Spatial projections of solar PV installations at subnational level: Accuracy testing of regression models

Abstract

As the growth of solar photovoltaics (PV) accelerates, spatial PV projections at subnational level are necessary for planning grid infrastructure and addressing demand-supply balancing challenges, posed by this intermittent source of electricity. Although spatial models of weather-dependent PV productivity are common, few studies have focused on projections of PV installations. This study uses a comprehensive dataset with 68′341 PV installations in Switzerland to develop 1- to 5-year-ahead projections of PV installations at a level of 143 Swiss districts. A new modelling methodology is demonstrated, using in-sample and out-of-sample accuracy testing of a multiple linear and two spatial regression models with techno-economic and socio-demographic predictor variables. The results show that exploitable solar PV potential, household size, population density, and electricity prices are predictors with positive effect, and the share of unproductive land area is a predictor of PV installations at a district level with negative effect. Spatial regression models point to the importance of spatial spillovers across proximate districts. The accuracy testing shows that spatial regression models have slightly higher accuracy during in-sample testing of projections, but concerning out-of-sample testing, the multiple linear regression model performs equally well for 1- to 5-year-ahead projections.