Probabilistic wind power forecasting based on the laplace distribution and golden search

Abstract

The point forecast of wind power and its error distribution are estimated under the assumption that the error distribution follows known distributions in a closed form. The point forecast is estimated via the gradient boosting machine (GBM). The mean of the error distribution is assumed to be zero, and the standard deviation (STD) of the error distribution is found to minimize the sum of the mismatches between the quantiles of the error distribution and the actual target value. The mismatch is measured by the pinball loss function, and the optimal STD is found through the golden section search method. The performance of our proposed algorithm is verified by using the numerical weather prediction (NWP) and wind power data from the 2014 Global Energy Forecasting Competition (GEFCom). Our current benchmark ranking is second according to the published competition result of 14th week.