Modelling complex investment decisions in Germany for renewables with different machine learning algorithms. (August 2019) - Frey, Ulrich J.; Klein, Martin; Deissenroth, Marc
Abstract: Investment decisions in renewable energies are known to be influenced by many diverse drivers, e.g. social, political, geographic, economic and psychological. Non-comprehensive models are problematic since missed interactions might introduce bias. We implement a robust modelling approach by (1) using a large data set with 1.4 million solar installations and (2) three different machine learning algorithms (deep neural networks, gradient boosting, random forests). Generalized linear models serve as baseline and comparison. A high prediction accuracy can be achieved on the county level with deep neural networks (adjusted R 2  = 0.86) and gradient boosting (adjusted R 2  = 0.87). The most important drivers are population per county, followed by type of urbanisation and social variables like unemployment, with varying degree of importance for the different machine-learning algorithms. Our approach points out both differences and agreements across methods and therefore a higher confidence in their interpretation. Highlights: One of the first comprehensive models for investment decisions in photovoltaics. Applies gradient boosting achieving very high explanatory power (adj. R2 = 0.87). Provides robust estimates by using neural networks, random forests as comparison. Most important drivers are demographic.