Hybrid agent-based modeling of rooftop solar photovoltaic adoption by integrating the geographic information system and data mining technique

Abstract

Modeling energy technology adoption involves heterogeneity and dynamic interactions of individuals based on the physical, technical, and economic environments in the decision making process. In this context, this study aims to develop a hybrid model integrating an agent-based modeling (ABM) with the geographic information system and logistic regression for simulating rooftop solar photovoltaic (PV) adoption in the study area. Towards this end, this study regarded “building” as an “agent” to simulate the market diffusion of rooftop solar PV systems in the Nonhyeon neighborhood, located in the Gangnam district, Seoul, South Korea, based on various factors affecting the adoption (i.e., physical, demographic & socioeconomic, technical, economic, and social factors). This study considered three behavioral rules of rooftop solar PV adoption, which were determined using panel logistic regression according to different motivators for rooftop solar PV adoption. Based on these different behavioral rules, three hybrid ABM models were developed to simulate the market diffusion of rooftop solar PV systems. It was shown that models including the various potential motivators for the adoption proposed in this study better represented the reality of aggregate decision-making processes, while the model including only the motivators proposed in the previous ABM studies failed to perform well, rarely adopting the rooftop solar PV system during the runs. The ABM proposed in this study allows the estimation of the aggregate amount and patterns of future market diffusion for rooftop solar PV systems, which can be widely used by governments and electric utilities for evaluating policies and business models.