Classification of the energy production potential of water lens solar concentrators using machine learning

Assessing the potential of renewable energy sources for buildings in neighborhoods becomes a crucial task in the early planning stage. Integrating solar energy equipment into urban buildings poses many challenges, such as uncertainties and the complexity of urban built agglomeration. Due to the time-consuming solar energy potential assessment process and lack of knowledge of urban actors, a reliable framework is required to predict buildings' solar energy potential. This research presents a comprehensive machine learning data processing framework to predict output energy of Water Lenses (WL) based on buildings specifications and relationship to the neighbourhood. The research used a raw dataset consisting of 7000 sample buildings in different situations by applying 12 years of climatic conditions in Tallinn, Estonia. The results were entered into a Supervised Machine Learning process and the Gaussian Naive Bayes technique was used for classification of building features to be implemented with solar systems. Finally, the process was measured by a confusion matrix that showed 80% accuracy of ML output predictions in the urban context.