Empowering Wind Energy Output Optimization: Comparative Assessment of Hybrid Artificial Intelligence Models Towards Wind Speed Forecasting Accuracy
TL;DR
This study proposes a hybrid machine learning approach that combines the strengths of random forest regression, artificial neural network, and support vector regression as a meta-model for wind speed forecasting, and finds that temperature, relative humidity, and cyclical time-encoded features are the most important inputs.
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 optimal 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 integration. 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 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, relative 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 important. 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.