EEM 2017 Forecast Competition: Wind power generation prediction using autoregressive models

Energy forecasting provides essential contribution to integrate renewable energy sources into power systems. Today, renewable energy from wind power is one of the fastest growing means of power generation. As wind power forecast accuracy gains growing significance, the number of models used for forecasting is increasing as well. In this paper, we propose an autoregressive (AR) model that can be used as a benchmark model to validate and rank different forecasting models and their accuracy. The presented paper and research was developed within the scope of the European energy market (EEM) 2017 wind power forecasting competition.

I. Introduction

Wind power is one of the fastest growing means of power generation. In a time of paradigm change in energy policy and intensified competition of renewable energy sources, wind power offers several environmental benefits. However, due to its intermittent nature, the integration of wind power poses challenges on power system operation [1]–[3]. In order to minimize power imbalance, appropriate means of forecasting power production are essential. Since forecasting is inherently erroneous, the target is to minimize the forecast error. A vast number of forecast models have been developed in research. The state-of-the-art in wind power forecasting is summarized in [1] and [4]. In the review of [5], five basic types of forecasting models are identified.