Probabilistic load forecasting using post-processed weather ensemble predictions
Probabilistic forecasting of electricity demand (load) facilitates the efficient management and operations of energy systems. Weather is a key determinant of load. However, modelling load using weather is challenging because the relationship cannot be assumed to be linear. Although numerous studies...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
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Taylor and Francis
2022
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| _version_ | 1826310844922724352 |
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| author | Ludwig, N Arora, S Taylor, JW |
| author_facet | Ludwig, N Arora, S Taylor, JW |
| author_sort | Ludwig, N |
| collection | OXFORD |
| description | Probabilistic forecasting of electricity demand (load) facilitates the efficient management and operations of energy systems. Weather is a key determinant of load. However, modelling load using weather is challenging because the relationship cannot be assumed to be linear. Although numerous studies have focussed on load forecasting, the literature on using the uncertainty in weather while estimating the load probability distribution is scarce. In this study, we model load for Great Britain using weather ensemble predictions, for lead times from one to six days ahead. A weather ensemble comprises a range of plausible future scenarios for a weather variable. It has been shown that the ensembles from weather models tend to be biased and underdispersed, which requires that the ensembles are post-processed. Surprisingly, the post-processing of weather ensembles has not yet been employed for probabilistic load forecasting. We post-process ensembles based on: (1) ensemble model output statistics: to correct for bias and dispersion errors by calibrating the ensembles, and (2) ensemble copula coupling: to ensure that ensembles remain physically consistent scenarios after calibration. The proposed approach compares favourably to the case when no weather information, raw weather ensembles or post-processed ensembles without ensemble copula coupling are used during the load modelling. |
| first_indexed | 2024-03-07T07:58:03Z |
| format | Journal article |
| id | oxford-uuid:c4d76c24-d3f7-4119-92e7-9abbb10e3658 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:58:03Z |
| publishDate | 2022 |
| publisher | Taylor and Francis |
| record_format | dspace |
| spelling | oxford-uuid:c4d76c24-d3f7-4119-92e7-9abbb10e36582023-09-04T10:24:45ZProbabilistic load forecasting using post-processed weather ensemble predictionsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:c4d76c24-d3f7-4119-92e7-9abbb10e3658EnglishSymplectic ElementsTaylor and Francis2022Ludwig, NArora, STaylor, JWProbabilistic forecasting of electricity demand (load) facilitates the efficient management and operations of energy systems. Weather is a key determinant of load. However, modelling load using weather is challenging because the relationship cannot be assumed to be linear. Although numerous studies have focussed on load forecasting, the literature on using the uncertainty in weather while estimating the load probability distribution is scarce. In this study, we model load for Great Britain using weather ensemble predictions, for lead times from one to six days ahead. A weather ensemble comprises a range of plausible future scenarios for a weather variable. It has been shown that the ensembles from weather models tend to be biased and underdispersed, which requires that the ensembles are post-processed. Surprisingly, the post-processing of weather ensembles has not yet been employed for probabilistic load forecasting. We post-process ensembles based on: (1) ensemble model output statistics: to correct for bias and dispersion errors by calibrating the ensembles, and (2) ensemble copula coupling: to ensure that ensembles remain physically consistent scenarios after calibration. The proposed approach compares favourably to the case when no weather information, raw weather ensembles or post-processed ensembles without ensemble copula coupling are used during the load modelling. |
| spellingShingle | Ludwig, N Arora, S Taylor, JW Probabilistic load forecasting using post-processed weather ensemble predictions |
| title | Probabilistic load forecasting using post-processed weather ensemble predictions |
| title_full | Probabilistic load forecasting using post-processed weather ensemble predictions |
| title_fullStr | Probabilistic load forecasting using post-processed weather ensemble predictions |
| title_full_unstemmed | Probabilistic load forecasting using post-processed weather ensemble predictions |
| title_short | Probabilistic load forecasting using post-processed weather ensemble predictions |
| title_sort | probabilistic load forecasting using post processed weather ensemble predictions |
| work_keys_str_mv | AT ludwign probabilisticloadforecastingusingpostprocessedweatherensemblepredictions AT aroras probabilisticloadforecastingusingpostprocessedweatherensemblepredictions AT taylorjw probabilisticloadforecastingusingpostprocessedweatherensemblepredictions |