This study presents the development and application of a tool for energy benchmarking of public buildings, demonstrated through a real case study in the area of Milan, Italy. By leveraging statistical analysis methodologies, the tool explores factors influencing thermal transmission and estimates potential energy reductions. While the dataset’s limited size poses constraints on analysis robustness, it underscores growing interest in managing public buildings sustainably. The tool offers flexibility, enabling variable adjustments as datasets evolve. Future implementations could incorporate economic considerations and explore detailed urban characteristics. Enhancements may involve integrating electricity consumption data, refining predictive modeling, and employing advanced control systems. The tool’s integration with external databases could enhance predictive accuracy, aligning with objectives regarding energy communities and renewable energy integration. Furthermore, the study highlights the potential for broader district-scale energy optimization, including interventions like improved insulation and district heating. Such advancements, coupled with classical refurbishment interventions, promise enhanced energy efficiency in public buildings. Overall, the tool represents a crucial step toward efficient energy management in the built environment, with implications for sustainable development and climate action.
A Tool for Energy Benchmarking of Public Buildings Applied to a Real Case Study in Milan, Italy
Ferrando, Martina;Banfi, Alessia;Causone, Francesco
2026-01-01
Abstract
This study presents the development and application of a tool for energy benchmarking of public buildings, demonstrated through a real case study in the area of Milan, Italy. By leveraging statistical analysis methodologies, the tool explores factors influencing thermal transmission and estimates potential energy reductions. While the dataset’s limited size poses constraints on analysis robustness, it underscores growing interest in managing public buildings sustainably. The tool offers flexibility, enabling variable adjustments as datasets evolve. Future implementations could incorporate economic considerations and explore detailed urban characteristics. Enhancements may involve integrating electricity consumption data, refining predictive modeling, and employing advanced control systems. The tool’s integration with external databases could enhance predictive accuracy, aligning with objectives regarding energy communities and renewable energy integration. Furthermore, the study highlights the potential for broader district-scale energy optimization, including interventions like improved insulation and district heating. Such advancements, coupled with classical refurbishment interventions, promise enhanced energy efficiency in public buildings. Overall, the tool represents a crucial step toward efficient energy management in the built environment, with implications for sustainable development and climate action.| File | Dimensione | Formato | |
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