A wind power forecasting method and its confidence interval estimation

Abstract

This paper describes a wind power forecasting method and its confidence interval estimation. Recently, flat control of wind power generators by various batteries is required. For the flat control, accurate wind power forecasts and their error confidence intervals are needed. In this paper, wind speed forecasts are calculated by regression models using GPV (Grid Point Vale) weather forecasts. The forecasts are adjusted by the fuzzy inference using the latest errors. The wind power forecasts are translated from the wind speed forecasts using two power-curves. The power-curves are selected or 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 the other fuzzy inference. The proposed methods are applied to actual wind power generators, and found that forecasting errors are better than the conventional methods. The almost all of forecasts can be within error confidence intervals estimated by the proposed method. The results show the effectiveness of the proposed methods. © 2011 The Institute of Electrical Engineers of Japan.