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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-topredict 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 decisionmaking 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 and pattern recognition that are included in a Business Intelligence Tool for decision support using only open source components.

Data Analytics for Advanced Energy Management in Real-Time based on Open Source Technologies