Climate Clustering for Photovoltaic Interest

Understanding climatic conditions is crucial for photovoltaic (PV) energy generation. While the KöppenGeiger climate classification is widely used, its adaptation to PV interests, the Köppen-Geiger-Photovoltaic (KG-PV) classification enhances its relevance. This paper introduces a novel method for clustering climates, specifically designed for PV applications. The method aims to minimize manual work, provide flexibility with inputs, select optimal classes, and ensure efficient implementation. The process involves data filtering, normalization, principal component analysis (PCA), optimal cluster number selection, and k-means clustering. The results demonstrate significant alignment between the proposed method and the established KG-PV classification, despite using fewer inputs.