Wind Power Short-Term Prediction Method Based on Time-Domain Dual-Channel Adaptive Learning Model
Driven by dual carbon targets, the scale of wind power integration has surged dramatically. However, its strong volatility causes insufficient short-term prediction accuracy, severely constraining grid security and economic dispatch. To address three key challenges in extracting temporal characteris...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/14/3722 |
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| author | Haotian Guo Keng-Weng Lao Junkun Hao Xiaorui Hu |
| author_facet | Haotian Guo Keng-Weng Lao Junkun Hao Xiaorui Hu |
| author_sort | Haotian Guo |
| collection | DOAJ |
| description | Driven by dual carbon targets, the scale of wind power integration has surged dramatically. However, its strong volatility causes insufficient short-term prediction accuracy, severely constraining grid security and economic dispatch. To address three key challenges in extracting temporal characteristics of strong volatility, adaptive fusion of multi-source features, and enhancing model interpretability, this paper proposes a Time-Domain Dual-Channel Adaptive Learning Model (TDDCALM). The model employs dual-channel feature decoupling: one Transformer encoder layer captures global dependencies while the raw state layer preserves local temporal features. After TCN-based feature compression, an adaptive weighted early fusion mechanism dynamically optimizes channel weights. The ACON adaptive activation function autonomously learns optimal activation patterns, with fused features visualized through visualization techniques. Validation on two wind farm datasets (A/B) demonstrates that the proposed method reduces RMSE by at least 8.89% compared to the best deep learning baseline, exhibits low sensitivity to time window sizes, and establishes a novel paradigm for forecasting highly volatile renewable energy power. |
| format | Article |
| id | doaj-art-1f91dfbb5d624f048001d9111a661ecb |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-1f91dfbb5d624f048001d9111a661ecb2025-08-20T03:08:12ZengMDPI AGEnergies1996-10732025-07-011814372210.3390/en18143722Wind Power Short-Term Prediction Method Based on Time-Domain Dual-Channel Adaptive Learning ModelHaotian Guo0Keng-Weng Lao1Junkun Hao2Xiaorui Hu3State Key Laboratory of Internet of Things for Smart City, University of Macau, Macao 999078, ChinaState Key Laboratory of Internet of Things for Smart City, University of Macau, Macao 999078, ChinaSchool of Electrical Engineering, Dalian University of Technology, Dalian 116024, ChinaState Key Laboratory of Internet of Things for Smart City, University of Macau, Macao 999078, ChinaDriven by dual carbon targets, the scale of wind power integration has surged dramatically. However, its strong volatility causes insufficient short-term prediction accuracy, severely constraining grid security and economic dispatch. To address three key challenges in extracting temporal characteristics of strong volatility, adaptive fusion of multi-source features, and enhancing model interpretability, this paper proposes a Time-Domain Dual-Channel Adaptive Learning Model (TDDCALM). The model employs dual-channel feature decoupling: one Transformer encoder layer captures global dependencies while the raw state layer preserves local temporal features. After TCN-based feature compression, an adaptive weighted early fusion mechanism dynamically optimizes channel weights. The ACON adaptive activation function autonomously learns optimal activation patterns, with fused features visualized through visualization techniques. Validation on two wind farm datasets (A/B) demonstrates that the proposed method reduces RMSE by at least 8.89% compared to the best deep learning baseline, exhibits low sensitivity to time window sizes, and establishes a novel paradigm for forecasting highly volatile renewable energy power.https://www.mdpi.com/1996-1073/18/14/3722short-term forecast of wind powertime-domain dual-channel adaptive learning modelACONadaptive weighted early fusion |
| spellingShingle | Haotian Guo Keng-Weng Lao Junkun Hao Xiaorui Hu Wind Power Short-Term Prediction Method Based on Time-Domain Dual-Channel Adaptive Learning Model Energies short-term forecast of wind power time-domain dual-channel adaptive learning model ACON adaptive weighted early fusion |
| title | Wind Power Short-Term Prediction Method Based on Time-Domain Dual-Channel Adaptive Learning Model |
| title_full | Wind Power Short-Term Prediction Method Based on Time-Domain Dual-Channel Adaptive Learning Model |
| title_fullStr | Wind Power Short-Term Prediction Method Based on Time-Domain Dual-Channel Adaptive Learning Model |
| title_full_unstemmed | Wind Power Short-Term Prediction Method Based on Time-Domain Dual-Channel Adaptive Learning Model |
| title_short | Wind Power Short-Term Prediction Method Based on Time-Domain Dual-Channel Adaptive Learning Model |
| title_sort | wind power short term prediction method based on time domain dual channel adaptive learning model |
| topic | short-term forecast of wind power time-domain dual-channel adaptive learning model ACON adaptive weighted early fusion |
| url | https://www.mdpi.com/1996-1073/18/14/3722 |
| work_keys_str_mv | AT haotianguo windpowershorttermpredictionmethodbasedontimedomaindualchanneladaptivelearningmodel AT kengwenglao windpowershorttermpredictionmethodbasedontimedomaindualchanneladaptivelearningmodel AT junkunhao windpowershorttermpredictionmethodbasedontimedomaindualchanneladaptivelearningmodel AT xiaoruihu windpowershorttermpredictionmethodbasedontimedomaindualchanneladaptivelearningmodel |