GEFCom2014: Probabilistic solar and wind power forecasting using a generalized additive tree ensemble approach

摘要

We investigate the probabilistic forecasting of solar and wind power generation in connection with the Global Energy Forecasting Competition 2014. We use a voted ensemble of a quantile regression forest model and a stacked random forest gradient boosting decision tree model to predict the probability distribution. The raw probabilities thus obtained need to be post-processed using isotonic regression in order to conform to the monotonic-increase attribute of probability distributions. The results show a great performance in terms of the weighted pinball loss, with the model achieving second place on the final competition leaderboard. (C) 2016 Published by Elsevier B.V. on behalf of International Institute of Forecasters.