An Analytics Framework for Modeling Residential Photovoltaic Adoption and Decision Dynamics

TL;DR

This paper presents a comprehensive data-driven framework to model and analyze residential photovoltaic adoption, emphasizing imitation effects, external influences, and spatial heterogeneity to inform policy and investment.

Contribution

It introduces a novel integrated analytical framework combining temporal, external, and spatial analytics for photovoltaic adoption modeling.

Findings

Imitation effects are a key driver of PV adoption.

Social perception influences adoption more than regulations or socioeconomic factors.

Spatial analysis reveals demographic and socioeconomic correlations with adoption patterns.

Abstract

Photovoltaic generation plays a central role in the energy transition, yet understanding its adoption dynamics requires robust analytical frameworks that capture both temporal and spatial patterns of decision behavior. This study applies a data-driven decision analytics approach to examine residential self-consumption photovoltaic installations in Catalonia within an innovation diffusion framework. The temporal evolution of adoption is modeled using a logistic growth function, providing evidence that imitation effects are a primary driver of adoption decisions. To extend the analysis, a quantitative methodology is developed to estimate the influence of external factors on adoption behavior, revealing that social perception exerts a stronger impact than regulatory and socioeconomic variables when considered independently. In addition, a spatial analytics component is incorporated to assess territorial heterogeneity, identifying correlations between adoption patterns and demographic and socioeconomic characteristics. The findings contribute to predictive and diagnostic analytics by offering a structured framework to model technology diffusion and inform policy and investment decisions aimed at accelerating sustainable energy adoption.