Abstract Details
Activity Number:
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623
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Type:
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Invited
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Date/Time:
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Thursday, August 7, 2014 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #310710
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View Presentation
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Title:
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Probabilistic Forecasting of Wind Power Ramps Using Autoregressive Logit Models
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Author(s):
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James W. Taylor*+
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Companies:
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University of Oxford
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Keywords:
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wind power ramps ;
probability forecasting ;
autoregressive logit models
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Abstract:
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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 increases or decreases in the power output from a wind farm. This paper considers the probabilistic forecasting of a ramp, defined as exceedance beyond a specified threshold. In this paper, we directly model the exceedance probability using autoregressive logit models fitted to wind power time series. These models can be estimated by maximizing a Bernoulli likelihood. To try to capture the extent to which an observation does or does not exceed the threshold, we also consider a likelihood based on the asymmetric Laplace density, which has previously been employed for quantile estimation. We simultaneously estimate the probabilities of exceeding different thresholds using a categorical distribution. To model jointly the probability of ramps at more than one wind farm, we use a spatial autoregressive logit model, estimated using a multivariate Bernoulli density. We evaluate one step-ahead probability forecasts for hourly wind power data using the Brier score and a test for conditional coverage.
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Authors who are presenting talks have a * after their name.
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