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<p>Rapid growth in wind energy highlights the need for accurate forecasting to optimize generation and grid integration. This review analyzes current wind power prediction models, covering their methodologies, strengths, and limitations to guide researchers, engineers, and policymakers. It begins with Numerical Weather Prediction (NWP) models, which are essential yet limited by challenges in complex terrains and localized events. In response, machine learning techniques—such as artificial neural networks, support vector regression, and random forests—have gained prominence for improving forecast accuracy. Advanced methods like bootstrapping and Bayesian model averaging enhance probabilistic forecasts by quantifying uncertainty. The integration of LIDAR and satellite data has further refined wind resource assessment and forecasting accuracy. This review explores the impact of remote sensing and forecasting on decision-making for wind farm operators and grid managers, while also addressing challenges posed by climate change, extreme weather, and the evolution of smart grids. Scenario analysis and grid optimization strategies are discussed, and the review concludes by evaluating current successes and identifying future research needs—emphasizing interdisciplinary collaboration and data sharing. Notably, machine learning improved wind power prediction accuracy by 15% over traditional models. The GFS model achieved an MAE of 0.45 MW and an RMSE of 0.60 MW, demonstrating strong performance in wind energy forecasting.</p>

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Optimizing Wind Energy Integration: A Review of Forecasting Techniques and Emerging Trends

  • 期刊论文

  • 发表日期：

    2025年11月4日

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  • Jaisiva Selvaraj,
  • Lakshmanan Muthuramalingam,
  • Viji Karthikeyan,
  • Alagar Karthick,
  • Vasanthaseelan Sathiyaseelan

摘要

Rapid growth in wind energy highlights the need for accurate forecasting to optimize generation and grid integration. This review analyzes current wind power prediction models, covering their methodologies, strengths, and limitations to guide researchers, engineers, and policymakers. It begins with Numerical Weather Prediction (NWP) models, which are essential yet limited by challenges in complex terrains and localized events. In response, machine learning techniques—such as artificial neural networks, support vector regression, and random forests—have gained prominence for improving forecast accuracy. Advanced methods like bootstrapping and Bayesian model averaging enhance probabilistic forecasts by quantifying uncertainty. The integration of LIDAR and satellite data has further refined wind resource assessment and forecasting accuracy. This review explores the impact of remote sensing and forecasting on decision-making for wind farm operators and grid managers, while also addressing challenges posed by climate change, extreme weather, and the evolution of smart grids. Scenario analysis and grid optimization strategies are discussed, and the review concludes by evaluating current successes and identifying future research needs—emphasizing interdisciplinary collaboration and data sharing. Notably, machine learning improved wind power prediction accuracy by 15% over traditional models. The GFS model achieved an MAE of 0.45 MW and an RMSE of 0.60 MW, demonstrating strong performance in wind energy forecasting.