, Zhejiang, ChinaThis paper proposes an innovative framework for medium-term wind power forecasting, employing a robust, multi-module Artificial Intelligence approach to improve prediction accuracy and reliability over extended horizons. The framework consists of three key components: an internal–external learning process, a vertical–horizontal learning process, and a residual-based robust forecasting method. The internal–external process combines Variational Mode Decomposition with a stacked N-BEATS model, achieving stable and accurate forecasts across nearly 200 time steps. The vertical–horizontal process integrates the Polar Lights Optimizer with Joint Opposite Selection and a regression model based on the bidirectional long short-term memory and the gated recurrent unit, enabling efficient hyperparameter optimization and yielding a determination coefficient above 0.9996 for training data and a normalized root mean square error of 0.2448 for test data. We compared our proposed method with nine classical and state-of-the-art techniques and found that it delivers higher accuracy in medium-term prediction, extending to nearly 200 steps. The residual-based method addresses uncertainties by generating 95% confidence intervals, enhancing the model’s robustness in practical applications. By simulating real-world conditions, this framework provides reliable medium-term forecasts, making it an effective tool for renewable energy system dispatch and precise error control.http://www.sciencedirect.com/science/article/pii/S266654682500045XMultivariate wind power predictionResidual-based robust predictionRenewable energy optimizationHyperparameter optimizationRegression with hybrid Artificial Intelligence |
| spellingShingle | Bo Wu Xiuli Wang Bangyan Wang Yaohong Xie Shixiong Qi Wenduo Sun Qihang Huang Xiang Ma Synergistic Artificial Intelligence framework for robust multivariate medium-term wind power prediction with uncertainty envelopes Energy and AI Multivariate wind power prediction Residual-based robust prediction Renewable energy optimization Hyperparameter optimization Regression with hybrid Artificial Intelligence |
| title | Synergistic Artificial Intelligence framework for robust multivariate medium-term wind power prediction with uncertainty envelopes |
| title_full | Synergistic Artificial Intelligence framework for robust multivariate medium-term wind power prediction with uncertainty envelopes |
| title_fullStr | Synergistic Artificial Intelligence framework for robust multivariate medium-term wind power prediction with uncertainty envelopes |
| title_full_unstemmed | Synergistic Artificial Intelligence framework for robust multivariate medium-term wind power prediction with uncertainty envelopes |
| title_short | Synergistic Artificial Intelligence framework for robust multivariate medium-term wind power prediction with uncertainty envelopes |
| title_sort | synergistic artificial intelligence framework for robust multivariate medium term wind power prediction with uncertainty envelopes |
| topic | Multivariate wind power prediction Residual-based robust prediction Renewable energy optimization Hyperparameter optimization Regression with hybrid Artificial Intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S266654682500045X |
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