Abstract
Wind power forecasting is essential for the reliable and efficient operation of wind farms based on power grids, which is significantly important for stakeholders like wind farm owners, power pools, and power traders. Wind power prediction is also crucial for opti mal power dispatch, grid security, and minimizing generation curtailment at wind farms. Hence, an accurate methodology for the power production of wind farms is needed for identifying long-term operational performance, failure detection, and ensuring grid in tegration. The present study proposes a hybrid machine learning (ML) approach that combines the strengths of random forest regression (RFR), artificial neural network (ANN), and support vector regression (SVR), using linear regression (LR) as a meta-model for wind speed forecasting. This modeling approach was trained, validated, and tested on 87,600 hourly data points across 10 variables from high-wind potential sites in the Kingdom of Saudi Arabia: Damat Al Jandal, Taif, Abha, East Coast, and Red Sea. The key findings AcademicEditor: LarsJohanning Received: 9July2026 Revised: 25August2026 Accepted: 31August2026 Published: 3 September2026 Copyright: ©2026bytheauthors. Licensee MDPI,Basel,Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY)license. suggest that the hybrid RFR + ANN + SVR model performed better than individual models, including the simple persistence model, achieving R2 scores up to 95.3 in testing, with train–test performance loss of less than 5% across all scenarios. The mean bias error (MBE) stayed within ±0.17 m/s, and the relative root mean square error (RRMSE) stayed below 15%. The hybrid model outperformed the metric-site standalone models and persistence model. Through feature importance analysis, this study also finds that temperature, rel ative humidity, and cyclical time-encoded features are the most important inputs. For offshore sites, thermal features like pressure and dew point were found to be more impor tant. Due to its demonstrated superior performance across metric-site combinations, this hybrid framework is adaptable to other wind regions with intermittent renewables, with implications for reliable renewable energy integration and grid load stability.