A Generative AI Technique for Synthesizing a Digital Twin for U.S. Residential Solar Adoption and Generation

TL;DR

This paper presents a novel machine learning and explainable AI-based methodology to generate detailed, household-level solar adoption and energy output data across the U.S., enabling better decision-making and policy analysis.

Contribution

It introduces a new data-driven approach to synthesize high-resolution residential solar datasets, creating a digital twin for modeling and policy evaluation.

Findings

Validated synthetic datasets match real-world data

Digital twin enables policy impact analysis

Case study shows increased adoption with incentives

Abstract

Residential rooftop solar adoption is considered crucial for reducing carbon emissions. The lack of photovoltaic (PV) data at a finer resolution (e.g., household, hourly levels) poses a significant roadblock to informed decision-making. We discuss a novel methodology to generate a highly granular, residential-scale realistic dataset for rooftop solar adoption across the contiguous United States. The data-driven methodology consists of: (i) integrated machine learning models to identify PV adopters, (ii) methods to augment the data using explainable AI techniques to glean insights about key features and their interactions, and (iii) methods to generate household-level hourly solar energy output using an analytical model. The resulting synthetic datasets are validated using real-world data and can serve as a digital twin for modeling downstream tasks. Finally, a policy-based case study utilizing the digital twin for Virginia demonstrated increased rooftop solar adoption with the 30\% Federal Solar Investment Tax Credit, especially in Low-to-Moderate-Income communities.