Purpose This study investigated the cost benefits of BIM-integrated energy optimization for a residential complex. The purpose of this research is to optimally design residential buildings for energy efficiency by observing climatic conditions and the proportion between energy-consuming facilities using a building information modeling (BIM) system in a temperate and humid environment. Design/methodology/approach Revit software was used for modeling and simulation and Green Building Studio for scenario comparisons. Both are Web-based and resource-efficient. The project aimed to assess energy cost variations due to BIM optimization, considering the building’s size and seaside location. BIM was also used to examine building form and orientation. The building is a four-block residential complex exceeding 18, 000 sq m with a central corridor. Located near the Caspian Sea, it experiences hot summers and mild winters with high humidity and frequent fog. The research steps include initial building modeling using parameters common in the Iranian construction industry; initial energy analysis to determine building energy consumption costs; selection of a plan sample for optimization in design, layout and building materials/components; integration of a photovoltaic system, considering operating costs; and energy analysis of the optimized building, comparing energy consumption in normal and optimized modes. Findings Solar panels with a 20. 4% efficiency rating were used, covering 90% of the available roof area, with a 30-year operational lifespan. The analysis revealed that using BIM technology to optimize energy consumption parameters could reduce energy costs by up to 58% compared to the existing building and by up to 69% compared to a baseline scenario. Adjusting for the current building conditions, the annual energy consumption cost was reduced to 13 per square meter, with a corresponding energy intensity of 112 kWh/m². Following the optimization of structural and utility design, internal layout and energy-related parameters, the annual energy consumption cost was further reduced to 5. 43 per square meter, and the energy intensity decreased to 83. 1 kWh/m². Originality/value The framework of research would enable designers to explore a wider range of design options, considering not only the static geometry of the building but also its dynamic interaction with the surrounding environment. By incorporating real-time weather data, the framework could predict the building’s energy performance across different seasons and weather conditions, allowing for more informed design decisions and a significant reduction in energy consumption. Furthermore, the methodology moves beyond generic climate zone categorization, performing a granular, localized performance analysis where the high humidity and fog directly influence key design outputs, such as thermal bridge mitigation strategies and the precise sizing and balancing of dehumidification-aware heating, ventilation and air conditioning systems, thereby ensuring the resulting energy efficiency framework is not only technically sound but also intrinsically tied to the demanding conditions of the building’s specific seaside locale.
Amani et al. (2025) studied this question.