Energy time series forecasting : efficient and accurate forecasting of evolving time Series from the energy domain

Abstract

Continuous balancing of electric power consumption and production is a fundamental prerequisite for the stability and efficiency of electricity grids. This balancing task requires accurate forecasts of future electricity demand and supply at any point in time. For this purpose, today’s energy data management systems (EDMS) typically use quantitative models—called forecast models—that already provide accurate predictions. However, recent developments in the energy domain such as real-time intra-day trading and the integration of more renewable energy sources also require more efficient forecasting calculations and a rapid provisioning of forecasting results. Furthermore, today’s EDMSs fulfill a number of different tasks, each exhibiting different requirements for the calculation of forecasts with respect to runtime and accuracy. Thus, it is necessary to flexibly adapt the forecasting process with respect to the needs of the current requests. In contrast, currently employed forecasting approach esare rather time-consuming and inflexible.One reason is the very expensive estimation of the forecast model parameters, involving a large number of simulations in a search space that increases exponential with the number of parameters.