In this paper we present a novel agent-based modeling methodology to predict rooftop solar adoptions in the residential energy market. We first applied several linear regression models to estimate missing variables for non-adopters, so that attributes of non-adopters and adopters could be used to train a logistic regression model. Then, we integrated the logistic regression model along with other predictive models into a multi-agent simulation platform and validated our models by comparing the forecast of aggregate adoptions in a typical zip code area with its ground truth. This result shows that the agent-based model can reliably predict future adoptions. Finally, based on the validated agent-based model, we compared the outcome of a hypothesized seeding policy with the original incentive plan, and investigated other alternative seeding policies which could lead to more adopters.

An important contribution of our work is to quantitatively The SunShot Initiative (Sunshot 2011) has the goal of reducing assess the impact of peer effects on PV adoption in relationship the total costs for photovoltaic (PV) solar energy systems to other economic and non-economic variables. It has to be "cost-competitive" with other forms of energy. At that long been noted that peer effects play a significant role in cost, PV could be widely adopted and thus allow the United the adoption of new technology. For instance, (Rogers 2003) States (US) increase it's use of clean energy - a goal of the highlights the importance of "opinion leaders" and interpersonal Department of Energy (U.S.

Understanding population wide attitude change is an important step to understanding the behavior of societies. In this talk, we will study population wide attitude change through the use of computational models. Using a model based on parallel constraint satisfaction, we will show how varying parameters, such as cognitive effort, and community structure, can impact attitude change in populations.

Attitudes play a significant role in determining how individuals process information and behave. In this paper we have developed a new computational model of population wide attitude change that captures the social level: how individuals interact and communicate information, and the cognitive level: how attitudes and concept interact with each other. The model captures the cognitive aspect by representing each individuals as a parallel constraint satisfaction network. The dynamics of this model are explored through a simple attitude change experiment where we vary the social network and distribution of attitudes in a population.