The book contains the data required to measure and manage energy consumption in residential buildings. This book describes energy information in detail so that any homeowner can measure energy use on a continuing basis, make decisions regarding how to conserve energy, implement improvements, then monitor the results of those improvements. In the past, it has been difficult to collect residential energy consumption data in real-time. This book helps overcome that challenge by teaching readers how to use self-installed data collection devices that monitor consumption of circuits or appliances, along with freely available information to benchmark against other homes in the area. It demonstrates how information derived from many sources, such as the kWh listed on an electric bill, can be combined into simple calculations that illuminate how well conservation efforts are working from day to day, month to month, or year to year. Homeowners have ultimate control over the decision making process required to realize energy savings. This book simplifies the tasks of collecting, calculating and reporting energy information to the homeowner, putting the power to conserve energy in the hands of the people who will ultimately benefit the most directly from conservation efforts.

America's economy and lifestyles have been shaped by the low prices and availability of energy. In the last decade, however, the prices of oil, natural gas, and coal have increased dramatically, leaving consumers and the industrial and service sectors looking for ways to reduce energy use. To achieve greater energy efficiency, we need technology, more informed consumers and producers, and investments in more energy-efficient industrial processes, businesses, residences, and transportation. As part of the America's Energy Future project, Real Prospects for Energy Efficiency in the United States examines the potential for reducing energy demand through improving efficiency by using existing technologies, technologies developed but not yet utilized widely, and prospective technologies. The book evaluates technologies based on their estimated times to initial commercial deployment, and provides an analysis of costs, barriers, and research needs. This quantitative characterization of technologies will guide policy makers toward planning the future of energy use in America. This book will also have much to offer to industry leaders, investors, environmentalists, and others looking for a practical diagnosis of energy efficiency possibilities.

This analysis examines the relationship between energy demand and residential building attributes, demographic characteristics, and behavioral variables using the U.S. Department of Energy's Residential Energy Consumption Survey 2005 microdata. This study investigates the applicability of the smooth backfitting estimator to statistical analysis of residential energy consumption via nonparametric regression. The methodology utilized in the study extends nonparametric additive regression via local linear smooth backfitting to categorical variables. The conventional methods used for analyzing residential energy consumption are econometric modeling and engineering simulations. This study suggests an econometric approach that can be utilized in combination with simulation results. A common weakness of previously used econometric models is a very high likelihood that any suggested parametric relationships will be misspecified. Nonparametric modeling does not have this drawback. Its flexibility allows for uncovering more complex relationships between energy use and the explanatory variables than can possibly be achieved by parametric models. Traditionally, building simulation models overestimated the effects of energy efficiency measures when compared to actual "as-built" observed savings. While focusing on technical efficiency, they do not account for behavioral or market effects. The magnitude of behavioral or market effects may have a substantial influence on the final energy savings resulting from implementation of various energy conservation measures and programs. Moreover, variability in behavioral aspects and user characteristics appears to have a significant impact on total energy consumption. Inaccurate estimates of energy consumption and potential savings also impact investment decisions. The existing modeling literature, whether it relies on parametric specifications or engineering simulation, does not accommodate inclusion of a behavioral component. This study attempts to bridge that gap by analyzing behavioral data and investigate the applicability of additive nonparametric regression to this task. This study evaluates the impact of 31 regressors on residential natural gas usage. The regressors include weather, economic variables, demographic and behavioral characteristics, and building attributes related to energy use. In general, most of the regression results were in line with previous engineering and economic studies in this area. There were, however, some counterintuitive results, particularly with regard to thermostat controls and behaviors. There are a number of possible reasons for these counterintuitive results including the inability to control for regional climate variability due to the data sanitization (to prevent identification of respondents), inaccurate data caused by to self-reporting, and the fact that not all relevant behavioral variables were included in the data set, so we were not able to control for them in the study. The results of this analysis could be used as an in-sample prediction for approximating energy demand of a residential building whose characteristics are described by the regressors in this analysis, but a certain combination of their particular values does not exist in the real world. In addition, this study has potential applications for benefit-cost analysis of residential upgrades and retrofits under a fixed budget, because the results of this study contain information on how natural gas consumption might change once a particular characteristic or attribute is altered. Finally, the results of this study can help establish a relationship between natural gas consumption and changes in behavior of occupants.

Energy used in residential buildings is a major energy use in Canada (21% of all energy) and presumably in most areas in the Northern hemisphere. A substantial fraction of this energy (24%) is used in multi-residential buildings. However, very little attention has been given to examining the patterns of energy and water consumption, let alone how they can be improved. This report suggests that significant savings and improvements in the efficiency of multi-residential systems are achievable, and this could go a long way toward reducing energy usage and fulfilling Canadian Kyoto obligations. Watershed Technologies Inc. and OZZ Energy Solutions Inc. have been monitoring energy and water usage in multi-residential buildings in the Toronto area since 1996. The present analysis looks at detailed (hourly) gas and electricity loads in 34 buildings and water consumption in 21 buildings over a two-year period from May 2001 to April 2003. An earlier report by OZZ documented the different physical features of these buildings and presented an overall summary and a preliminary analysis of gas, electricity and water consumption data along with daily and seasonal summaries. A more detailed analysis is offered in the present report. A new model is developed which describes the building's energy consumption for cooling and heating more effectively than the traditional degree-day model. The new model quantifies not only the changes in energy load per unit change in temperature but also its effective thermal capacity, the heating system overhead and system response to other weather factors, namely wind, relative humidity and solar access. More than two fold differences in the energy per unit area or water used per apartment unit among the buildings are observed. A study of seasonal and daily variability suggests that at least a part of these differences in the building energy efficiency can be attributed to poor or complete lack of energy management in many of the buildings. The data also suggests that in-suite electric heating is 2.5 times more efficient than central gas heating systems in multi-residential buildings. The same appears true of in-suite air conditioning, when compared to centralized cooling systems.