Wind Power Forecasting

Follow this topic Ask about this topic

Wind power forecasting estimates future turbine or farm output, including short-term changes and rare ramp events, using operational measurements alongside numerical weather predictions and satellite observations. Approaches range from tree ensembles and recurrent networks to graph and operator models that encode spatial or physical structure, with probabilistic and conformal methods estimating forecast uncertainty. Pretrained and federated models aim to transfer forecasts across sites with little or no local training, while benchmarks increasingly test diverse conditions and extreme weather. A central challenge is achieving reliable, calibrated forecasts across farms and weather regimes: average accuracy can hide failures during extremes, and model complexity alone does not ensure robustness.

Latest papers

24 papers2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Guarded Agent-in-the-Loop Residual Adaptation for Evolving Wind Power Regimes
Oct 26, 2026Yuhang Dong, Min Xia, Jinghong Li +3Wind Power Forecasting Non-Stationary Time Series Forecasting
Nanjing University of Information Science and Technology, Nanjing, China
Multi-site Learning for Forecasting Day-Ahead Wind-Power Production for Onshore Wind Farms in Great Britain with Deep Neural Networks
Oct 18, 2026James Kean, Aidan O'SullivanWind Power Forecasting Time Series Forecasting
UCL Energy Institute, University College London
Nearest Neighbor Functional Curve Completion for Intraday Probabilistic Revisions of Renewable Generation Forecasts
Oct 12, 2026Guillermo Terrén-Serrano, Michael LudkovskiProbabilistic Time Series Forecasting Time Series Forecasting
Environmental Markets Lab (emLab), University of California Santa Barbara, Santa Barbara, CA, USA. · Environmental Studies Program, University of California Santa Barbara, Santa Barbara, CA, USA. · Statistics and Applied Probability, University of California Santa Barbara, Santa Barbara, CA, USA.
WPBench: A Comprehensive Benchmark for Wind Power Forecasting
Sep 21, 2026Yuhan Zhu, Jilin Hu, Xinying Cai +7Multivariate Time Series Forecasting Benchmark Design
School of Data Science & Engineering, East China Normal University, Shanghai, China · College of Computer Science and Technology, Zhejiang University, Hangzhou, China · Hangzhou Hikvision Digital Technology Co., Ltd., Hangzhou, China
Predicting Wind Turbine Power Using Machine Learning and Weather Forecasts
Sep 5, 2026Khivishta Boodhoo, Isaac Triguero, Josh Plumbly +2Wind Power Forecasting Time Series Forecasting
Low Carbon Energy and Resources Technologies Research Group, Faculty of Engineering, University of Nottingham. · Computational Optimisation and Learning Lab, School of Computer Science, University of Nottingham, Nottingham, United Kingdom · Department of Computer science and Artificial Intelligence,University of Granada, Spain +3
Hybrid Semantic Context-Enhanced Ensemble Learning for Wind Power Ramp-Event Forecasting and Uncertainty-Aware Evaluation
Aug 29, 2026Momina Liaqat Ali, Muhammad Abid, Muhammad Abdullah +1Wind Power Forecasting Time Series Forecasting
Department of Industrial and Systems Engineering, University of Tennessee, Knoxville Knoxville, TN 37996, USA. · Department of Mechanical and Aerospace Engineering, University of Tennessee, Knoxville Knoxville, TN 37996, USA. · Department of Mechanical Engineering, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan. +1
Technical Comparative Benchmarking Study: Advanced AI Hybrid Methods for Renewable Energy Farm Optimization and Forecasting
Aug 27, 2026Majid Masoumi, Asghar Dashtiy, Mohammad Dehghan +1Wind Power Forecasting Forecasting Benchmarks
Department of Computer Science Yazd University Yazd · Department of Mechanical Engineering Yazd University Yazd
Skillful forecasting of offshore winds from satellite scatterometer constellations
Jul 29, 2026Francesco Pinto, Luca Lanzilao, Paco Lopez Dekker +1Remote Sensing Spatiotemporal Forecasting
Department of Geoscience and Remote Sensing, Delft University of Technology, Stevinweg 1, 2628 CN, Delft, The Netherlands · School of Engineering and Computer Science, Bern University of Applied Sciences, Quellgasse 21, 2501, Biel, Switzerland
Fourier Geometric Wind Power Forecasting with Numerical Weather Prediction
Jul 19, 2026Shiyuan Piao, Fan Zehui, Yang Liu +4Multivariate Time Series Forecasting Time Series Forecasting
The Hong Kong University of Science and Technology · The Chinese University of Hong Kong · Tsinghua University Goldwind Science and Technology Co.,Ltd
Improving Wind and Solar Power Prediction with Efficient Wrapper-based Feature Selection: An Empirical Study
Jul 15, 2026Daniel Grillmeyer, Marius Hadry, Michael Stenger +3Solar Power Forecasting Feature Selection
University of Würzburg, Würzburg, Germany · Baden-Wuerttemberg Cooperative State University Mosbach, Mosbach, Germany
Climate-Invariant Conformal Prediction Intervals for Multi-Horizon Solar and Wind Forecasting
