Probabilistic forecasting of wind power ramp events using autoregressive logit models

Journal article

A challenge for the efficient operation of power systems and wind farms is the occurrence of wind power ramps, which are sudden large changes in the power output from a wind farm. This paper considers the probabilistic forecasting of a ramp event, defined as exceedance beyond a specified threshold. We directly model the exceedance probability using autoregressive logit models fitted to the change in wind power. These models can be estimated by maximising a Bernoulli likelihood. We introduce a model that simultaneously estimates the ramp event probabilities for different thresholds using a multinomial logit structure and categorical distribution. To model jointly the probability of ramp events at more than one wind farm, we develop a multinomial logit formulation, with parameters estimated using a bivariate Bernoulli distribution. We use a similar approach in a model for jointly predicting one and two steps-ahead. We evaluate post-sample probability forecast accuracy using hourly wind power data from four wind farms.

Authors: Taylor, J

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Elsevier
• Journal: European Journal of Operational Research
• Volume: 259
• Issue: 2
• Pages: 703–712
• Publication date: 2016-10-24
• Acceptance date: 2016-10-20
• DOI: 10.1016/j.ejor.2016.10.041
• EISSN: 1872-6860
• ISSN: 0377-2217
• Keywords: autoregressive logit models; OR in energy; probability forecasting; wind power ramps