A scalable demand and renewable energy forecasting system for distribution grids

Managing a reliable, renewable, and affordable power grid is a challenging task because the mix of power generating and consuming devices connected to the network continues to change. Improved forecasts help network operators respond to these changes and make data-driven decisions regarding, e.g., demand response and market operations. A system producing short-term energy forecasts of demand and renewable generation at multiple aggregation levels across the service territory of a distribution utility is presented. The system automates the process of ingesting and curating large amounts of data from multiple sources, such as high-resolution weather forecasts, SCADA (supervisory control and data acquisition) data and, smart meter data. This results in a richer and higher-quality data set which improves accuracy for residual demand forecasts because it enables the use of real-time data and the creation of detailed models for solar energy generation. Results of an operational deployment of the system on the service territory covered by the largest electric distribution utility in Vermont, Green Mountain Power, are presented.