Towards Optimal Building Retrofits: A Multi-Objective Optimisation Framework Using Genetic Algorithms and EnergyPlus

The decarbonisation of the building sector requires the identification of optimal retrofit strategies that balance energy efficiency, cost, and indoor comfort. This paper presents the conceptual design of an integrated simulation pipeline that couples Genetic Algorithms (GAs) with dynamic building performance simulations. The tool will leverage EnergyPlus for energy and comfort simulations, while multi-objective optimisation will be conducted using NSGA-II and NSGA-III algorithms. Key inputs include the building model, a microclimate-adjusted weather file, user-defined objectives, and a set of possible retrofit measures. A novel feature of the pipeline is the dynamic adjustment of objective functions based on stakeholder preferences, ensuring that outputs are aligned with user priorities. Following simulation and optimisation, the Pareto-optimal retrofit scenarios will be ranked through a Multi-Criteria Decision Support System (MDSS), providing tailored recommendations. The proposed pipeline aims to deliver an effective, user-centric decision-making framework to support engineers, consultants, advanced homeowners, and policymakers in achieving sustainable building upgrades.