Defining challenges of solar irradiance modeling on façades in urban environments: A systematic review

• A review of methods for solar irradiance modeling on facades in urban environments. • State-of-the-art models enable sub-hourly analyses in detailed urban canyons. • Simplified methods often neglect material optics and vegetation elements. • Computational limitations constrain accurate facade analyses at urban scales. • Data-drivenmethods and remote sensing define the next frontiers in the field. Modeling solar irradiance on façades in urban environments involves specific challenges due to the complex geometrical, optical, and temporal dynamics characteristic of the built environment. Whereas existing reviews focused predominantly on roofs or both roofs and façades, the present study investigates façade-specific factors, including solar reflections, shading effects, material optical properties, and the presence of vegetation. This review analyzes 49 studies based on metadata, representation of the built environment, and the solar irradiance modeling approaches. Categorization and correlation analyses were performed with the aim of identifying façade-specific challenges and evaluating their implications. The results permitted to define four main challenges: (i) simulation of solar radiation paths typically involves simplifications that reduce physical realism; (ii) optical properties of urban surfaces are often reduced to uniform albedo values, neglecting material heterogeneity and seasonal variations; (iii) achieving high spatiotemporal resolution over large areas forces trade-offs that reduce the reliability of the estimations while validation data and protocols in urban settings are lacking; and (iv) realistic representation of the urban environment and solar irradiance at large scales are constrained by the high computational demand. Following this, the impacts of these challenges on façade solar applications were investigated across multiple domains (i.e., energy, environmental, economy, social, user comfort, microclimate). Finally, future research trends were outlined, such as (a) advancing the digitalization of urban environments through large-scale automated detection of materials and vegetation, and (b) incorporating data-driven methods to improve computational efficiency and results’ accuracy in complex urban environments.