Machine learning reduces soft costs for residential solar photovoltaics.

ABSTRACT: Further deployment of rooftop solar photovoltaics (PV) hinges on the reduction of soft (non-hardware) costs-now larger and more resistant to reductions than hardware costs. The largest portion of these soft costs is the expenses solar companies incur to acquire new customers. In this study, we demonstrate the value of a shift from significance-based methodologies to prediction-oriented models to better identify PV adopters and reduce soft costs. We employ machine learning to predict PV adopters and non-adopters, and compare its prediction performance with logistic regression, the dominant significance-based method in technology adoption studies. Our results show that machine learning substantially enhances adoption prediction performance: The true positive rate of predicting adopters incre

SUBMITTER: Dong C 

PROVIDER: S-EPMC10156750 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature