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The most common method of benchmarking energy use in buildings is to compare the energy use of the building under consideration with the energy use of a population of like buildings. Usually there is some empirical compensation for features and factors that affect energy use such as the size of the building and the weather conditions. Two fundamental limitations of this approach are: 1) only similar kinds of buildings can be compared, and 2) the entire population may be inefficient, which would cause many inefficient buildings to be rated as efficient. The first limitation is important when benchmarking laboratory buildings because there is no public database of energy use and building features that can be used to construct empirical benchmarks for laboratories. The second limitation is also important because there is evidence that energyconsuming processes in laboratory buildings, especially HVAC systems, are inefficient because of highly conservative design practices.This paper describes a benchmarking method that is fundamentally different than the method described above. The principle of the new method is to construct a benchmark that represents the minimum amount of energy required to meet a set of basic functional requirements of the building. These requirements include code-compliant environmental controls, adequate lighting, etc. The benchmark is computed based on idealized models of equipment and system performance. Using idealized models produces a benchmark that is independent of design and easy to compute. Once the benchmark has been computed for a single building, an effectiveness metric is computed by dividing the model-based benchmark by the actual consumption. This metric, or its inverse, can be compared with the metrics of other buildings. Since functional requirements have been incorporated into the benchmark, it is possible to compare the performance of dissimilar buildings, or buildings that have rare or unique functional requirements.The performance of the model-based benchmarking method was compared with two alternative methods based on the ability to predict actual energy use. Using building energy data from the UC Berkeley campus, it was shown that the model-based benchmarking method was more accurate when a combination of laboratory and non-laboratory buildings was analyzed.

Data center thermal management challenges have been steadily increasing over the past few years due to rack level power density increases resulting from system level compaction. These challenges have been compounded by antiquated environmental control strategies designed for low power density installations and for the worst-case heat dissipation rates in the computer systems. Current data center environmental control strategies are not energy efficient when applied to the highly dynamic, high power density data centers of the future. Current techniques control the computer room air conditioning units (CRACs) based on the return air temperature of the air -typically set near 20 C. Blowers within the CRACs are normally operated at maximum flow rate throughout the operation of the data center unless they are equipped with non-standard variable frequency drives. At this setting the blowers typically provide significantly more airflow than is required by the equipment racks to prevent recirculation and the subsequent formation of hot spots. This strategy tends to be overly conservative and inefficient. As an example air entering a given system housed in a rack undergoes a temperature rise of 15 C due to the heat added by the system. The return air control strategy strives to keep the entire room at a fixed temperature. Therefore in a typical data center the CRAC supply temperature, and hence the air entering the racks, is 13-15 C and the CRAC return is 20-22 C. At these settings the CRACs can consume almost as much energy as the computer equipment they are cooling [1] [2]. Experiments conducted by the authors using these CRAC settings show that nearly 0.7 W is consumed by the environmental control system for every 1 W of heat dissipated by the computer equipment in the authors' experimental facility indicating that the energy efficiency of standard data center environmental control systems is poor. This study examines several opportunities for improving thermal management and energy performance of data centers with automatic control. Experimental results are presented that demonstrate how simple, modular control strategies can be implemented. Furthermore, experimental data is presented that show it is possible to improve the energy performance of a data center by up to 70% over current standards while maintaining proper thermal management conditions.

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