Solar power forecasting

[1] As major barriers to solar energy implementation, such as materials cost and low conversion efficiency, continue to fall, issues of intermittency and reliability have come to the fore.

Forecast horizons below 1 hour typically require ground based sky imagery and sophisticated time series and machine learning models.

This class of techniques includes the use of any kind of statistical approach, such as autoregressive moving averages (ARMA, ARIMA, etc.

[8] An important element of nowcasting solar power are ground based sky observations and basically all intra-day forecasts.

Basically all highly accurate short term forecasting methods leverage several data input streams such as meteorological variables, local weather phenomena and ground observations along with complex mathematical models.

For intra-day forecasts, local cloud information is acquired by one or several ground-based sky imagers at high frequency (1 minute or less).

The combination of these images and local weather measurement information are processed to simulate cloud motion vectors and optical depth to obtain forecasts up to 30 minutes ahead.

[10] These methods leverage the several geostationary Earth observing weather satellites (such as Meteosat Second Generation (MSG) fleet) to detect, characterise, track and predict the future locations of cloud cover.

As it was mentioned before and detailed in Heinemann et al., these statistical approaches comprises from ARMA models, neural networks, support vector machines, etc.