Office equipment is expected to be the fastest-growing segment of commercial energy use over the next 20 years, yet many aspects of office equipment energy use are poorly understood. User behavior, such as turning off devices at night or enabling power management, influences energy use to a great extent. The computing environment also plays a role both in influencing user behavior and in the success of power management. Information about turn-off rates and power management rates for office equipment was collected through a series of after-hours audits in commercial buildings. Sixteen businesses were recruited, including offices (small, medium and large offices in a variety of industries), schools, and medical buildings in California, Georgia, and Pennsylvania. The types and power states of office equipment found in these buildings were recorded and analyzed. This article presents these data for computers, monitors, printers, copiers, fax machines, scanners and multi-function devices. These data can be used to improve estimates of both energy consumption for these devices and savings from energy conservation efforts.

This research was conducted in support of two branches of the EPA ENERGY STAR program, whose overall goal is to reduce, through voluntary market-based means, the amount of carbon dioxide emitted in the U.S. The primary objective was to collect data for the ENERGY STAR Office Equipment program on the after-hours power state of computers, monitors, printers, copiers, scanners, fax machines, and multi-function devices. We also collected data for the ENERGY STAR Commercial Buildings branch on the types and amounts of "miscellaneous" plug-load equipment, a significant and growing end use that is not usually accounted for by building energy managers. This data set is the first of its kind that we know of, and is an important first step in characterizing miscellaneous plug loads in commercial buildings. The main purpose of this study is to supplement and update previous data we collected on the extent to which electronic office equipment is turned off or automatically enters a low power state when not in active use. In addition, it provides data on numbers and types of office equipment, and helps identify trends in office equipment usage patterns. These data improve our estimates of typical unit energy consumption and savings for each equipment type, and enables the ENERGY STAR Office Equipment program to focus future effort on products with the highest energy savings potential. This study expands our previous sample of office buildings in California and Washington DC to include education and health care facilities, and buildings in other states. We report data from twelve commercial buildings in California, Georgia, and Pennsylvania: two health care buildings, two large offices (> 500 employees each), three medium offices (50-500 employees), four education buildings, and one "small office" that is actually an aggregate of five small businesses. Two buildings are in the San Francisco Bay area of California, five are in Pittsburgh, Pennsylvania, and five are in Atlanta, Georgia.

The Home Energy Saver (HES, http://HomeEnergySaver.lbl.gov) is an interactive web site designed to help residential consumers make decisions about energy use in their homes. This report describes the underlying methods and data for estimating energy consumption. Using engineering models, the site estimates energy consumption for six major categories (end uses); heating, cooling, water heating, major appliances, lighting, and miscellaneous equipment. The approach taken by the Home Energy Saver is to provide users with initial results based on a minimum of user input, allowing progressively greater control in specifying the characteristics of the house and energy consuming appliances. Outputs include energy consumption (by fuel and end use), energy-related emissions (carbon dioxide), energy bills (total and by fuel and end use), and energy saving recommendations. Real-world electricity tariffs are used for many locations, making the bill estimates even more accurate. Where information about the house is not available from the user, default values are used based on end-use surveys and engineering studies. An extensive body of qualitative decision-support information augments the analytical results.