Engineers from the Australian National University (ANU) and Fraunhofer Institute for Solar Energy Systems ISE have created a data set from 1287 residential installations of PV systems across Australia and have made this data set freely available. The data set could provide solar researchers with the means to perform the spatial analyses needed to manage solar integration into the power grid of a city in a controlled way. By subscribing to a public website (pvoutput.org), the engineers were able to access raw PV power data from the automatic logging of PV systems’ electrical converters. This data was extracted from the website and put into a database. The engineers then collected details about each PV system, such as its size and efficiency. Using this metadata alongside satellite images, the engineers performed rigorous quality controls and trained tuning algorithms on the data set to find any possible system-like losses, such as shading, and remove them from the data. “Not just deleting them, but scaling it back up to make it representative,” said ANU research fellow Jamie Bright. The data set contains six months’ worth of measurements from three different cities in Australia. Map of Australia highlighting Perth, Western Australia; Adelaide, South Australia; and Canberra, Australian Capital Territory. Each red marker represents the location of the PV systems within the database. Longitude and latitude are reported in degrees. Courtesy of Australian National University. Researchers are encouraged to leverage this spatiotemporal data set of distributed PV power data, which is free to download and is available in raw, quality-controlled, and tuned formats. Each PV system is accompanied by individual metadata including geolocation, user-reported metadata, and simulated parameterization. Data from PV systems distributed across a city is necessary for solar researchers to understand how a solar energy source can be integrated into the power grid without disrupting the reliable flow of electricity. To maintain the recommended voltage for appliances and ensure electricity supply, grid operators need to react and plan for solar power fluctuations. “Now we’ve proved with this data set that live reporting PV systems can significantly improve forecasting — solar forecasting companies are deploying our approach to a real operational industrial forecasting system,” Bright said. The research was published in the Journal of Renewable and Sustainable Energy (https://doi.org/10.1063/1.5094059).