Scalable Deployment of Advanced Building Energy Management Systems

• Main Authors: Adetola, Veronica, Ahuja, Sunil, Bailey, Trevor, Dong, Bing, Khawaja, Taimoor, Luo, Dong, O Neill, Zheng, Shashanka, Madhusudana
• Format: Report
• Language: English
• Subjects: ABEMS(ADVANCED BUILDING ENERGY MANAGEMENT SYSTEMS); Air Condition, Heating, Lighting & Ventilating; BEM(BEM BUILDING ENERGY MODELS); BMS(BUILDING MANAGEMENT SYSTEMS); BUILDINGS; COST ANALYSIS; EFFICIENCY; ENERGY MANAGEMENT; HVAC(HEATING VENTILATION AND AIR CONDITIONING); Non-electrical Energy Conversion; PERFORMANCE(ENGINEERING); ROM(REDUCED-ORDER MODELS)

The United Technologies Research Center (UTRC)1, with the sponsorship from DoD ESTCP program, has performed a demonstration of an advanced Building Energy Management System (aBEMS) that 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: 1) to demonstrate 10% building energy savings by providing the facility engineers with actionable energy fault information to identify and correct poor system performance, and 2) to demonstrate 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 advanced building energy management system 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 facilities 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.