Using small data and big data: Neighborhood Energy & Data Management Integration System
Technische Universiteit Eindhoven - Eindhoven University of Technology , Technische Universiteit Eindhoven - Eindhoven University of Technology, Faculteit Bouwkunde - Department of the Built Environment, Building Physics and Services (BPS) , NWO-institutenorganisatie, CWI - Centrum Wiskunde & Informatica, Algorithms and Complexity (A&C) , NWO-institutenorganisatie, CWI - Centrum Wiskunde & Informatica, Intelligent and Autonomous Systems , Technische Universiteit Eindhoven - Eindhoven University of Technology, Faculteit Electrical Engineering - Department of Electrical Engineering, Meet- en Besturingssystemen, Meten en Regelen , Technische Universiteit Eindhoven - Eindhoven University of Technology, Faculteit Electrical Engineering - Department of Electrical Engineering, Electrical Energy Systems (EES) , NWO-institutenorganisatie , Technische Universiteit Eindhoven - Eindhoven University of Technology, Faculteit Bouwkunde - Department of the Built Environment
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Technische Universiteit Eindhoven - Eindhoven University of Technology , Technische Universiteit Eindhoven - Eindhoven University of Technology, Faculteit Bouwkunde - Department of the Built Environment, Building Physics and Services (BPS) , NWO-institutenorganisatie, CWI - Centrum Wiskunde & Informatica, Algorithms and Complexity (A&C) , NWO-institutenorganisatie, CWI - Centrum Wiskunde & Informatica, Intelligent and Autonomous Systems , Technische Universiteit Eindhoven - Eindhoven University of Technology, Faculteit Electrical Engineering - Department of Electrical Engineering, Meet- en Besturingssystemen, Meten en Regelen , Technische Universiteit Eindhoven - Eindhoven University of Technology, Faculteit Electrical Engineering - Department of Electrical Engineering, Electrical Energy Systems (EES) , NWO-institutenorganisatie , Technische Universiteit Eindhoven - Eindhoven University of Technology, Faculteit Bouwkunde - Department of the Built Environment
There is a need for more energy system integration, while the autonomy of individual systems is necessary to cope with the exploding complexity of multiple buildings and their interaction with the electricity grid. The use of Big Data in combination with deep learning techniques offers new opportunities to better predict energy consumption and decentralized production of renewable energy (for example, based on local weather data taking into account local phenomena such as urban heat islands). This combined with multi-agent systems with a cooperative approach provides decentralized control and monitoring autonomy to further reduce the complexity of energy system integration.
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