Probabilistic forecasting of wind power ramp events using autoregressive logit models
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....
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| Format: | Journal article |
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Elsevier
2016
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| _version_ | 1797094848753303552 |
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| author | Taylor, J |
| author_facet | Taylor, J |
| author_sort | Taylor, J |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T04:19:41Z |
| format | Journal article |
| id | oxford-uuid:ca990b8d-a847-4696-9bb8-774f824448ee |
| institution | University of Oxford |
| last_indexed | 2024-03-07T04:19:41Z |
| publishDate | 2016 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:ca990b8d-a847-4696-9bb8-774f824448ee2022-03-27T07:08:33ZProbabilistic forecasting of wind power ramp events using autoregressive logit modelsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:ca990b8d-a847-4696-9bb8-774f824448eeSymplectic Elements at OxfordElsevier2016Taylor, JA 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. |
| spellingShingle | Taylor, J Probabilistic forecasting of wind power ramp events using autoregressive logit models |
| title | Probabilistic forecasting of wind power ramp events using autoregressive logit models |
| title_full | Probabilistic forecasting of wind power ramp events using autoregressive logit models |
| title_fullStr | Probabilistic forecasting of wind power ramp events using autoregressive logit models |
| title_full_unstemmed | Probabilistic forecasting of wind power ramp events using autoregressive logit models |
| title_short | Probabilistic forecasting of wind power ramp events using autoregressive logit models |
| title_sort | probabilistic forecasting of wind power ramp events using autoregressive logit models |
| work_keys_str_mv | AT taylorj probabilisticforecastingofwindpowerrampeventsusingautoregressivelogitmodels |