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This paper examines spatial effects on hybrid-electric vehicle (HEV) adoption. This is in contrast to most existing analyses, which concentrate on analyzing socioeconomic factors and demographics. This paper uses a general spatial model to estimate the strength of ‘neighbor effects’ on HEV adoption—namely that each consumer’s HEV-adoption decision can be influenced by the HEV-adoption decisions of geographic neighbors. We use detailed census tract-level demographic data from the 2010 United States Census and the 2012 American Community Survey and vehicle registration data collected by the Ohio Bureau of Motor Vehicles. We find that HEV adoption exhibits significant spatial effects. We further conduct a time-series analysis and show that historical HEV adoption has a spatial effect on future adoption. These results suggest that HEVs may appear in more dense clusters than models that do not consider spatial effects predict. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a model to simulate the electricity demand of a single household consisting of multiple individuals. The total consumption is divided into four main categories, namely cold appliances, heating, ventilation, and air conditioning, lighting, and energy consumed by household members’ activities. The first three components are modeled using engineering physically-based models, while the activity patterns of individuals are modeled using a heterogeneous Markov chain. Using data collected by the U.S. Bureau of Labor Statistics, a case study for an average American household is developed. The data are used to conduct an in-sample validation of the modeled activities and a rigorous statistical validation of the predicted electricity demand against metered data is provided. The results show highly realistic patterns that capture annual and diurnal variations, load fluctuations, and diversity between household configuration, location, and size. [Copyright &y& Elsevier]

Abstract: This paper specifies and estimates state-level models of short- and long-term electricity demand in the United States. The short-term model predicts hourly load based on weather and calendar inputs. The long-term model estimates interannual demand, and includes population, prices, and gross state product as predictors. These models are combined to incorporate the short- and long-term trends in electricity consumption when generating forecasts of diurnal patterns into the future. Finally, the authors investigate the effects of short-run price elasticities of demand. The short-term model is shown to be within 95% accuracy of actual levels in out-of-sample tests. [Copyright &y& Elsevier]