AI-DRIVEN APPROACHES TO DESIGNING ENERGY-EFFICIENT SMART BUILDINGS

This study explores the application of Artificial Intelligence (AI) techniques for designing energy-efficient smart buildings, with a focus on integrating Building Information Modelling (BIM), energy modelling, and AI-driven tools. The research highlights the significance of AI in optimizing building orientation, HVAC design, material selection, and window-to-wall ratios while incorporating renewable energy systems such as solar photovoltaics. Using Autodesk Revit for digital modelling and Insight for energy simulation, various design scenarios were analyzed to evaluate energy performance. Results revealed that south-facing orientations minimized energy consumption, energy-efficient HVAC systems provided substantial long-term savings, and climate-appropriate materials like hollow bricks and triple-glazed low-E windows significantly reduced thermal loads. Solar analysis demonstrated the effectiveness of south-facing panels in maximizing renewable energy generation, while studies on glazing confirmed that excessive east–west window ratios increased cooling demands. AI-based tools such as Coohom further enhanced design efficiency by automating layouts and improving visualization. Overall, the findings emphasize that AI-integrated approaches can reduce energy consumption, improve sustainability, and create climate-responsive smart buildings, particularly in hot-dry regions like Jabalpur. The study concludes that combining digital modelling, renewable integration, and AI-driven optimization provides a reliable framework for future-ready, energy-efficient smart building design.