Hybrid methodology for modeling short-term wind power generation using conditional Kernel density estimation and singular spectrum analysis

Abstract

A fundamental part of the probabilistic forecasting of wind energy process is to take into account wind speed forecasts. To achieve accurate probabilistic forecast of wind output, it is developed a hybrid methodology using a nonparametric techniques known as SSA (Singular Spectrum Analysis) and (CKDE) Conditional Kernel Density Estimation. SSA is employed to forecast wind speed and CKDE to obtain probabilistic forecasts of wind energy, based on the fact that wind power generation has a nonlinear relation with the wind speed and both are random variables distributed according to a joint density function. A Brazilian hourly wind dataset including wind speed and wind power is used to illustrate the approach. Once the wind speed forecasts are obtained the corresponding probabilistic forecast of the wind power generation is estimated for a lead time of 24 hours ahead. The results obtained are compared with other existing methodologies.

Keywords

• Wind power generation
• SSA
• CKDE
• time series
• forecasting