DDABM-SOLAR is an agent-based rooftop solar diffusion model that is implemented in Repast Simphony. It is originally developed to forecast individual and aggregate solar adoption. In particular, the model itself consists of several components, such as solar system capacity model, system cost(buy/lease) model, electricity consumption model and adoption decision model. Notably, all these models are learned from empirical data using machine
learning, following our proposed data-driven agent-based modeling (DDABM) methodology (see the paper below [1]). The current implementation also includes functions, such as, model sensitivity analysis to incentive budget, one-parameter incentive optimization and seeding policy optimization. For more details about our DDABM and policy optimization, please refer to our following publication:

[1 ] Zhang, H., Vorobeychik, Y., Letchford, J., & Lakkaraju, K. (2016). Data-driven agent-based modeling, with application to rooftop solar adoption. Autonomous Agents and Multi-Agent Systems, 30(6), 1023-1049.