Forcasting evolving time series of energy demand and supply

  • Authors:

  • Lars Dannecker;Matthias Böhm;Wolfgang Lehner;Gregor Hackenbroich

  • Affiliations:

  • SAP Research Dresden, Dresden, Germany;Technische Universität Dresden, Database Technology Group, Dresden, Germany;Technische Universität Dresden, Database Technology Group, Dresden, Germany;SAP Research Dresden, Dresden, Germany

  • Venue:
  • ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
  • Year:
  • 2011

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Abstract

Real-time balancing of energy demand and supply requires accurate and efficient forecasting in order to take future consumption and production into account. These balancing capabilities are reasoned by emerging energy market developments, which also pose new challenges to forecasting in the energy domain not addressed so far: First, real-time balancing requires accurate forecasts at any point in time. Second, the hierarchical market organization motivates forecasting in a distributed system environment. In this paper, we present an approach that adapts forecasting to the hierarchical organization of today's energy markets. Furthermore, we introduce a forecasting framework, which allows efficient forecasting and forecast model maintenance of time series that evolve due to continuous streams of measurements. This framework includes model evaluation and adaptation techniques that enhance the model maintenance process by exploiting context knowledge from previous model adaptations. With this approach (1) more accurate forecasts can be produced within the same time budget, or (2) forecasts with similar accuracy can be produced in less time.