Forecasting wind power - an ensemble technique with gradual coopetitive weighting based on weather situation

Abstract

The prediction of the power generation of wind farms is a non-trivial problem with increasing importance during the last decade due to the rapid increase of wind power generation in the power grid. The prediction task is commonly addressed using numerical weather predictions, statistical methods, or machine learning techniques. Various articles have shown that ensemble techniques for forecasting can yield better results regarding forecasting accuracy than single techniques alone. Typical ensembles make use of a parameter, or data diversity approach to build the models. In this article, we propose a novel ensemble technique using both, cooperative and competitive characteristics of ensembles to gradually adjust the influences of single forecasting algorithms in the ensemble based on their individual strengths using a “coopetitive” weighting formula. The observed quality of the models during training is used to adaptively weigh the models based on the location in the input data space (i.e., depending on the weather situation). We compute the overall weights for a particular weather situation using both, a spatial as well as a global weighting term. The experimental evaluation is performed on a data set consisting of data from 45 wind farms, which is made publicly available. We demonstrate that the technique is among the best performing algorithms compared to other state-of-the-art algorithms and ensembles. Furthermore, the practical applicability of the proposed technique is discussed.