Abstract

Due to the climate change the need of sustainable technological progress is growing. Renewable energies undergo a significant upturn, but they come with the price of nearly uncontrollable and hard-to-predict fluctuating energy generation. This forces a paradigm change in handling the energy resources in the society. Especially in the industry, there will appear a need to manage energy in the sense of planning and controlling power consumption. If a company wants to make use of its flexibility in energy consumption - for example by managing a local virtual power plant - there is a need to make decisions in the required pace. Thus, a real-time decision-making process needs an adequate IT-infrastructure and algorithms tailored to the specific use case. This paper gives an overview of the necessary components to realize real-time analytics for industrial energy management. The concepts are expatiated on a scalable real-time computing architecture and a mathematical approach for short term prediction of energy consumption that are included in a Business Intelligence Tool for decision support using only open source components.

Authors: Marcel Graus, Philipp Niemietz, Michaela Hiller, Mohammad Touhidur Rahman

Published in: World Congress on Sustainable Technologies (WCST-2017)

  • Date of Conference: 11-14 December 2017
  • DOI: 10.20533/WCST.2017.0004
  • ISBN: 978-1-908320-78-0
  • Conference Location: University of Cambridge, UK