Data management system for energy analytics and its application to forecasting

Abstract

The effective management of a power grid with an increasing share of (distributed) renewables and more and more available data, e.g., coming from smart meters, heavily relies on advanced data analytics such as demand and supply forecasting. In this context, data management is one major 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 energy data management systems for the electricity grid is presented. 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 concept that might represent raw measurements or arbitrary operations. The benefits of the system is demonstrated for the design and live update energy demand forecasts.