The transition to renewable energy is vital for the Netherlands to meet the National Climate Agreement's goal of a 95% reduction in CO2 emissions by 2050. Residential energy, accounting for 20% of the nation's total, is a key area for local municipalities, which are crucial in implementing decarbonisation measures. Solar photovoltaics (PV) have rapidly emerged as a leading technology, achieving the government's target of 7 TWh from small onshore projects by 2022, eight years ahead of schedule.

However, the rapid adoption of solar PV presents several challenges for policymakers, particularly at the municipal level. These challenges include grid integration, financing, and ensuring equitable access to solar technology. Addressing these issues requires a deep understanding of the factors influencing solar PV adoption and the dynamics at play within different municipalities. Inverse modelling presents a methodology to explore these complexities. This technique involves working backwards from observed outcomes to identify and understand the underlying processes and parameters that generated those results. The application of inverse modelling is, however, very novel. Therefore, this thesis aims to lay the groundwork for future applications of inverse modelling by exploring the factors that influence solar PV adoption in Dutch municipalities. To achieve this goal, the following research question has been formulated:

How can inverse modelling contribute to uncovering plausible explanations for residential solar PV adoption dynamics in Dutch municipalities?

The study employs inverse modelling combined with agent-based modelling to analyse adoption patterns across different municipalities, utilising Random Search and Bayesian Search algorithms. Findings indicate that social factors are often more influential than economic incentives in driving solar PV adoption, emphasising the role of community engagement and peer effects.

These insights underscore the potential of inverse modelling to reveal the complex interplay of factors influencing solar PV adoption. This study demonstrates that inverse modelling can effectively identify patterns and key determinants within municipal data. The research suggests that future efforts should refine this approach, considering multiple agent-based models and alternative methodologies to enhance robustness and accuracy, paving the way for more comprehensive and accurate research in the future.

In conclusion, inverse modelling provides a powerful tool for uncovering the dynamics of solar PV adoption in Dutch municipalities by providing a systematic approach to understanding the underlying factors of the system and its intentions. Through IM, the key determinants that drive solar PV adoption can be inferred by analysing observed patterns and behaviours within the municipalities. This approach is additionally powerful because it does not rely solely on pre-existing theories or assumptions, as with forward modelling. Instead, it derives insights directly from the data.