Probabilistic Forecasting of Wind Power Ramp Events using Autoregressive Logit Models

Taylor, James (2016) Probabilistic Forecasting of Wind Power Ramp Events using Autoregressive Logit Models. European Journal of Operational Research, 259 (2). pp. 703-712.

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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.

Item Type: Article
Keywords: OR in energy, wind power ramps, probability forecasting, autoregressive logit models, management science
Subject(s): Management science
Date Deposited: 14 Nov 2016 11:14
Last Modified: 27 Oct 2018 02:38
Funders: N/A

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