Ontology-based Building Energy System Commissioning and Monitoring

Commissioning and operating building energy systems necessitates much configuration and testing work of the building monitoring and automation system. Generally suppliers and installers use singular proprietary software systems and customized monitoring databases. Once setup and in operation, there is also no guarantee that the building is energy-efficient and most of time building users themselves act as energy wasting factors due to their wrong usage of the building energy system. As a response, the presented work aims at developing an expert system that shall ease the transition between design and operation as well as provide live recommendations for a continuous commissioning of the building energy system. For that purpose, it relies on Building Information Modeling (BIM) and Semantic Web technologies. The presented approach tries then to bring a complementary added value to classical building automation and control systems (BACS) by the means of semantic modeling and knowledge reuse for semantic analysis and characterization of the building energy system and its operational conditions. For that purpose, it implements a knowledge base of energy conservation measures and potential operating errors that prescribe energy-efficiency actions and handlings to building users or a facility managers. The system consumes for a part building data gathered during its operation through monitoring system. For another part, it relies on metadata contained in initial BIM-compliant building design models. The resulting software application might be used in the future as an add-on to existing building management systems (BMS). Modern BMS are able to handle a huge amount of data that are analyzed for supervising, controlling and benchmarking buildings. BMS data are mainly gained through sensors and meters that provide information about e.g. the operational state of technical equipment, indoor temperature or energy consumption. Because of its highly time-dependent nature, this kind of information can be categorized as dynamic data about a building in contrast to static data which represent the building and its technical systems as they are i.e. as built physical entities. This latter kind of information encompasses data about the energy system components, their technical characteristics and their layout in the building. Even if numerous dynamic data are produced during building operation there is no much use of building static information created during its design. In view of that, the proposed methodology aims at closing this informational gaps between building design and operation by making reuse of initial design models serialized in IFC. The intrinsic relationships between dynamic and static data are then represented into some ontologies and analyzed by means of logical reasoning. In existing BMS those relationships are semantically poor and only contained partially in the backend data model of the BMS, like a relational database in most cases. The proposed semantic building information model is then used for interpreting building energy system behaviors and identifying best energy conservation measures. More specifically, an energy system ontology together with a risk ontology are introduced to support reuse of knowledge for optimized building operation.