Short-Term Forecast Analysis on Wind Power Generation Data

The forecasting of Wind power generation plays a critical role in the safe and stable operation of a power grid. Grid operators rely on the short-term forecasts of load and generation sources to optimize operations such as unit commitment and economic dispatch. These forecasts needs to be stable and efficient because of the low dispatchability and increasing percentage of renewable energy sources in the generation mix. We will describe the results of our performance study with different forecasting methodologies and will also propose hybrid methods for delivering consistent results with a varying dataset. The National Renewable Energy Laboratory (NREL) wind integration dataset having 5 predictor variables and a data resolution of 5 minutes is used for this analysis. Forecasting methodologies evaluated include ARIMA, RF, SVM, GLM, GAM and four additional hybrid methods. We will reveal the robust models of GLM and GLM based hybrid methods to deliver consistent forecasts of wind power generation.