Modeling residential solar PV adoption in Ireland: An agent-based modeling approach

Transitioning to renewable energy, particularly through the adoption of residential solar photovoltaic (PV) systems, is essential for sustainability and carbon reduction goals. This study employs an agent-based modeling (ABM) approach to simulate and forecast residential PV adoption, integrating key economic, policy, and behavioral factors. The model is calibrated using historical adoption data and evaluated statistically, achieving a coefficient of determination ( R 2 ) of 0.9971 and a Root Mean Square Error (RMSE) of 1661.62, indicating strong alignment with the historical adoption trajectory. A temporal holdout validation was also conducted to assess predictive performance on unseen adoption data. The model performs sensitivity analyses on key drivers, including subsidies, electricity prices, and PV system costs, assessing their impacts on adoption trends. Additionally, future adoption scenarios are simulated to examine potential policy and economic impacts. To account for uncertainties in long-term projections, a Monte Carlo-based uncertainty analysis estimates adoption trajectories under varying economic and demographic conditions. This research provides a data-driven tool for policymakers, utilities, and stakeholders to design effective policies that support residential PV adoption.