Temporal Model of the Drivers of Household PV Purchase in Australia

Abstract

Australian household PV adoption rates are the highest in the world and this is causing a rise in technical problems and the cost of the distribution system. This paper offers a predictive model of household PV purchases in Australia and this could be used in policy to better manage PV uptake patterns. The analysis used 1.6 million domestic PV installation decisions over 11 years from 2006 to 2017 and is statistically significant. Autoregressive integrated moving average (ARIMA) modelling was used to reduce non-stationarity in the data and Granger Causal modelling showed the most effective policy levers are price, subsidy, business confidence and PV feed-in tariffs. This analysis develops a model of Australian PV adoption and increases understanding of consumer roles in the future electricity system. This is compared to other similar models in the literature. The key contribution is that the scale of the model creates a temporal prediction that is not in other literature. The second contribution is that the model may apply to other household energy decisions. This was measured by comparing Australian PV adoption to solar hot water adoption.