Modelling complex investment decisions in Germany for Renewables with different machine learning algorithms

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 R2 = 0.86) and gradient boosting (adjusted R2 = 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