## 1 Introduction
The most expensive component was the all-weather security camera enclosure ($350). In addition, General Purpose IO (GPIO) pins would allow a variety of external sensors to be connected to the Pi. Low cost and ease of use were essential if the Skylmager were to be deployed in a rural sustainable development microgrid. In contrast to some commercial all-sky imaging systems, only open source software would be used. The Pi accommodates several operating systems (OS) including Raspbian, a Linux-based derivative of Debian. It can be operated with a monitor or in \"headless\" mode, and once deployed can be accessed remotely with SSH/SFTP. Code for the Skylmager would be written in Python and allow calls to libraries such as Open Computer Vision, Scikit-Learn, SQLite, and Mosquito. In the summer of 2014 it was an open question whether the proposed imager could deliver the functionality of much more expensive systems and be thoroughly tested before its deployment. As part of the DOE microgrid INTEGRATE program, the first deployment of the Skylmager occurred in Fall 2015 at the ESF building at NREL. INTEGRATE aggregated sustainable generation and loads into microgrids controlled by an Energy Management System with the OpenFMB protocol. In 2016 a second SkyImager was installed at the CPS Energy microgrid at Joint Base San Antonio. A multi-year collaboration between CPS-UTSA and the Universidad de La Laguna resulted in the deployment of two Skylmagers in the Canary Islands. These utilize stereoscopic images to determine heights of the bases of cumulus clouds. Deployment of Skylmagers in three diverse locations provided not only big data, but operational experience in harsh extremes of climate. This resulted in improvements and additions to our original design: weatherproofing, new environmental sensors, the need for scheduled maintenance, optimal positioning of the camera, communications with OpenFMB publish-subscribe protocols, and using WiFi and cloud computing. The Skylmager will be an integral part of the command and control for microgrids, both as part of the larger SmartGrid in an urban environment or in an islanded mode in a military or rural setting. Solar forecasting is widely considered a key means of integrating solar power efficiently and reliably into the electric grid. For a utility to meet projected customer demand with electricity from sustainable resources, high-accuracy global horizontal irradiance (GHI) forecasts must be available over widely different time and space scales. A convenient separation of this forecasting problem into two parts is as follows: (1) _intra-hour forecasts_ of the ramp events that are caused when cumulus clouds move between the sun and the solar panels, and (2) _day-ahead forecasts_ for 12, 24, and perhaps 36 h into the future. There is overlap between the two parts, but this taxonomy is convenient not only because the physics and forecasting techniques are generally different, but also the way in which the utility makes use of the forecasts. A single ramp event on a microgrid powered primarily by PV arrays can result in over/under voltages, as well as frequency deviations and may require secondary spinning reserves to be brought on line. Day-ahead irradiance forecasts are useful in predicting surplus/deficit generation capacity that can then be augmented or sold in the day-ahead electricity market.