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

I. Dimoulkas, P. Mazidi, L. Herre

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.

DOI: DOI icon 10.1109/EEM.2017.7982035    

Published: June 2017.


    Research topics:
  • Forecasting and data mining

IIT-17-078A

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