Optimising the use of ensemble information in numerical weather forecasts of wind power generation
Electricity generation output forecasts for wind farms across Europe use numerical weather prediction (NWP) models. These forecasts influence decisions in the energy market, some of which help determine daily energy prices or the usage of thermal power generation plants. The predictive skill of powe...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
IOP Publishing
2019
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| _version_ | 1826264828578103296 |
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| author | Stanger, J Finney, I Weisheimer, A Palmer, T |
| author_facet | Stanger, J Finney, I Weisheimer, A Palmer, T |
| author_sort | Stanger, J |
| collection | OXFORD |
| description | Electricity generation output forecasts for wind farms across Europe use numerical weather prediction (NWP) models. These forecasts influence decisions in the energy market, some of which help determine daily energy prices or the usage of thermal power generation plants. The predictive skill of power generation forecasts has an impact on the profitability of energy trading strategies and the ability to decrease carbon emissions. Probabilistic ensemble forecasts contain valuable information about the uncertainties in a forecast. The energy market typically takes basic approaches to using ensemble data to obtain more skilful forecasts. There is, however, evidence that more sophisticated approaches could yield significant further improvements in forecast skill and utility.In this letter, the application of ensemble forecasting methods to the aggregated electricity generation output for wind farms across Germany is investigated using historical ensemble forecasts from the European Centre for Medium-Range Weather Forecasting (ECMWF). Multiple methods for producing a single forecast from the ensemble are tried and tested against traditional deterministic methods. All the methods exhibit positive skill, relative to a climatological forecast, out to a lead time of at least seven days. A wind energy trading strategy involving ensemble data is implemented and produces significantly more profit than trading strategies based on single forecasts. It is thus found that ensemble spread is a good predictor for wind power forecast uncertainty and is extremely valuable at informing wind energy trading strategy. |
| first_indexed | 2024-03-06T20:14:07Z |
| format | Journal article |
| id | oxford-uuid:2b912086-7e26-4e14-bf1b-33f2ea6a1f81 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T20:14:07Z |
| publishDate | 2019 |
| publisher | IOP Publishing |
| record_format | dspace |
| spelling | oxford-uuid:2b912086-7e26-4e14-bf1b-33f2ea6a1f812022-03-26T12:31:45ZOptimising the use of ensemble information in numerical weather forecasts of wind power generationJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:2b912086-7e26-4e14-bf1b-33f2ea6a1f81EnglishSymplectic Elements at OxfordIOP Publishing2019Stanger, JFinney, IWeisheimer, APalmer, TElectricity generation output forecasts for wind farms across Europe use numerical weather prediction (NWP) models. These forecasts influence decisions in the energy market, some of which help determine daily energy prices or the usage of thermal power generation plants. The predictive skill of power generation forecasts has an impact on the profitability of energy trading strategies and the ability to decrease carbon emissions. Probabilistic ensemble forecasts contain valuable information about the uncertainties in a forecast. The energy market typically takes basic approaches to using ensemble data to obtain more skilful forecasts. There is, however, evidence that more sophisticated approaches could yield significant further improvements in forecast skill and utility.In this letter, the application of ensemble forecasting methods to the aggregated electricity generation output for wind farms across Germany is investigated using historical ensemble forecasts from the European Centre for Medium-Range Weather Forecasting (ECMWF). Multiple methods for producing a single forecast from the ensemble are tried and tested against traditional deterministic methods. All the methods exhibit positive skill, relative to a climatological forecast, out to a lead time of at least seven days. A wind energy trading strategy involving ensemble data is implemented and produces significantly more profit than trading strategies based on single forecasts. It is thus found that ensemble spread is a good predictor for wind power forecast uncertainty and is extremely valuable at informing wind energy trading strategy. |
| spellingShingle | Stanger, J Finney, I Weisheimer, A Palmer, T Optimising the use of ensemble information in numerical weather forecasts of wind power generation |
| title | Optimising the use of ensemble information in numerical weather forecasts of wind power generation |
| title_full | Optimising the use of ensemble information in numerical weather forecasts of wind power generation |
| title_fullStr | Optimising the use of ensemble information in numerical weather forecasts of wind power generation |
| title_full_unstemmed | Optimising the use of ensemble information in numerical weather forecasts of wind power generation |
| title_short | Optimising the use of ensemble information in numerical weather forecasts of wind power generation |
| title_sort | optimising the use of ensemble information in numerical weather forecasts of wind power generation |
| work_keys_str_mv | AT stangerj optimisingtheuseofensembleinformationinnumericalweatherforecastsofwindpowergeneration AT finneyi optimisingtheuseofensembleinformationinnumericalweatherforecastsofwindpowergeneration AT weisheimera optimisingtheuseofensembleinformationinnumericalweatherforecastsofwindpowergeneration AT palmert optimisingtheuseofensembleinformationinnumericalweatherforecastsofwindpowergeneration |