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Artificial Intelligence for sustainable and energy efficient buildings

Presentation held at EPoSS and EPSI Annual Forum 2020, Digital Conference, September 29-30, 2020
: Mayer, Dirk; Enge-Rosenblatt, Olaf; Haufe, Jürgen; Wilde, Andreas; Seidel, Stephan

presentation urn:nbn:de:0011-n-6059926 (956 KByte PDF)
MD5 Fingerprint: ca72976320ca3a1d25eb477ec6882aa8
Created on: 3.11.2020

2020, 9 Folien
European Technology Platform on Smart Systems Integration (EPoSS Annual Forum) <2020, Online>
European Platform for Sport Innovation (EPSI Annual Forum) <2020, Online>
Presentation, Electronic Publication
Fraunhofer IIS, Institutsteil Entwurfsautomatisierung (EAS) ()

According to the goals of Europe’s green deal missions, the continent strives for becoming carbon neutral by 2050. Since buildings are a major contributor to the overall consumption of energy, improving their energy efficiency can be a key to a more sustainable and greener Europe. On the way towards zero-emission buildings, several challenges have to be met: In modern energy systems, several energy sources have to be orchestrated to maintain a high security of supply, to guarantee a healthy environment for the building users, and both by using a minimum of conventional energy. Further, the modern building also hosts the electric filling station for one or several electric vehicles, requiring a significant amount of electrical power. Since the components of the building energy systems are integrating more and more sensors and embedded systems, buildings are becoming networked cyber-physical energy systems – especially larger objects like airports, shopping malls or office buildings. Artificial intelligence can help to optimize the operation of these complex systems. Applications include monitoring of the building energy control system for faulty operational states, which usually are decreasing the efficiency of the energy system. Data collected on the energy system components can be used to analyse and predict the current state and enable condition-based maintenance. For a broad variety of HVAC components like simple air filters or complex combined heat and power plants, data analysis can be a basis for increasing their energy efficiency. Finally, predictive algorithms aggregate data and determine optimal control programs for the energy system with the goal to save energy. This contribution gives an overview on our recent applied research activities in several applications for intelligent building systems, including condition monitoring of the control system and other components component as well as predictive control. The experiences from implementations on actual buildings are reported and potential future applications for smart systems are discussed.