A wind power forecasting method with confidence interval

Abstract

This paper presents a wind power forecasting method with confidence interval. Wind speed forecasts are calculated by regression models using GPV (Grid Point Vale) weather forecasts. The forecasts are adjusted by fuzzy inference using the latest error. The wind power forecasts are translated from the wind speed forecasts using two power-curves. They are combined by fuzzy inference depending on wind direction. The error confidence interval models are generated for each forecasting target time. Each confidence interval is combined by another fuzzy inference. The proposed method has been applied to actual power generators, and found that forecasting errors are better than the conventional methods. The results show the effectiveness of the proposed method. © 2011 IEEE.