The United Technologies Research Center (UTRC) demonstrated 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 aBEMS is targeted at commercial buildings that use building energy management systems. The demonstration was conducted in a drill hall/office building and a large barracks facility at Naval Station Great Lakes, Illinois. Specific technical objectives were to demonstrate: (1) 10% building energy savings by providing 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.

Technology Description

The aBEMS assimilated data from multiple sources—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 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. This detailed building model is required for the existing technology (demonstrated in ESTCP project EW-200929) and represents a practical barrier to a broad scalable application. The facility Building Management System (BMS) was extended to incorporate the energy diagnostics and analysis algorithms, producing systematic identification of alternative, energy-efficient heating, ventilation, and air conditioning (HVAC) operation strategies. The scalability of the solution has also been demonstrated by applying (1) load estimation techniques and ROMs for the building and HVAC systems, reducing the need for constructing specific, detailed models for each building, and (2) probabilistic graphic models for energy diagnostics, as the graphic structure does not have to be learned for similar equipment and systems every time.

Demonstration Results

The overall performance evaluation for the aBEMS is summarized as follows:

  • Greater than 20% savings was demonstrated for building energy consumption by improving facility manager decision support to diagnose energy faults and prioritize alternative, energy-efficient operation strategies.
  • A ROM library for building envelope and HVAC equipment was developed, validated, and tested using demonstration buildings at Naval Station Great Lakes.
  • A prototype toolkit to seamlessly and automatically transfer a Building Information Model (BIM) to a Building Energy Model (BEM) was developed and tested. This dramatically reduced the time to create a BEM (50% time reduction).
  • A tool chain for a scalable probabilistic graphical model-based energy diagnostics was established, tested, and demonstrated. Greater than 15% energy savings was achieved by correcting air handling unit economizer faults. Greater than 95% of faults identified were classified correctly.
  • A ROM-based HVAC operation sensitivity study was implemented and greater than 20% energy savings was identified by precooling/preheating the building, resetting chilled water supply temperature set points, resetting zone temperature set points, and optimizing outside airflow rate in the demonstration buildings.
  • A visualization dashboard for building performance energy monitoring, HVAC operation strategies prioritization, and energy diagnostics was developed and deployed in the demonstration buildings. This dashboard provides an effective way for facility managers to perform building performance decision-making.

Faults and issues identified by the aBEMS were valued by the facility team because the tool provided additional visibility into the building operation that was not provided by the existing traditional BMS. This additional information allowed the facility team to identify previously unknown operational issues and prioritize their maintenance actions.

Implementation Issues

The primary concern for future implementation of the aBEMS is the instrumentation cost. The largest components are the equipment and installation costs related to submetering and the on-site weather station. It is possible and reasonable to eliminate the on-site weather station by using weather data from the Internet or an existing weather station on the base. Additional research efforts are needed to establish cost-effective submetering.

The UTRC stage-gated technology and product development processes have been applied to begin transitioning the technology into a commercial product. The aBEMS will be part of a new BMS product or will be applied as an overlay on an existing BMS.