Journal of Resource Management and Decision Engineering 5 Serial Number 15 2026 03 01 Modeling and Analysis of Energy Consumption Reduction Strategies in Sustainable Buildings in Hot and Humid Regions Using Simulation and Artificial Neural Networks (Case Study: Hormozgan) Modeling and Analysis of Energy Consumption Reduction Strategies in Sustainable Buildings in Hot and Humid Regions Using Simulation and Artificial Neural Networks (Case Study: Hormozgan) 1 10 10.61838/kman.jrmde.240 EN Journal Article 2025 10 08 <p>This study was designed to model and analyze effective strategies for reducing energy consumption in sustainable buildings in hot and humid regions, with a specific focus on Hormozgan Province. Given the rapid growth of energy use in the building sector and the region’s climatic challenges, the development of low-energy and climate-responsive solutions has become imperative. The research adopts an applied and hybrid methodology (analytical–simulation and machine learning), in which building energy simulation and artificial neural networks (ANNs) were employed to predict energy consumption. Ten sustainable design strategies were evaluated, including advanced thermal insulation, green roofs, passive cooling design, reduction of the window-to-wall ratio (WWR), variable refrigerant flow (VRF) smart ventilation systems, low-emissivity glazing, optimal building orientation, and hybrid ventilation. The results indicated that the strategy of advanced insulation using mineral wool was the most effective solution for hot and humid climates, achieving an energy savings of 75.1%. This was followed by the combined strategy of green roof and insulation (58.7% savings) and passive cooling design (57.3%), which ranked second and third, respectively. The findings further demonstrated that building-envelope-based strategies (insulation, WWR reduction, orientation) exhibit substantially higher effectiveness compared to active systems (such as hybrid ventilation). The ANN model achieved a coefficient of determination (R²) exceeding 0.95 and a mean absolute percentage error (MAPE) below 2%, indicating a high level of predictive accuracy. By providing a quantitative ranking of energy consumption reduction strategies, this research offers a scientifically grounded and operational framework for designers, policymakers, and urban planners working in hot and humid regions of the country.</p> https://journalrmde.com/index.php/jrmde/article/download/240/248