Data Management System for Energy Analytics and its Application to Forecasting

The effective management of a power grid with an increas-ing share of (distributed) renewables and more and more available data, e.g., coming from smart meters, heavily re-lies on advanced data analytics such as demand and supply forecasting. In this context, data management is one ma-jor challenge in electric grids. Large amount of data from multiple heterogeneous sources require transformations, e.g., spatio-temporal alignment or anomaly detection, to serve data analytics tasks and are often applied on different views of the data, e.g., on state, substation or feeder level. In this paper, the progress on the development of an en-ergy data management systems for the electricity grid is pre-sented. The design of the system was inspired by the real-world use case of forecasting short-term energy demand in Vermont, using data from a combination of SCADA, smart meters and weather forecasting services. A general data model addressing the aforementioned challenges and aimed at supporting advanced data analytics is introduced. The proposed data model views a time series as an abstract con-cept that might represent raw measurements or arbitrary operations. The benefits of the system is demonstrated for the design and live update energy demand forecasts. 1