Jul 13, 2026Shreedhar Gangwar, Abhinav Bains, Banalaxmi BrahmaRegression Prediction Intervals Group-Conditional Conformal Prediction
B. R. Ambedkar National Institute of Technology, Jalandhar, India · Department of Computer Science and Engineering, Dr. B.R. Ambedkar National Institute of Technology Jalandhar, India
UniWind: Day-Ahead Wind Power Forecasting via Physics-Informed State Routing
Jul 2, 2026Ronghui Xu, Tongxin Wu, Guozhen Zhang +4Time Series Forecasting Physics-Informed ML
East China Normal University, Shanghai, China · Southern University of Science and Technology, Shenzhen, China · TsingRoc.ai, Beijing, China +1
Tyan-WP: A Wind Power Foundation Model for Ultra-Short-Term Probabilistic Forecasting
Jun 7, 2026Jiahui Huang, Ao Luo, Lei Liu +6Zero-Shot Time Series Forecasting Time Series Forecasting
School of Information Science and Technology, University of Science and Technology of China, Hefei, 230022, China · China Electric Power Research Institute, Beijing, 100192, China
A Systematic Evaluation of Current Architectures in Wind Power Forecasting
Jun 1, 2026Vinicius Bortolini, Gilson Adamczuk Oliveira, Erick Oliveira Rodrigues +1Time Series Forecasting Wind Power Forecasting
Industrial and Systems Engineering Graduate Program (PPGEPS), Federal University of Technology Parand (UTFPR), Pato Branco, Parand $5503-390, Brazil
LLM Agent Based Renewable Energy Forecasting Using Edge and IoT Data A Review of Solar Wind Weather and Grid Aware Decision Support
May 24, 2026Pavan Manjunath, Thomas PrueferTime Series Forecasting LLM-Based Time Series Forecasting
Independent Researcher
A Posterior-Predictive Variance Decomposition for Epistemic and Aleatoric Uncertainty in Wind Power Forecasting
May 21, 2026Yinsong Chen, Samson S. Yu, Kashem M. MuttaqiUncertainty Quantification Time Series Forecasting
School of Engineering, Deakin University, Melbourne, VIC 3216, Australia · ARC Training Centre in Energy Technologies for Future Grids, School of Engineering, University of Wollongong, Wollongong, NSW 2522, Australia
ECTO: Exogenous-Conditioned Temporal Operator for Ultra-Short-Term Wind Power Forecasting
May 12, 2026Cao Yuan, Junjun WangTime Series Forecasting Non-Stationary Time Series Forecasting
Wuhan Polytechnic University, Wuhan, 430048, Hubei, China
A Quantum Inspired Variational Kernel and Explainable AI Framework for Cross Region Solar and Wind Energy Forecasting
May 9, 2026Pavan Manjunath, Thomas PruferPower Systems Time Series Forecasting
Independent Research Scholar, Germany
A Direct Classification Approach for Reliable Wind Ramp Event Forecasting under Severe Class Imbalance
Mar 20, 2026Alejandro Morales-Hernández, Fabrizio De Caroa, Gian Marco Paldino +3Wind Power Forecasting Class-Imbalanced Learning
Machine Learning Group, Universite Libre de Bruxelles, Brussels, 1050, Belgium · University of Sannio, Benevento, 82100, Italy · WEL Research Institute, Wavre, 1300, Belgium
On Additive Gaussian Processes for Wind Farm Power Prediction
Mar 18, 2026Simon M. Brealy, Lawrence A. Bull, Daniel S. Brennan +4Wind Power Forecasting Gaussian Processes
Dynamics Research Group, Department of Mechanical Engineering, The University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK · Computational Statistics and Machine Learning Group, Department of Engineering, University of Cambridge, Cambridge, CB3 0FA, UK · Vattenfall R&D, Vattenfall AB, Älvkarleby, Sweden
A Behaviour-Aware Federated Forecasting Framework for Distributed Stand-Alone Wind Turbines
Mar 5, 2026Bowen Li, Xiufeng Liu, Maria Sinziiana AstefanoaeiWind Power Forecasting Clustered Federated Learning
IT University of Copenhagen, Denmark. · Technical University of Denmark (DTU), Denmark
FreeGNN: Continual Source-Free Graph Neural Network Adaptation for Renewable Energy Forecasting
Mar 2, 2026Abderaouf Bahi, Amel Ourici, Ibtissem Gasmi +3Source-Free Domain Adaptation Continual Test-Time Adaptation
Computer Science and Applied Mathematics Laboratory (LIMA), Faculty of Science and Technology, Chadli Bendjedid University, P.O. Box 73, El Tarf 36000, Algeria · Mathematical Modeling and Numerical Simulation Laboratory (LAM2SIN), Faculty of Technology, Badji Mokhtar University, P.O. Box 12, Annaba 23000, Algeria · College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates
R $^2$ Energy: A Large-Scale Benchmark for Robust Renewable Energy Forecasting under Diverse and Extreme Conditions
Feb 17, 2026Zhi Sheng, Yuan Yuan, Guozhen Zhang +1Forecasting Benchmarks Time Series Forecasting
Center for Urban Science and Computation, Tsinghua University · Courant Institute of Mathematical Sciences, New York University
Probabilistic Wind Power Forecasting with Tree-Based Machine Learning and Weather Ensembles
Feb 13, 2026Max Bruninx, Diederik van Binsbergen, Timothy Verstraeten +2Gradient-Boosted Decision Trees Ensemble Learning
Vrije Universiteit Brussel, Brussels, Belgium · Norwegian University of Science and Technology, Trondheim, Norway

