Abstract: The paper describes work to enable improved energy performance of existing and new retail stores belonging to a national chain and thereby also identify measures and tools that would improve the performance of ?big box? stores generally. A detailed energy simulation model of a standard store design was developed and used to:

- demonstrate the benefits of benchmarking the energy performance of retail stores of relatively standard design using baselines derived from simulation,

- identify cost-effective improvements in the efficiency of components to be incorporated in the next design cycle,

- use simulation to identify potential control strategy improvements that could be adopted in all stores, improving operational efficiency.

The core enabling task of the project was to develop an energy model of the current standard design using the EnergyPlus simulation program. For the purpose of verification of the model against actual utility bills, the model was reconfigured to represent twelve existing stores (seven relatively new stores and five older stores) in different US climates and simulations were performed using weather data obtained from the National Weather Service. The results of this exercise, which showed generally good agreement between predicted and measured total energy use, suggest that dynamic benchmarking based on energy simulation would be an effective tool for identifying operational problems that affect whole building energy use.

The models of the seven newer stores were then configured with manufacturers? performance data for the equipment specified in the current design and used to assess the energy and cost benefits of increasing the efficiency of selected HVAC, lighting and envelope components. The greatest potential for cost-effective energy savings appears to be a substantial increase in the efficiency of the blowers in the roof top units and improvements in the efficiency of the lighting. The energy benefits of economizers on the roof-top units were analyzed and found to be very sensitive to the operation of the exhaust fans used to control building pressurization.

Abstract: This report summarizes and compares two currently available commercial building energy-benchmarking tools. One tool is the U.S. Environmental Protection Agency"s Energy Star National Energy Performance Rating System, which is a national regression-based benchmarking model (referred to in this report as Energy Star). The second is Lawrence Berkeley National Laboratory"s Cal-Arch, which is a California-based distributional model (referred to as Cal-Arch). Prior to the time Cal-Arch was developed in 2002, there were several other benchmarking tools available to California consumers but none that were based solely on California data. The Energy Star and Cal-Arch benchmarking tools both provide California with unique and useful methods to benchmark the energy performance of California"s buildings. Rather than determine which model is better, the purpose of this report is to understand and compare the underlying data, information systems, assumptions, and outcomes of each model.

Abstract: Building energy benchmarking is the comparison of whole-building energy use relative to a set of similar buildings. It provides a useful starting point for individual energy audits and for targeting buildings for energy-saving measures in multiple-site audits. Benchmarking is of interest and practical use to a number of groups. Energy service companies and performance contractors communicate energy savings potential with typical and best-practice benchmarks while control companies and utilities can provide direct tracking of energy use and combine data from multiple buildings. Benchmarking is also useful in the design stage of a new building or retrofit to determine if a design is relatively efficient. Energy managers and building owners have an ongoing interest in comparing energy performance to others. Large corporations, schools, and government agencies with numerous facilities also use benchmarking methods to compare their buildings to each other.

Abstract: Building energy benchmarking is a useful starting point for commercial building owners and operators to target energy savings opportunities. There are a number of tools and methods for benchmarking energy use. Benchmarking based on regional data can provides more relevant information for California buildings than national tools such as Energy Star.

This paper discusses issues related to benchmarking commercial building energy use and the development of Cal-Arch, a building energy benchmarking database for California. Currently Cal-Arch uses existing survey data from California"s Commercial End Use Survey (CEUS), a largely underutilized wealth of information collected by California"s major utilities. DOE"s Commercial Building Energy Consumption Survey (CBECS) is used by a similar tool, Arch, and by a number of other benchmarking tools. Future versions of Arch/Cal-Arch will utilize additional data sources including modeled data and individual buildings to expand the database.

Abstract: The efficiencies of methods employed in solution of building simulation models are considered and compared by means of benchmark testing. Direct comparisons between the Simulation Problem Analysis and Research Kernel (SPARK) and the HVACSIM programs are presented, as are results for SPARK versus conventional and sparse matrix methods. An indirect comparison between SPARK and the IDA program is carried out by solving one of the benchmark test suite problems using the sparse methods employed in that program. The test suite consisted of two problems chosen to span the range of expected performance advantage. SPARK execution times versus problem size are compared to those obtained with conventional and sparse matrix implementations of these problems. Then, to see if the results of these limiting cases extend to actual problems in building simulation, a detailed control system for a heating, ventilating and air conditioning (HVAC) system is simulated with and without the use of SPARK cut set reduction. Execution times for the reduced and non-reduced SPARK models are compared with those for an HVACSIM model of the same system. Results show that the graph-theoretic techniques employed in SPARK offer significant speed advantages over the other methods for significantly reducible problems, and that by using sparse methods in combination with graph theoretic methods even problem portions with little reduction potential can be solved efficiently.