Solar Energy Prediction System

Solar Energy Prediction System

As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this renewable resource. NCAR and a team of researchers from public, private, and academic sectors partnered to develop and assess a new, cutting-edge solar power forecasting system called Sun4Cast™. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers.

Sun4Cast™ integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is NCAR’s WRF-SolarTM, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple numerical weather prediction (NWP) models are blended via the DICast® System.  For short-range (0-6 h) forecasts, Sun4Cast leverages several observation-based nowcasting technologies. These technologies are blended via the Nowcasting Expert System Integrator (NESI). The NESI and DICast® systems are subsequently blended to produce short to mid-term irradiance forecasts for solar array locations. The irradiance forecasts are translated into power with uncertainties quantified using an analog ensemble approach, and are provided to the industry partners for real-time decision-making.

After testing Sun4Cast™ at multiple sites, the research team has determined that it can be up to 50 percent more accurate than current solar power forecasts. This improved accuracy will enable utilities to deploy solar energy more reliably and inexpensively, reducing the need to purchase energy on the spot market. 

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