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 (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.

Automated summary

Accurately forecasting output of grid connected wind and solar systems is crucial for increasing renewable energy penetration on the electrical network. Statistical forecasting tools were used to generate forecasts with prediction intervals and tested on wind and solar farms in Australia, showing good performance and adaptability for short term forecasting on different time scales.