Abstract
The purpose of this study is to investigate factors on the very local level (households, addresses) that determine whether or not photovoltaic systems - solar cell systems to generate electricity - are to be installed on buildings. We consider a case study of Germany. We aim to identify if the decision of households to install photovoltaics can be explained by peer effects measured by pre-existing proximate installations (also known as the installed base). Since our analysis is based on individual decisions of households, we employ a discrete choice model with panel data in order to model the decisions whether to install in a certain period or not. We employ a geocoded data set of 21 million addresses and the grid-connected photovoltaic systems set up in Germany through 2010 (we consider 11 periods). In total, our data set used for analysis comprises nearly 210 million observations. Our analysis reveals a positive influence of previously installed systems located nearby on the decision to install a photovoltaic system. However, this effect decreases in time. We present an outlook on how our model can be used for forcasting purposes and locational decision making. Finally, a concept is proposed how Google Trends data can be used within our model.
Keywords: discrete choice models, advanced analytics, diffusion modelling, technology adaption, photovoltaics, energy
