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.

Residential buildings have continuously improved in energy efficiency, partly as a consequence of the introduction of energy regulations in many countries. Although better thermal properties and systems efficiency have lowered energy consumption for space heating in recent decades, substantial differences in energy consumption in similar dwellings are still being observed. These differences in consumption are thought to be caused by differences in occupancy patterns, by quality of construction and by rebound effects.This research addresses the effect of energy performance regulations and occupant behaviour on energy consumption for space and water heating in dwellings built after the introduction of the energy performance regulations in the Netherlands. The results of this research show that improving the energy efficiency of buildings alone is not enough to decrease that energy consumption.

This book is about the optimization of the characterization of the thermal properties of building envelopes, through experimental tests and the use of artificial intelligence. It analyses periodic and stationary thermal properties using measurement approaches based on the heat flow meter method and the thermometric method. These measurements are then analysed using advanced artificial intelligence algorithms. The book is structured in four parts, beginning with a discussion of the importance of thermal properties in the energy performance of buildings. Secondly, theoretical and experimental methods for characterizing thermal properties are analysed. Then, the methodology is developed, and the characteristics and properties of the algorithms used are explored. Finally, the results obtained with the algorithms are analysed and the most appropriate approaches are determined. This book is of interest to researchers, civil and industrial engineers, energy auditors and architects, by providing a resource which improves energy audit tasks in existing buildings.

This book explores China’s low-carbon consumption in the context of residential behaviour, corporate practices and policy Implication. It first calculates the carbon and ecological footprints of residential consumption, including both direct and indirect emissions, before discussing Chinese residential behavioural aspects and determinants of electricity saving, low-carbon transportation, low-carbon product purchasing, and e-waste recycling. The authors then investigate the relationship between industrial growth and carbon emissions, using the example of the iron and steel industry to examine the motivation for energy intensive industries to reduce carbon emissions. They also consider energy efficiency and inter-company collaboration on carbon emission reduction. Lastly, the book describes the major low-carbon policies in China and their impact, economic cost and public acceptance.