Final Report Summary - AMBI (Advanced Methods in Building Diagnostics and Maintenance)
We spend a large portion of our lives inside buildings which in turn spend huge amounts of energy to maintain our comfort. Modern buildings are equipped with complex automation systems to ensure our comfort and seemlessly respond to our presence and needs. Indeed, the building automation systems need to be properly maintained to efficiently serve the building occupants. Complexity of the building automation systems makes the maintenance challenging though. As a result, many buildings are maintained in a reactive manner only in response to occupants complaints and escalated issues.
The goal of the AMBI (Advanced methods in buildings diagnostics and maintenance) project was to enhance the management of buildings through efficient decision making about maintenance actions. This will allow facility managers to make more informed decisions to ensure their building is running optimally, and to plan any maintenance and infrastructure improvements ahead of time.
AMBI researchers developed a novel ontology-assisted procedure to setup the DSS. Automated reasoning enabled by the use of ontologies greatly reduces and simplifies the efforts needed from the engineers who configure and commission the DSS system. For example, the right set of fault-detection rules is configured based on topology of an air conditioning system and types of individual components therein.
To maximize the number and depth of insights provided to the facility managers, methods were developed to extract additional information hidden in the data. The methods combine control theory, formal verification and machine learning approaches to estimate properties of the automation system which are too expensive or even impossible to measure, such as occupancy in conditioned spaces.
When reasoning about maintenance actions, the facility managers typically deal with trade-offs: in a short term, occupant comfort is achieved at the cost of higher energy use, in a long term, system efficiency is achieved by more expensive maintenance. A methodology was developed which monetizes the individual aspects considered by facility managers and recommends the best maintenance actions and their timing with respect to the preferences of the facility manager.
The AMBI prototypes will have an impact on the next generation of building management systems and their services by improving the energy efficiency of buildings operation and increasing occupant comfort and safety. The building automation maintenance example has encouraged further fundamental research in formal verification for cyber-physical systems, resulting in publicly available tools such as FAUST2 or SCOTS.
Over the short term, project partners are using the AMBI as a basis for future work, whether it is academic research or commercial innovation. Over the longer term, AMBI will enable facility management teams to efficiently control their buildings and confidently plan just-in-time maintenance of the underlying systems, reducing both capital and operational expenses. Furthermore, the building’s occupants will experience better comfort, which will result in increasing their productivity thereby helping the businesses keep their edge.
Further information can be found on the project website: http://www.ambi-project.eu/
The goal of the AMBI (Advanced methods in buildings diagnostics and maintenance) project was to enhance the management of buildings through efficient decision making about maintenance actions. This will allow facility managers to make more informed decisions to ensure their building is running optimally, and to plan any maintenance and infrastructure improvements ahead of time.
AMBI researchers developed a novel ontology-assisted procedure to setup the DSS. Automated reasoning enabled by the use of ontologies greatly reduces and simplifies the efforts needed from the engineers who configure and commission the DSS system. For example, the right set of fault-detection rules is configured based on topology of an air conditioning system and types of individual components therein.
To maximize the number and depth of insights provided to the facility managers, methods were developed to extract additional information hidden in the data. The methods combine control theory, formal verification and machine learning approaches to estimate properties of the automation system which are too expensive or even impossible to measure, such as occupancy in conditioned spaces.
When reasoning about maintenance actions, the facility managers typically deal with trade-offs: in a short term, occupant comfort is achieved at the cost of higher energy use, in a long term, system efficiency is achieved by more expensive maintenance. A methodology was developed which monetizes the individual aspects considered by facility managers and recommends the best maintenance actions and their timing with respect to the preferences of the facility manager.
The AMBI prototypes will have an impact on the next generation of building management systems and their services by improving the energy efficiency of buildings operation and increasing occupant comfort and safety. The building automation maintenance example has encouraged further fundamental research in formal verification for cyber-physical systems, resulting in publicly available tools such as FAUST2 or SCOTS.
Over the short term, project partners are using the AMBI as a basis for future work, whether it is academic research or commercial innovation. Over the longer term, AMBI will enable facility management teams to efficiently control their buildings and confidently plan just-in-time maintenance of the underlying systems, reducing both capital and operational expenses. Furthermore, the building’s occupants will experience better comfort, which will result in increasing their productivity thereby helping the businesses keep their edge.
Further information can be found on the project website: http://www.ambi-project.eu/