2013Unpublished venueRequires access

Scalable Deployment of Advanced Building Energy Management Systems

James E. Galvin, Trevor Bailey

Open publisher page 2 citations

Abstract

Abstract : The United Technologies Research Center (UTRC), with sponsorship from the U.S. Department of Defense (DoD) Environmental Security Technology Certification Program (ESTCP), has performed a demonstration of an advanced Building Energy Management System (aBEMS). The system employs advanced methods of whole-building performance monitoring combined with statistical learning methods and data analysis to enable identification of both gradual and discrete performance erosion and faults. The specific technical objectives of the demonstration project were to demonstrate: (1) 10% building energy savings by providing the facility engineers with actionable energy fault information to identify and correct poor system performance, and (2) an additional 10% energy savings by identifying alternative energy system operation strategies that improve building energy performance. The demonstrated technology is targeted at commercial buildings that use building energy management systems. The demonstration was conducted in a drill hall/office building (Building 7230) and a large barracks facility (Building 7113/7114) at Naval Station Great Lakes. At Great Lakes, greater than 20% savings were demonstrated for building energy consumption by improving facility manager decision support to diagnose energy faults and prioritize alternative, energy-efficient operation strategies. The aBEMS assimilated data from multiple sources including blueprints, reduced-order models (ROM) and measurements, and employed probabilistic graphical models and other advanced statistical learning algorithms to identify patterns of anomalies. The results were presented graphically in a manner understandable to a facility s manager. The system incorporated learning algorithms and simplified reduced-order simulation models to circumvent the need to manually construct and maintain a detailed building energy simulation model.

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What this paper is about

Abstract : The United Technologies Research Center (UTRC), with sponsorship from the U.S. Department of Defense (DoD) Environmental Security Technology Certification Program (ESTCP), has performed a demonstration of an advanced Building Energy Management System (aBEMS). The system employs advanced methods of whole-building performance monitoring combined with statistical learning methods and data analysis to enable identification of both gradual and discrete performance erosion and faults. The specific technical objectives of the demonstration project were to demonstrate: (1) 10% building energy savings by providing the facility engineers with actionable energy fault information to identify and correct poor system performance, and (2) an additional 10% energy savings by identifying alternative energy system operation strategies that improve building energy performance. The demonstrated technology is targeted at commercial buildings that use building energy management systems. The demonstration was conducted in a drill hall/office building (Building 7230) and a large barracks facility (Building 7113/7114) at Naval Station Great Lakes. At Great Lakes, greater than 20% savings were demonstrated for building energy consumption by improving facility manager decision support to diagnose energy faults and prioritize alternative, energy-efficient operation strategies. The aBEMS assimilated data from multiple sources including blueprints, reduced-order models (ROM) and measurements, and employed probabilistic graphical models and other advanced statistical learning algorithms to identify patterns of anomalies. The results were presented graphically in a manner understandable to a facility s manager. The system incorporated learning algorithms and simplified reduced-order simulation models to circumvent the need to manually construct and maintain a detailed building energy simulation model.

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Available abstract

Abstract : The United Technologies Research Center (UTRC), with sponsorship from the U.S. Department of Defense (DoD) Environmental Security Technology Certification Program (ESTCP), has performed a demonstration of an advanced Building Energy Management System (aBEMS). The system employs advanced methods of whole-building performance monitoring combined with statistical learning methods and data analysis to enable identification of both gradual and discrete performance erosion and faults. The specific technical objectives of the demonstration project were to demonstrate: (1) 10% building energy savings by providing the facility engineers with actionable energy fault information to identify and correct poor system performance, and (2) an additional 10% energy savings by identifying alternative energy system operation strategies that improve building energy performance. The demonstrated technology is targeted at commercial buildings that use building energy management systems. The demonstration was conducted in a drill hall/office building (Building 7230) and a large barracks facility (Building 7113/7114) at Naval Station Great Lakes. At Great Lakes, greater than 20% savings were demonstrated for building energy consumption by improving facility manager decision support to diagnose energy faults and prioritize alternative, energy-efficient operation strategies. The aBEMS assimilated data from multiple sources including blueprints, reduced-order models (ROM) and measurements, and employed probabilistic graphical models and other advanced statistical learning algorithms to identify patterns of anomalies. The results were presented graphically in a manner understandable to a facility s manager. The system incorporated learning algorithms and simplified reduced-order simulation models to circumvent the need to manually construct and maintain a detailed building energy simulation model.

Key concepts: Software deployment, Scalability, Computer science, Distributed computing, Computer architecture, Systems engineering, Software engineering, Operating system

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