Enhancing Retrofit Design for Office Buildings in Indonesia’s Hot-Humid Climates: Exploring Energy and Comfort Trade-Offs through Multi-Objective Optimisation Framework
The building and construction sector is responsible for approximately 34% of total final energy use and contributes about 37% of energy-related carbon dioxide emissions worldwide (United Nations Environment Programme, 2024). Although new buildings can be constructed to high energy performance standards, around 75% of today’s buildings are expected to remain in use in 2050. Most existing buildings do not comply with contemporary energy performance standards. Therefore, energy retrofitting the existing buildings offers significant opportunities to reduce global energy consumption and greenhouse gas emissions. However, the retrofit process is inherently complex due to the wide array of potential energy-efficient measures, conflicting optimisation objectives, and the multifaceted nature of assessing building performance. To address these challenges, simulation-based multi-objective optimisation (MOO) has been identified as a promising methodological approach, allowing for the simultaneous evaluation of various retrofit measures based on conflicting objectives (Attia et al., 2013). In the context of Indonesia, a densely populated country facing rapid construction growth and increasing energy demand, there is a notable lack of comprehensive retrofit regulations and insufficient scholarly attention to these issues. This research aims to develop and apply a simulation-based MOO framework to explore optimal energy retrofit measures, demonstrated through a comparative analysis of two case studies with different architectural styles and construction periods for two office buildings in Jakarta, Indonesia, with a hot-humid climate. The research methodology utilised environmental monitoring, a questionnaire, and built the digital twins using the dynamic thermal simulation software DesignBuilder (DB). Successful model validation based on ASHRAE 14 was achieved using on-site measured data. Subsequently, sensitivity analysis was performed to assess how variations in input variables affect the uncertainty of simulation outcomes, thereby enabling the identification of variables with the most significant influence on model performance (Pianosi et al., 2016). Optimisation was carried out using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to generate sets of Pareto-optimal solutions representing the optimal variable combinations for both buildings. This optimisation aims to reduce cooling energy consumption and discomfort hours with CO₂ emissions reductions as an additional output. In the final phase of the methodology, a multi-criteria decision-making (MCDM) process was implemented through the Weighted Sum Model (WSM). The results showed three Pareto front solutions for the first building and six sets for the second building. These combinations could reduce the cooling energy, discomfort hours, and CO2 emissions up to 23.89%, 77.66%, and 27.28%, respectively.
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