Probabilistic Forecasting of Wind and Solar Farm Output
Accurately forecasting the output of grid connected wind and solar systems is critical to increasing the overall penetration of renewables on the electrical network. This includes not only forecasting the expected level, but also putting error bounds on the forecast. The National Electricity Market...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/16/5154 |
| _version_ | 1797523959402463232 |
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| author | John Boland Sleiman Farah |
| author_facet | John Boland Sleiman Farah |
| author_sort | John Boland |
| collection | DOAJ |
| description | Accurately forecasting the output of grid connected wind and solar systems is critical to increasing the overall penetration of renewables on the electrical network. This includes not only forecasting the expected level, but also putting error bounds on the forecast. The National Electricity Market (NEM) in Australia operates on a five minute basis. We used statistical forecasting tools to generate forecasts with prediction intervals, trialing them on one wind and one solar farm. In classical time series forecasting, construction of prediction intervals is rudimentary if the error variance is constant—Termed homoscedastic. However, if the variance changes—Either conditionally as with wind farms, or systematically because of diurnal effects as with solar farms—The task is much more complicated. The tools were trained on segments of historical data and then tested on data not used in the training. Results from the testing set showed good performance using metrics, including Coverage and Interval Score. The methods used can be adapted to various time scales for short term forecasting. |
| first_indexed | 2024-03-10T08:50:32Z |
| format | Article |
| id | doaj.art-439652451372431c8c9783ba5218d09c |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T08:50:32Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-439652451372431c8c9783ba5218d09c2023-11-22T07:32:45ZengMDPI AGEnergies1996-10732021-08-011416515410.3390/en14165154Probabilistic Forecasting of Wind and Solar Farm OutputJohn Boland0Sleiman Farah1Industrial AI Research Centre, UniSA STEM, University of South Australia, Adelaide, SA 5001, AustraliaIndustrial AI Research Centre, UniSA STEM, University of South Australia, Adelaide, SA 5001, AustraliaAccurately forecasting the output of grid connected wind and solar systems is critical to increasing the overall penetration of renewables on the electrical network. This includes not only forecasting the expected level, but also putting error bounds on the forecast. The National Electricity Market (NEM) in Australia operates on a five minute basis. We used statistical forecasting tools to generate forecasts with prediction intervals, trialing them on one wind and one solar farm. In classical time series forecasting, construction of prediction intervals is rudimentary if the error variance is constant—Termed homoscedastic. However, if the variance changes—Either conditionally as with wind farms, or systematically because of diurnal effects as with solar farms—The task is much more complicated. The tools were trained on segments of historical data and then tested on data not used in the training. Results from the testing set showed good performance using metrics, including Coverage and Interval Score. The methods used can be adapted to various time scales for short term forecasting.https://www.mdpi.com/1996-1073/14/16/5154solar farmswind farmsprobabilistic forecastingprediction intervalhomoscedasticautoregressive moving average (ARMA) models |
| spellingShingle | John Boland Sleiman Farah Probabilistic Forecasting of Wind and Solar Farm Output Energies solar farms wind farms probabilistic forecasting prediction interval homoscedastic autoregressive moving average (ARMA) models |
| title | Probabilistic Forecasting of Wind and Solar Farm Output |
| title_full | Probabilistic Forecasting of Wind and Solar Farm Output |
| title_fullStr | Probabilistic Forecasting of Wind and Solar Farm Output |
| title_full_unstemmed | Probabilistic Forecasting of Wind and Solar Farm Output |
| title_short | Probabilistic Forecasting of Wind and Solar Farm Output |
| title_sort | probabilistic forecasting of wind and solar farm output |
| topic | solar farms wind farms probabilistic forecasting prediction interval homoscedastic autoregressive moving average (ARMA) models |
| url | https://www.mdpi.com/1996-1073/14/16/5154 |
| work_keys_str_mv | AT johnboland probabilisticforecastingofwindandsolarfarmoutput AT sleimanfarah probabilisticforecastingofwindandsolarfarmoutput |