An effective method for modeling wind power forecast uncertainty
Wind forecasts are an important tool for electric system operators. Proper use of wind power forecasts to make operating decisions must account for the uncertainty associated with the forecast. Data from different regions in the USA with forecasts made by different vendors show the forecast error distribution is strongly dependent on the forecast level of wind power. For low wind power forecast, the forecasts tend to under-predict the actual wind power produced, whereas when the forecast is for high power, the forecast tends to over-predict the actual wind power. Most of the work in this field neglects the influence of wind forecast levels on wind forecast uncertainty and analyzes wind forecast errors as a whole. The few papers that account for this dependence, group wind forecasts by the value of the forecast and fit parametric distributions to actual wind power in each bin of data. In the latter case, different parameters and possibly different distributions are estimated for each data bin. We present a method to model wind power forecast uncertainty as a single closed-form solution using a logit transformation of historical wind power forecast and actual wind power data. Once transformed, the data become close to jointly normally distributed. We show the process of calculating confidence intervals of wind power forecast errors using the jointly normally distributed logit transformed data. This method has the advantage of fitting the entire dataset with five parameters while also providing the ability to make calculations conditioned on the value of the wind power forecast.