Interest in rating the real-life energy performance of buildings has increased in recent years, and the real life efficiency performance rating of buildings is important for any sustainable energy future. The ability to compare the energy performance of one commercial building with that of another is important for determination of national and international energy efficiency because comparison allows meaningful measurements of potential relative improvements. This ability also may allow different classes of buildings to be analyzed together (e.g., offices and hospitals).
The European Union has been examining requirements for improving the energy efficiency of residential and commercial buildings, because a large potential improvement in energy performance has been determined to exist. Among the requirements examined is the establishment of a general framework for a common methodology for calculating the integrated energy performance of buildings. The United States Environmental Protection Agency (EPA) has established an empirical energy performance rating system for some commercial building types, the Energy Star rating system, whereby a normalized energy performance rating scale is developed. The energy use of a specific building is normalized based on the factors in the method, and the normalized energy is compared to the performance rating scale. Buildings scoring in the top 25% on the scale have energy performance level that makes them eligible for consideration of award of an Energy Star label.
Commercial building energy performance, or energy efficiency, is often measured to a certain degree by building energy experts, and even many non experts, without using any real standards. To judge how well a specific building is doing, however, energy performance and energy measurement should involve a comparison of building energy use to some type of standard, which in the past has typically been the energy use of other, similar buildings. The challenge over the years has been to determine a true standard for comparison and to determine what a ‘‘similar’’ building is. Because the historical methods of comparison had known limitations, building energy experts developed their own sense of what constitutes an energy-efficient building. This expert sense is based on experience with similar buildings, the types of activities within specific buildings, and any history of achieving reductions in energy use in comparable buildings. This expert knowledge has gaps and is not easily transferable, because it is usually based on several years of experience concerning expected patterns of energy use for different buildings and impacts of schedules, uses, geographic location, and system configurations. This expert knowledge is used to ‘‘adjust’’ the measure of the performance of a commercial building to provide a more informed measure of performance. However, this knowledge is ad hoc, with multiple practitioners probably arriving at differing assessments of the same building. In the end, the result is essentially a subjective expert opinion, albeit possibly a very good one, but also possibly not.
Five generic classes of building energy data analysis methods have been identified as useful in measuring the energy performance of commercial buildings: 1. Annual total energy and energy intensity comparisons. 2. Linear regression and end-use component models. 3. Multiple regression models. 4. Building simulation programs. 5. Dynamic thermal performance models.
All of these analytical approaches can be used to develop building energy performance measurement methods, but the most effective current approach in use today, based on results achieved, involves the third approach, multiple regression models. When calculating commercial building energy performance using multiple regression models, the effects of many factors can be modeled, potentially factoring out influences such as the number of people in a building or occupant density. The Energy Star rating system develops its performance rating scales using multiple regression models.
The limitations of the other methods include their inability to cover wide ranges of buildings without an inordinate amount of data. Some of the other methods require large volumes of data to develop empirical results. In the following discussion of performance rating systems, both simple annual total energy intensity comparisons (Method 1 above) and multiple regression method information will be addressed; the first is useful both as an example and as a well-understood quantity, whereas multiple regression analysis illustrates the current state-of-the art methodology.
