AI-Based Optimization of Residential Building Layout for Energy Efficiency

Residential buildings are responsible for a large and rising share of global and national energy consumption, and a significant part of this demand is fixed at the design stage through decisions about room arrangement, orientation, compactness, and zoning. Traditional layout design relies on the designer's experience and iterative manual trial-and-error, a process that cannot practically explore more than a small fraction of the possible design space and therefore routinely leaves substantial energy savings unrealized. Artificial intelligence (AI) - encompassing machine learning (ML) predictive models, evolutionary and metaheuristic optimization algorithms, and, more recently, generative deep-learning models such as generative adversarial networks (GANs) and diffusion models - offers a way to search this design space rapidly and systematically, coupling fast energy-performance prediction with automated layout generation and refinement. This paper reviews and synthesizes recent research on AI-based optimization of residential building layouts for energy efficiency, describes the principal computational pipelines used (machine-learning-based energy prediction, evolutionary/genetic-algorithm optimization, and generative layout models), and develops a representative framework and case-study workflow suited to Indian residential construction. The review shows that machine-learning surrogate models (random forest, gradient boosting, deep neural networks) can predict building energy consumption with R² values above 0.99 in some studies, several orders of magnitude faster than physics-based simulation, allowing them to be embedded inside evolutionary optimization loops; genetic-algorithm and multi-objective evolutionary approaches (e.g., NSGA-II) coupled with such surrogates have achieved reported energy reductions of roughly 5- 25% relative to conventional designs; and diffusion-based generative models combined with energy-aware genetic refinement have achieved up to 17.5% energy reduction relative to baseline generative layouts while preserving architectural plausibility and spatial constraints. Based on this synthesis, the paper proposes a three-stage AI-based layout optimization framework - datadriven energy prediction, generative layout synthesis, and evolutionary refinement - and discusses its applicability, limitations, and future development for Indian residential design practice, including integration with the National Building Code, Eco-Niwas Samhita, and locally available climate and construction-cost data