The overall objective of this project was to apply the Building Energy Asset Management (BEAM) software to manage the performance of building energy assets and optimize their availability, reliability, and energy consumption while fulfilling the building’s functional objectives that support its missions. To validate the BEAM performance objectives, the project team set out to answer the following key questions:

  1. Can BEAM building continuous condition monitoring detect building asset faults and performance degradation and, thereby, potentially reduce or eliminate waste due to operation of faulty assets?
  2. Can BEAM-derived optimal maintenance policies show significant savings of lifecycle cost compared to current practices at the demonstration site?
  3.  Can BEAM’s calculation of potential business penalties incurred in the event of failure and stoppage of energy assets provide actionable insights that inform optimal maintenance decision-making?

Technology Description

BEAM is an innovative software technology developed collaboratively by Siemens Corporation, Corporate Technology, and Rutgers University. BEAM includes a 5-step management workflow process: Synthesize, Measure, Analyze, Plan, and Act. This process is performed using two main modules of the BEAM software suites: the BEAM Configuration Tool and the BEAM Runtime Tool.

The BEAM technology combines continuous condition monitoring with analytic tools for asset management. Integration of such software with existing building automation systems generates real-time data for building energy asset conditions, thereby enabling predictive maintenance and repair actions prior to complaint occurrence. In addition, embedded modeling and simulation engines empower building operators to evaluate and improve existing asset maintenance policies and decision making, specifically promoting maintenance and investment in critical “energy assets” to assure accomplishment of missions while minimizing overall lifecycle costs.

Demonstration Results

The BEAM technology was demonstrated at the U.S. Air Force Academy in Colorado Springs, Colorado, from May 2012 to December 2013. During the 18-month project, the project team customized the BEAM tools for military installations to support a “Stand Alone” mode in which BEAM software does not connect directly to a Building Automation System (BAS), thereby avoiding the necessity of network security certification. In the Stand Alone mode, BEAM receives batch BAS data collected periodically by the operator of the BAS. The project team then applied BEAM to one of the campus buildings, Arnold Hall, for performance validation and cost analysis.

The project team conducted quantitative evaluation of the existing operations and maintenance (O&M) policies for Arnold Hall energy assets using the BEAM Runtime software to predict energy cost, maintenance cost, and business penalty cost associated with these policies for periods of 2, 5, and 15 years. The results from the analysis were used to establish a baseline for BEAM performance validation. The project team then worked with the site to use BEAM software to define the optimal asset maintenance policy for each energy asset for periods of 2, 5, and 15 years. BEAM maintenance planning demonstrated reasonable levels of improvement in energy savings over the baseline and significant improvements in asset failure prevention, maintenance cost reduction, availability of assets, and avoidance of penalties due to business loss. The facility mangers also showed reasonable levels of satisfaction with BEAM ease of use and user interface. Overall, the BEAM-derived optimal maintenance policy showed significant improvement in lifecycle cost saving over current practices at the demonstration site. The demonstration site facility manager, building operator, and control engineers all expressed their willingness to use the BEAM tools to monitor asset conditions and conduct energy asset management in performing their daily jobs.

For total building energy consumption, the results show that use of the BEAM technology can lead to energy savings beyond the target of 5% set for this demonstration. The simulation results for the 2, 5, and 15-Year time horizons demonstrate respectively 8.03%, 8.01%, and 6.61% reduction in energy usage relative to the baseline of annual energy consumption at the demonstration site. For building systems maintenance, simulation results for the 2, 5, and 15-Year time horizons indicate respectively 76.81%, 88.30%, and 88.09% reduction in reliability events relative to the baseline. These results exceeded the success criteria target of a 20% reduction. Building system economic results for the 2, 5, and 15-Year time horizons show respectively 10%, 11%, and 17% reduction in Energy and Maintenance combined costs relative to the baseline, while the penalty cost is showing respectively 96.52%, 99.16%, and 98.37% reductions. The cost savings for Energy and Maintenance over the 15-year period are in the range of the target set for this demonstration, which is 15% savings.

Implementation Issues

Maintenance with BEAM planning will not result in downtime when the asset is needed. The building asset availability and reliability success criteria was set too high for the following reasons: (1) if an asset in unavailable for more than 20% of the time, the asset will probably be replaced; therefore, a 20% increase in availability is not reasonably achievable; and (2) assets for this particular building are already available more than 99% of the time; therefore, any improvement will be miniscule. Since in the case of BEAM, maintenance is pre-planned, it can be done during off periods where the asset is not needed or can be taken offline with minimal negative impact on the user.

  • Predictive Maintenance,

  • Fault Detection and Diagnostics (FDD),

  • Benchmarking,

  • Decision Support Tool,

  • ESCO,

  • Optimization,