Urbanization has led to increased energy consumption and greenhouse gas emissions, posing significant challenges for designers and urban planners to reduce building carbon footprints. Building morphology plays a crucial role in mitigating building energy consumption. This paper explores the correlation between building morphology and solar irradiation intensity at an urban neighborhood scale. The study uses a BIM model of the city of Grenoble to generate morphological parameters and run solar irradiation simulations. These data are calculated at the resolution of a sensor point, which is the fundamental measurement unit of a ‘morpho-energetic’ correlation study. They are then used to feed a Pearson correlation matrix. Results reveal distinct patterns, of relationship between morphological characteristics and solar irradiation, for façade and roof surfaces. The paper discusses the potential to incorporate sensor point-resolution data to improve solar performance, as well as the opportunities associated with machine learning techniques for solar irradiation prediction.