The system consists of several technologies to predict irradiance at differing timescales. In the short term (0-6 hrs), that involves improving various Nowcasting technologies. These technologies are based on current knowledge of the atmospheric state via observations, then use appropriate methods to predict near-term changes in that state. Each of these various approaches to Nowcasting have merit, and thus, are blended to produce a single best forecast for the end user. These technologies include: StatCast, TSICast, CIRACast, MADCast, and WRF-Solar-Now. In addition, forecasts from various numerical weather (NWP) models are blended via the Dynamic Integrated Forecast (DICast®) System. The Nowcast and DICast® systems are then used to produce the best solar irradiance forecast for industry partner solar array sites. Those irradiances are translated to power forecasts, the uncertainty is quantified using an analog ensemble (AnEn) approach, and provided to the industry partners for their real-time decision making.
The methods to make the forecast require a plethora of historical and real-time data from both models and observations, blended in real-time to provide a seamless forecast for use by decision makers. Thus, forecasting for variable renewable energy, here using solar power as an example, is a real-world Big Data problem. It includes data of large volume, velocity, variety, variability, veracity, and complexity. Each of these will be discussed in the presentation and recommendations will be made for best practices for such real-time forecasting systems.