Application of an automated procedure for Solar Radiation estimation for the City of Turin A pilot application in a portion of District 6

Cities represent dynamic symbols of human progress and creativity, continuously shaped by evolving urban planning influenced by technological advancements aimed at enhancing governance efficiency. Embracing digital tools becomes crucial in fostering sustainable urban development. This transformation underscores the synergy between technology and effective management, facilitating the evolution of cities into adaptable, future-ready environments. Aligned with this transformative vision, this thesis aims to offer valuable insights into advancing urban sustainability through sophisticated simulation techniques, focusing specifically on estimating solar radiation within Turin's urban landscape. This research contributes to the broader goal of enhancing digital twin applications in the energy sector, illustrating their potential to revolutionize urban planning and environmental management practices. The study employs a GIS-based approach to estimate solar radiation on an urban scale, assesses GIS tool precision, evaluates the input parameters for solar radiation estimation in Turin, and automates processes using the Graphical Modeler tool in QGIS software. This methodology facilitates achieving research objectives and allows for a comprehensive analysis of the findings. The research focuses on Turin's urban area, selecting representative buildings with diverse orientations and characteristics. Evaluation commences by comparing two commonly used GIS tools for solar radiation estimation, followed by gathering data from open-source datasets, including a high-resolution DSM file, monthly Linke turbidity factor, monthly Diffuse to Global (DG) radiation ratio values, and global horizontal irradiance (GHI). By organizing and evaluating these parameters, the study highlights the significance of selecting appropriate inputs influenced by geographic and architectural factors. Utilizing the Graphical Modeler tool in QGIS, the study develops three models for solar radiation estimation using monthly, seasonal, and annual parameters. These models utilize digital elevation models and building-specific information to derive median, maximum, and minimum monthly solar radiation values. The study suggests that using aggregated seasonal parameters strikes a balance between efficiency and accuracy in solar radiation estimation, requiring fewer inputs than monthly data while maintaining reasonable precision. This approach supports more effective solar energy production and environmental monitoring in urban contexts.