Alison Williams

Lighting Controls in Commercial Buildings

Abstract Researchers have been quantifying energy savings from lighting controls in commercial buildings for more than 30 years. This study provides a meta-analysis of lighting energy savings identified in the literature—240 savings estimates from 88 papers and case studies, categorized into daylighting strategies, occupancy strategies, personal tuning, and institutional tuning. Beginning with an overall average of savings estimates by control strategy, successive analytical filters are added to identify potential biases introduced to the estimates by different analytical approaches. Based on this meta-analysis, the best estimates of average lighting energy savings potential are 24 percent for occupancy, 28 percent for daylighting, 31 percent for personal tuning, 36 percent for institutional tuning, and 38 percent for multiple approaches. The results also suggest that simulations significantly overestimate (by at least 10 percent) the average savings obtainable from daylighting in actual buildings.

Surveys of Microwave Ovens in U.S. Homes

LBNL-5947E Surveys of Microwave Ovens in U.S. Homes Alison Williams, Hung-Chia (Dominique) Yang, Bereket Beraki, Louis-Benoit Desroches, Scott J. Young, Chun Chun Ni, Henry Willem, Jeffery B. Greenblatt, and Camilla Dunham Whitehead Lawrence Berkeley National Laboratory, Berkeley, California, USA Sally M. Donovan Consultant, Melbourne, Australia Environmental Energy Technologies Division December 2012 This work was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Building Technology, State, and Community Programs, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.

Estimates of Savings Achievable from Irrigation Controller

This paper performs a literature review and meta-analysis of water savings from several types of advanced irrigation controllers: rain sensors (RS), weather-based irrigation controllers (WBIC), and soil moisture sensors (SMS).The purpose of this work is to derive average water savings per controller type, based to the extent possible on all available data. After a preliminary data scrubbing, we utilized a series of analytical filters to develop our best estimate of average savings. We applied filters to remove data that might bias the sample such as data self-reported by manufacturers, data resulting from studies focusing on high-water users, or data presented in a non-comparable format such as based on total household water use instead of outdoor water use. Because the resulting number of studies was too small to be statistically significant when broken down by controller type, this paper represents a survey and synthesis of available data rather than a definitive statement regarding whether the estimated water savings are representative.