A key tenet of our forecasting philosophy is that different methods are necessary to produce improvements in predicting irradiance improvements at differing timescales. In the short term, that involves improving various Nowcasting technologies. Several technologies are being developed for this very short range (0-3 hr). These Nowcasting 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 applying five different approaches that are then blended to produce a single best forecast for the end user. These four technologies include: StatCast, TSICast, CIRACast, MADCast, and WRF-Solar-Now, which are blended to produce the final Nowcast. In addition, the team has been working to improve the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model and tailor it to improve solar irradiance forecasts to produce WRF-Solar. This effort has included frequent communications with NOAA's Earth System Research Laboratory (ESRL) to assure that the improvements can be implemented into their High Resolution Rapid Refresh (HRRR) model that will continue to provide base NWP forecasts during and beyond the project lifetime. The forecasts from the various models are then blended into a seamless forecast system and delivered to the end users to aid in their decision making. To this end, WRF-Solar predictions are blended with the publically available models via the Dynamic Integrated Forecast (DICast) System. The Nowcast and DICast systems are then used to produce the best solar irradiance forecast (GHI, DNI, and POA) for industry partner solar array sites. Those irradiances are translated to power forecasts and provided to the industry partners. The forecasts include an uncertainty estimate based on the analog ensemble technology. The components of the system have been operating during the calendar year 2015. This talk will provide a comparative analysis of the benefits and detriments of the various systems and provide recommendations for best practice forecasting.