A data-driven framework for predicting solar rooftop adoption in Germany based on open-source data

Abstract

The rapid growth of photovoltaic (PV) installation poses a major challenge for the energy transition in Germany. A key concern is that the increasing number of PV systems can create overloads in the low voltage grid, particularly in areas with high concentrations of installations. To better estimate the adoption of industry sized PV systems, a recommendation framework is introduced to assess the probability of adoption for specific companies. The presented framework utilizes openly available data and a hierarchical clustering approach to predict the likelihood of PV adoption for a company. Predicting PV adoption for companies allows identification of potential bottlenecks in the energy grid. As a recommendation system, it can be leveraged to promote PV systems more effectively, targeting areas with high adoption potential and optimizing grid infrastructure planning. In order to achieve that, openly available data sources have been acquired through web scraping. Company data then have been clustered using a hierarchical agglomerative approach. The recall value for the installation prediction showed an average performance of 0.75, which is found sufficient for an elaborated estimate of PV adoption.