## 1 Introduction
### Intra-Hour Solar Forecasting  
The energy alliance between UTSA and CPS Energy has as one of its primary goals the development of new solar forecasting technologies that combine inexpensive all-sky imaging cameras with sophisticated image processing techniques and artificial intelligence software to produce high-accuracy 15-min ahead solar irradiance forecasts. GHI consists of two components, the Direct Normal Irradiance (DNI) caused by sunlight traveling in a direct path from sun to PV array and the Diffuse Horizontal Irradiance (DHI), background illumination that is due to secondary reflections and absorption/re-radiation. The formula is \(GHI=\cos(\theta_{z})\)\(DNI+DHI\) where \(\theta_{z}\) is the zenith angle. Shadows cast by low-level cumulus clouds significantly impact the DNI but have little effect upon the background DHI. While it is possible to predict DNI separately [21], for verifying PV power output forecasts GHI is used. Figure 2 displays the three quantities: GHI, DNI, and DHI, on the date 27 October 2015 at the NREL ESF facility in Golden, CO. It shows that moderately cloudy conditions occasion multiple ramp events. Figure 1: GOES high-resolution satellite image cropped to show the central Texas region. Figure 3 displays a sequence of eight pictures taken by the Skylmager at the NREL site, one every two minutes starting (upper left) at 12:31 pm MST. At that time the sun is not obscured, but cumulus clouds are moving in from the left. At 12:35 the cloud begins to enter the solar disk and by 12:37 the sun is completely occluded. This continues until 12:44 when the cloud has moved past the sun and the DNI recovers. This ramp event is seen in the DNI oscillations that occur around the noon hour in Figure 2. While this example considers a single ramp event, it strongly suggests that the correlation between measured GHI and the presence of clouds obscuring the sun in the Skylmager pictures could be learned by AI models. The challenge in short term prediction of PV power is simply \"Where will cumulus cloud shadows be fifteen minutes from now?\" Our approach incorporates as much of the physics as possible, but is an idealization necessitated by the requirement to produce GHI forecasts in real time for the MGMS. Solving Navier-Stokes for the true dynamics of the atmosphere is not feasible on a Raspberry Pi. If GHI can be accurately forecast, then predicting PV power output is straightforward. The evolution of clouds and irradiance shown in Figures 2 and 3 is even more striking when video of the images is viewed, confirming that Skylmager pictures are highly correlated with the observed GHI time series. Moreover, it suggests the camera sensor could be used to measure irradiance as well as predict it. Once GHI has been accurately forecast, it is usually straightforward to assign a corresponding PV power output, which is what the MGMS requires. Figure 4 shows the relationship between GHI (Watts/m\({}^{2}\)) and PV power (Watts) from the RSF2 PV arrays located at NREL. The relationship is almost linear with a slight hysteresis effect that reflects the differences in morning versus afternoon irradiance. The task of predicting PV power output over multiple temporal and spatial scales, and from a variety of different equipment is a challenging one [22; 23; 24]. Figure 3: Skylmager sequence (27 October 2015, Golden, CO) showing a ramp event. Figure 2: Daily solar irradiance (W/m\({}^{2}\)) for 27 October 2015, Golden, CO.  
State of the Art in Solar Forecasting  
As photovoltaics achieve greater penetration, the SmartGrid will demand accurate solar forecasting hence a network of low cost, distributed sensors to acquire large amounts of image and weather data for input to the forecasting algorithms. Commercial sky imaging systems often prove too costly and have proprietary software, leading several research groups to develop their own systems. The solar forecasting research group at UC San Diego [25; 26; 27; 28; 29] has done pioneering work in this field for many years. For one example, [PERSON] et al.