Multi-objective optimization method application based on genetic algorithm in energy and cost saving for existing houses in Australia

Energy retrofitting of existing buildings is critical to reduce energy consumption and greenhouse gas emissions. However, the retrofit decision-making process involves complex choices between active and passive strategies and often conflicting retrofit objectives. Depending on the decision-makers (e.g., private or public), the primary objective may vary, such as minimizing investment costs, reducing energy demand, or cutting emissions.

This paper seeks to balance financial constraints with the need to reduce energy consumption and carbon emissions through housing retrofits, focusing on Australia’s existing building stock.

This paper presents a simulation-based multi-objective optimization technique using the NSGA-II genetic algorithm to improve the energy performance of detached houses in Victoria (Australia). The study aims to simultaneously minimize thermal energy demand, investment costs, and carbon emissions.

The approach integrates a Python-based optimization algorithm with EnergyPlus (with DesignBuilder as the interface) to explore solutions involving building envelopes, HVAC systems, indoor shading, and renewable energy sources. Applied to a typical 1970s detached home in Melbourne, the technique selects final solutions from Pareto fronts, considering both energy efficiency and cost-optimality criteria.

The optimal results show significant improvements, with a 64% increase in energy efficiency and up to a 78% reduction in greenhouse gas emissions. This research offers policymakers and householders, not only in Australia but elsewhere, valuable insights into the economic feasibility of retrofitting existing buildings to enhance energy efficiency, taking into account environmental considerations.