Sparse Gaussian process regression for multi-step ahead forecasting of wind gusts combining numerical weather predictions and on-site measurements

Accurate forecasts of wind gusts are crucially important for wind power generation, severe weather warnings, and the regulation of vehicle speed. To improve the short-term and long-term forecasting accuracy, the sparse Gaussian process regression (GPR) model that reduces the complexity of full GPR is employed for wind gust forecasting by combining numerical weather prediction (NWP) data and on-site measurements. Historical measurements of wind gusts and the European Centre for Medium-Range Weather Forecasts (ECMWF) data are used as inputs of sparse GPR models. In particular, the historical wind gust input allows the sparse GPR model to adapt to the local change of wind speed at specific locations. A moving window strategy is introduced to perform multi-step forecasting and reduce the size of training data. The feasibility of the proposed method is illustrated by measurements collected from the outdoor competition venues in the 2022 Winter Olympics. The presented approach is then compared with the ECMWF, GPR, random forest, ECMWF-Sparse GPR, and ECMWF-MLR models. The results indicate that the proposed method exhibits the best forecasting performance than other models, and it improves the forecasting accuracy in both short-term and long-term time scales.

Automated summary

Accurate forecasting of wind gusts is crucial for various applications. This study proposes a method that combines numerical weather prediction data and on-site measurements using a sparse Gaussian process regression model to improve forecasting accuracy. The method adapts to local changes in wind speed by incorporating historical measurements and ECMWF data. Results show that the proposed method outperforms other models in both short-term and long-term forecasting accuracy.