This paper presents the results of 11 after-hours walk-throughs of offices in the San Francisco CA and Washington D.C. areas. The primary purpose of these walk-throughs was to collect data on turn-off rates for various types of office equipment (computers, monitors, printers, fax machines, copiers, and multifunction products). Each piece of equipment observed was recorded and its power status noted (e.g. on, off, low power). Whenever possible, we also recorded whether power management was enabled on the equipment. The floor area audited was recorded as well, which allowed us to calculate equipment densities.We found that only 44 percent of computers, 32 percent of monitors, and 25 percent of printers were turned off at night. Based on our observations we estimate success rates of 56 percent for monitor power management and 96 percent for enabling of power management on printers.

Local environmental groups, although acknowledged increasingly since the mid-1980s

No abstract

A program called "Innovative Billing" has been developed to provide individualized energy information for a mass audience-the entire residential customer base of an electric or gas utility. Customers receive a graph on the bill that compares that customer's consumption with other similar customers for the same month. The program aims to stimulate customers to make efficiency improvements. To group as many as several million customers into small "comparison groups", an automated method must be developed drawing solely from the data available to the utility. This paper develops and applies methods to compare the quality of resulting comparison groups.A data base of 114,000 customers from a utility billing system was used to evaluate Innovative Billing comparison groups, comparing four alternative criteria: house characteristics (floor area, housing type and heating fuel); street; meter read route; and billing cycle. Also, customers were interviewed to see what forms of comparison graphs made most sense and led to fewest errors of interpretation. We find that good quality comparison groups result from using street name, meter book, or multiple house characteristics. Other criteria we tested, such as entire cycle, entire meter book, or single house characteristics such as floor area, resulted in poor quality comparison groups. This analysis provides a basis for choosing comparison groups based on extensive user testing and statistical analysis. The result is a practical set of guidelines that can be used to implement realistic, inexpensive innovative billing for the entire customer base of an electric or gas utility.