Short-term wind power forecasting and uncertainty analysis using a hybrid intelligent method

This study proposes a hybrid intelligent method for short-term wind power forecasting and uncertainty analysis. In practice, the power output of a wind turbine is a direct function of wind speed. Owing to the intermittent and irregular nature of wind, the wind power generation is not easily dispatched and the prediction of wind power is highly uncertain. To allow a procedure for more accurate forecasting, a deterministic wind power prediction method that uses multiple support vector regression (SVR) models is established based on the wind power capacity and wind speed forecasts obtained from the Taiwan Central Weather Bureau (TCWB). An enhanced harmony search (EHS) algorithm is then used to estimate the parameters for each SVR model. To assess the risk that is associated with wind power forecasts in a power grid, an EHS-based quantile regression method that accurately reflects the confidence intervals for wind power forecasts is presented. The proposed approach provides wind power forecasts for future 3 h in steps of 15 min and the associated forecasting uncertainty. During testing on three practical wind power generation systems, the proposed method gives better forecasting accuracy and produces more reasonable confidence intervals than existing methods.