The built environment plays a crucial role in global decarbonization efforts, yet buildings remain a major contributor to energy consumption and CO2 emissions. Retrofitting existing buildings is essential to decrease the environmental impact of the construction sector but also for mitigating energy poverty, which affected 10.6% of the European population in 2023. This study proposes a multi-objective and multi-disciplinary methodology for optimizing the energy retrofit of a social housing building. The approach integrates parametric modeling, energy and daylight simulations, and evolutionary optimization algorithms to enhance building performance while considering economic and environmental constraints. The optimization is structured in two phases: a first optimization of the building envelope focusing on passive strategies and a second one specifically on the shading system, taking into account the increasing energy consumption for cooling. Results demonstrate that the optimized building achieves an 86% reduction in heating energy consumption and a 41% decrease in cooling energy consumption. The methodology effectively balances energy efficiency, renovation costs, and CO2 emissions while ensuring daylight availability and occupants’ comfort. By utilizing mostly open-access parametric tools, the approach promotes accessibility and broader adoption. This study highlights the potential of multi-objective optimization in retrofit design, offering valuable insights for researchers, policymakers, and practitioners committed to sustainable building renovations.

Multi-objective optimization of building envelope and shading system for the retrofit of large social housing buildings / Del Vecchio, Gaia; Gulino, Annalisa; Ciardiello, Adriana; Ferrero, Marco. - 2:(2025), pp. 326-342. ( Colloqui.AT.e 2025 Trento; Italy ) [10.1007/978-3-032-06978-8_17].

Multi-objective optimization of building envelope and shading system for the retrofit of large social housing buildings

Adriana Ciardiello
Penultimo
;
Marco Ferrero
Ultimo
2025

Abstract

The built environment plays a crucial role in global decarbonization efforts, yet buildings remain a major contributor to energy consumption and CO2 emissions. Retrofitting existing buildings is essential to decrease the environmental impact of the construction sector but also for mitigating energy poverty, which affected 10.6% of the European population in 2023. This study proposes a multi-objective and multi-disciplinary methodology for optimizing the energy retrofit of a social housing building. The approach integrates parametric modeling, energy and daylight simulations, and evolutionary optimization algorithms to enhance building performance while considering economic and environmental constraints. The optimization is structured in two phases: a first optimization of the building envelope focusing on passive strategies and a second one specifically on the shading system, taking into account the increasing energy consumption for cooling. Results demonstrate that the optimized building achieves an 86% reduction in heating energy consumption and a 41% decrease in cooling energy consumption. The methodology effectively balances energy efficiency, renovation costs, and CO2 emissions while ensuring daylight availability and occupants’ comfort. By utilizing mostly open-access parametric tools, the approach promotes accessibility and broader adoption. This study highlights the potential of multi-objective optimization in retrofit design, offering valuable insights for researchers, policymakers, and practitioners committed to sustainable building renovations.
2025
Colloqui.AT.e 2025
Multi-Objective Optimization; Social housing; Building Performance Optimization; Building Energy Simulation; Daylight simulation
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Multi-objective optimization of building envelope and shading system for the retrofit of large social housing buildings / Del Vecchio, Gaia; Gulino, Annalisa; Ciardiello, Adriana; Ferrero, Marco. - 2:(2025), pp. 326-342. ( Colloqui.AT.e 2025 Trento; Italy ) [10.1007/978-3-032-06978-8_17].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1756921
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