An Experimental Hybrid Modelling Approach in Urban Energy Optimization: A Case Study in South Italy

Data poverty is one of the challenges in appointing data-driven modeling approaches in real-world decision-making for self-sufficient future cities. The micro-climatic models for energy-related decisions require high-resolution weather data for capturing dynamic factors of the urban built environment This study aims to demonstrate an experimental hybrid modeling approach for integrating open weather data (both hourly and monthly) within the model building to optimize energy generation, effectively manage energy demand, and improve decision-making processes in the energy sector. Such an approach should promote operational integrated energy models and the necessity of utilizing open data sources for thorough analysis and well-informed decision-making. To showcase the effectiveness of this method, we employ the use of Grasshopper to examine data obtained from EnergyPlus and apply it to a particular municipality located in southern Italy. The importance of including both temporal resolutions of weather data is emphasized by our findings, which enhance our comprehension of energy consumption patterns in the context under study. This study adds to the wider discussion on constructing integrated energy models and highlights the importance of using open data to support well-informed decision-making in the field of energy.