Oct 26, 2026J/K move · Enter open · S save

Guarded Agent-in-the-Loop Residual Adaptation for Evolving Wind Power Regimes

Yuhang Dong, Min Xia, Jinghong Li, Yue Dai+2

Nanjing University of Information Science and Technology, Nanjing, China

Wind Power Forecasting Non-Stationary Time Series Forecasting Agentic Time Series Forecasting Online Time Series Forecasting Residual Learning

Open paper AskPDF arXiv

Wind power forecasting models are often deployed as fixed temporal backbones, but the errors they make can vary across meteorological regimes such as cold waves, heat-and-calm periods, productive typhoon, and destructive storms. We present a guarded Agent-in-the-loop residual adaptation framework that leaves the temporal backbone unchanged and adapts only a small set of scene-conditioned residual experts. An Agent layer interprets external scene information, identifies the current regime, selects residual experts, and applies a correction only when recent observed errors support it. New experts enter the pool after they outperform their initializations on a validation set, keeping model growth under explicit control. Backbone-only and Agent-enhanced forecasts are compared at lead times of 4, 8, 12, 24, and 48 across nine time-series backbones. Across these runs, the guarded framework lowers both MAE and RMSE for all 45 backbone-horizon combinations, with average MAE reductions ranging from 11.21% to 20.26% across backbones. Online-selection baselines, component ablations, and routing analyses identify the roles of residual experts, scene-aware routing, and the online acceptance rule. Candidate-scene runs evaluate the promotion path under controlled conditions; long-term unattended deployment was not evaluated.