Evaluation of residential efficiency measures: methods for modeling end-use demands
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Finite natural resources are being depleted and in the process yielding negative environmental externalities such as pollution and climate change. Combined with population growth, municipalities are in need of more effective ways to address the increasing energy and water deficit between supply and demand. One approach to decreasing demand is through the diffusion of efficient technologies in the residential sector through command and control measures or incentives. Though incentives lessen the economic burden of capital costs and make efficient technology adoption more attractive, significant reductions of consumption are uncertain due to its decentralized nature that currently relies on voluntary engagement. Most importantly, uncertainty arises from the lack of detailed information. Additionally, the majority of current decision-making tools and methods rely on estimations, limited local data, or expert un-embodied knowledge. However, recent targeted studies, technological innovation and the changing needs of utilities have begun to capture high-resolution data that can better inform policy-makers. The overall purpose of this research is to assess and provide methodologies that leverage the potential of high-resolution data to evaluate current estimation methods, identify key proxies of end-use demand drivers, and assess the effectiveness of efficient technologies. With respect to residential air conditioning electricity demands, techniques are explored to assess estimated demand models using high-resolution end-use data, proxies that better predict air conditioning loads, cost effective data collection methods, and the importance of seasonal effects is highlighted. With respect to residential water demands, the models and analyses provide stronger proxies and methods for evaluating the likelihood and quantity of outdoor water demands. The studies provide adequate models that address single and joint technology choice, as well as evaluate differences in their effectiveness by U.S. region and landscape features. The models provide insight into ways to jointly control for price and technology choice endogeneity. Additionally, static engineering economic models are expanded to include modeling results to provide more dynamic analysis of water-saving technologies cost-effectiveness. Finally, our models and proxies are able to highlight key characteristics for future ideal data collection methods.