Energy End-Use Forecasting
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Technology Data Characterizing Space Conditioning In Commercial Buildings: Application To End-Use Forecasting With COMMEND 4.0

Osman Sezgen, Ellen M. Franconi, Jonathan G. Koomey, Steve E. Greenberg, Asim Afzal, and Leslie Shown

Lawrence Berkeley Laboratory
Berkeley, CA 94720
December 1995

Abstract

In the United States, energy consumption is increasing most rapidly in the commercial sector. Consequently, the commercial sector is becoming an increasingly important target for state and federal energy policies and also for utility-sponsored demand side management (DSM) programs. The rapid growth in commercial-sector energy consumption also makes it important for analysts working on energy policy and DSM issues to have access to energy end-use forecasting models that include more detailed representations of energy-using technologies in the commercial sector. These new forecasting models disaggregate energy consumption not only by fuel type, end use, and building type, but also by specific technology.

The disaggregation of space conditioning end uses in terms of specific technologies is complicated by several factors. First, the number of configurations of heating, ventilating, and air conditioning (HVAC) systems and heating and cooling plants is very large. Second, the properties of the building envelope are an integral part of a building's HVAC energy consumption characteristics. Third, the characteristics of commercial buildings vary greatly by building type. The Electric Power Research Institute's (EPRI's) Commercial End-Use Planning System (COMMEND 4.0) and the associated data development presented in this report attempt to address the above complications and create a consistent forecasting framework.

Expanding end-use forecasting models so that they address individual technology options requires characterization of the present floorstock in terms of annual and peak service requirements, energy technologies used, and cost-efficiency attributes of the energy technologies that consumers may choose for new buildings and retrofits. This report describes the process by which we collected space-conditioning technology data and then mapped it into the COMMEND 4.0 input format. The data are also generally applicable to other end-use forecasting frameworks for the commercial sector.

Data were obtained from various sources including the U.S. Department of Energy, EPRI, publications of the Lawrence Berkeley National Laboratory, and cost-estimation publications used in industry. Prototype simulations using the DOE-2 building energy analysis program were used extensively to generate data related to the effectiveness of shell measures, HVAC systems, and utilization systems. Simulations were also used to characterize service demand.

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