## 3 Results
### JBSA Microgrid Data  
The JBSA microgrid was built as a testbed for the CPS Energy Grid Modernization Laboratory: Management and control of a microgrid must address many factors including cybersecurity, data acquisition, data management, real-time computation, storage, bandwidth, interoperability, and usability requirements. In addition to the data acquired by the UTSA equipment-Skylmager, WXT520 Vaisala weather station, and pyranometer; this includes 36-hour ahead hourly weather forecasts scraped from the web, the day-ahead Load/PV forecasts, battery State-Of-Charge (SOC) readings, actual load for the base library building, and control data from the Siemens MGMS. The goal is to use all available data in order to refine site-specific solar irradiance forecasts for improved operation and control of the microgrid. Non-UTSA data from the JBSA microgrid was acquired from Itron MV-90 xi meters. This is a system used for the collection and management of interval data consisting of time stamped readings taken every \(x\) minutes where \(x\) can be 5, 15, 30, or 60. A large electric utility may acquire a billion interval readings in a single year and use them in a variety of ways  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**5 Moderately Cloudy Days**} & \multicolumn{3}{c}{**5 Clear Sky Days**} \\ \hline
**Model Name** & **GLM** & **MLP** & **RFR** & **GBT** & **GLM** & **MLP** & **RFR** & **GBT** \\ \hline MAPE & 39.75 & 13.53 & 15.79 & 20.93 & 12.0 & 2.44 & 2.66 & 3.79 \\ \hline Explained Variance \(R^{2}\) & 0.70 & 0.95 & 0.93 & 0.90 & 0.97 & 1.00 & 1.00 & 0.99 \\ \hline Mean Absolute Error & 86.43 & 30.66 & 33.30 & 44.68 & 20.43 & 5.30 & 5.84 & 8.57 \\ \hline Elapsed Time (Sec) & 0.1 & 513.1 & 69.9 & 12.4 & 0.1 & 512.8 & 60.5 & 12.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results for two datasets of 32 \(\times\) 32 resized sub-images.  
Figure 20: Scatterplots of Actual GHI versus Predicted GHI for 4 ML Models on moderately cloudy days (top row) and clear sky days (bottom row).  
including billing (demand response, real time pricing, curtaillable rates), open market operations, and load/market research.  
Analyses of the JBSA MV90 data, all of which were taken at 15 min intervals, demonstrated that much finer temporal resolution would be required to capture details of ramp events and provide accurate irradiance forecasts to the MGMS. While 1 min resolution was provided by the UTSA equipment such as the pyranometer and Vaisala weather station, the cost of this equipment precluded widespread deployment in a distributed environment. Similar equipment at the UTSA solar testbed provided a wealth of 1 min data, but we envisioned a network of hundreds of low cost SkyImagers spread across the city of San Antonio, and for this scenario low cost was an essential requirement. As previously discussed, there are a plethora of low-cost sensors that can be connected to the GPIO pins on a SBC, including the WeatherBoard2 (WB2), air quality and ozone sensors, and even devices to measure dust. Costing tens of dollars they provide a cost-effective way of environmental sensing that can easily be incorporated with the SkyImager. Figure 21 shows observations of irradiance, temperature, humidity, and pressure taken every minute on 26 November 2018 with the WB2 sensor mounted on a SkyImager.  
The mini photovoltaic module used at ULL is an off-the-shelf PV module, made of c-Si, with open circuit voltage of 6V and a short circuit current of 200 mA, with a maximum DC output of 1.1 W. The module was connected to a resistive load to dissipate the heat, and the data was registered by a INA219 DC Current Sensor able to measure very small currents, attached to the Raspberry Pi 3 model B.