Building energy systems often consume approximately 16% more energy [Mills, E. 2011. "Building Commissioning: A Golden Opportunity for Reducing Energy Costs and Greenhouse Gas Emissions in the United States." Energy Efficiency 4 (2): 145-173] than is necessary due to system deviation from the design intent. Identifying the root causes of energy waste in buildings can be challenging largely because energy flows are generally invisible. To help address this challenge, we present a model-based, real-time whole building energy diagnostics and performance monitoring system. The proposed system continuously acquires performance measurements of heating, ventilation and air-conditioning, lighting and plug equipment usage and compare these measurements in real-time to a reference EnergyPlus model that either represents the design intent for the building or has been calibrated to represent acceptable performance. A proof-of-concept demonstration in a real building is also presented. IntroductionThe total energy consumption for US commercial buildings was 17.43 quads (2003 Commercial Buildings Energy Consumption Survey database (CBECS)), approximately 18% of the total US energy consumption. The Department of Energy (DOE), the International Energy Agency, Intergovernmental Panel on Climate Change and other agencies have declared a need for commercial buildings to become 70-80% more energy efficient. Although energy-efficient building technologies are emerging, a key challenge is how to effectively maintain building energy performance over the evolving lifecycle of the building. It is well known that most buildings lose a portion of their desired and designed energy efficiency in the years after they are commissioned and recommissioned (Haves 1999; TIAX 2005;Friedman et al. 2010;Mills 2011). Achieving persistent low-energy performance is critical for realizing the energy, environmental, and economic goals expressed in the Energy Policy Act of 2005, Executive Order 13423, and the Energy Independence and Security Act of 2007. Field experience shows that energy savings of 5-30% are typically achievable simply by applying FDD (fault detection and diagnostics) and correcting the faults diagnosed in buildings (Liu, Song, and Claridge 2001;Katipamula and Brambley 2005a).Generally, FDD methods fall into three categories (Katipamula and Brambley 2005b).

Most commercial buildings do not perform as well in practice as intended by the design and their performances often deteriorate over time. Reasons include faulty construction, malfunctioning equipment, incorrectly configured control systems and inappropriate operating procedures (Haves et al., 2001, Lee et al., 2007. To address this problem, the paper presents a simulation-based whole building performance monitoring tool that allows a comparison of building actual performance and expected performance in real time. The tool continuously acquires relevant building model input variables from existing Energy Management and Control System (EMCS). It then reports expected energy consumption as simulated of EnergyPlus. The Building Control Virtual Test Bed (BCVTB) is used as the software platform to provide data linkage between the EMCS, an EnergyPlus model, and a database. This paper describes the integrated realtime simulation environment. A proof-of-concept demonstration is also presented in the paper.

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This paper presents an advanced building energy management system (aBEMS) that employs advanced methods of whole-building performance monitoring combined with statistical methods of learning and data analysis to enable identification of both gradual and discrete performance erosion and faults. This system assimilated data collected from multiple sources, including blueprints, reduced-order models (ROM) and measurements, and employed advanced statistical learning algorithms to identify patterns of anomalies. The results were presented graphically in a manner understandable to facilities managers. A demonstration of aBEMS was conducted in buildings at Naval Station Great Lakes. The facility building management systems were extended to incorporate the energy diagnostics and analysis algorithms, producing systematic identification of more efficient operation strategies. At Naval Station 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 paper concludes with recommendations for widespread aBEMS success.