This study presents the development and application of a tool for en-ergy 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 en-ergy reductions. While the dataset’s limited size poses constraints on analysis robustness, it underscores growing interest in managing public buildings sustain-ably. 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 modelling, and employing advanced con-trol systems. The tool's integration with external databases could enhance predic-tive accuracy, aligning with objectives regarding energy communities and renew-able 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 refur-bishment interventions, promise enhanced energy efficiency in public buildings. Overall, the tool represents a crucial step towards efficient energy management in the built environment, with implications for sustainable development and cli-mate action.
Sustainability in Energy and Buildings 2024
M. Ferrando;A. Banfi;F. Causone
2026-01-01
Abstract
This study presents the development and application of a tool for en-ergy 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 en-ergy reductions. While the dataset’s limited size poses constraints on analysis robustness, it underscores growing interest in managing public buildings sustain-ably. 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 modelling, and employing advanced con-trol systems. The tool's integration with external databases could enhance predic-tive accuracy, aligning with objectives regarding energy communities and renew-able 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 refur-bishment interventions, promise enhanced energy efficiency in public buildings. Overall, the tool represents a crucial step towards efficient energy management in the built environment, with implications for sustainable development and cli-mate action.| File | Dimensione | Formato | |
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