Probabilistic Forecasting of Wind and Solar Farm Output

John Boland and Sleiman Farah
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John Boland: Industrial AI Research Centre, UniSA STEM, University of South Australia, Adelaide, SA 5001, Australia
Sleiman Farah: Industrial AI Research Centre, UniSA STEM, University of South Australia, Adelaide, SA 5001, Australia

Energies, 2021, vol. 14, issue 16, 1-15

Abstract: 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.

Keywords: solar farms; wind farms; probabilistic forecasting; prediction interval; homoscedastic; autoregressive moving average (ARMA) models; exponential smoothing; heteroscedastic; autoregressive conditional heteroscedastic (ARCH) effect (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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