AAAI Publications, Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence

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Adaptable Fault Identification for Smart Buildings
Anika Schumann, Jer Hayes, Pascal Pompey, Olivier Verscheure

Last modified: 2011-08-24


Malfunctioning HVAC equipment in commercial buildings wastes between 15% and 30% of energy. Many diagnosis approaches tackle this problem, but they either suffer from a lack of detailed fault information or a lack of adaptability to different buildings and equipment. Clearly, especially in the light of an ever increasing amount of sensor data that is available in heavily metered smart buildings, easily adaptable self learning in-depth diagnosis approaches are needed. This paper addresses the challenges of developing such approaches and describes the contribution artificial intelligence techniques like transfer learning, ontologies, knowledge representation or diagnosis can make in overcoming these challenges.

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