Machine Learning for Identifying Potential Photovoltaic Installations on Parking Areas

Integrating renewable energy systems into urban areas is crucial for sustainable development. This study assesses the potential for installing photovoltaic (PV) systems in parking areas, focusing on a case study region in Hesse, Germany. A machine learning approach is developed to classify parking lots larger than 900 m2 into suitable and unsuitable categories. The input data includes OpenStreetMap (OSM), the Authoritative Topographic-Cartographic Information System (ATKIS), and high-resolution geospatial datasets. A reference dataset for the two classification categories is created. Multiple input features are generated, and their significance for the classification task is evaluated. Additionally, several shallow machine learning models are implemented and assessed. The XGBoost model demonstrates the highest accuracy at 99 % and is used to classify 10,894 parking areas throughout Hesse. Key suitability features include the Normalized Difference Vegetation Index (NDVI), surface seal (More)