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Highlights A tool was developed to help real estate owners plan refurbishment  The tool identifies priorities in terms of actions and buildings to be retrofitted  It combines dynamic building energy simulations and multi-objective optimisation  It was applied to a social housing stock with a multi-year budget constraint  Priorities are set based on the identified optimal renovation schedules

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Driven by the energy and digital transitions, the concept of smart buildings is gaining importance. In most cases, these buildings are designed with the aim of reducing the consumption of energy resources during the operation phase, while improving the occupants’ comfort and safety. However, smart sensors and actuators themselves have impacts on other environmental indicators and life cycle stages. In this work, the environmental performances of a smart multifamily house and of a standard one are compared using both dynamic building energy simulations and life cycle assessments (LCA). Two insulation levels are possible for the building and the alternatives’ comparison includes uncertainties and variabilities related to occupancy. It turns out that smart building has less impacts than conventional one over their entire life cycle, but their benefit decreases when the level of insulation increases.

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It is a challenge to achieve an affordable and energy efficient building renovation that leads to a decrease of the environmental impacts, as many objectives and various renovation actions should be considered. In this study, the many-objective optimisation algorithm NSGA-III is applied, for the first time in building energy. The solutions search process of NGSA-III makes it more efficient when dealing with more than three objectives. It was included in a simulation platform consisting in a dynamic building energy simulation (DBES) tool, a building life cycle assessment (LCA) tool and a cost database, in order to guide the decision towards good energy-environment-cost renovation solutions for a hotel in the Alps. Its application helps identifying good renovation strategies for the hotel. The results robustness to the climate data is then assessed.