Building retrofit optimization has gained increasing attention as a pathway to improve energy performance and support sustainability. This review examines 162 studies and synthesizes simulation-based (SBMOO) and metamodel-based (MBMOO) multi-objective optimization techniques for building retrofit across climatic conditions. The review also analyzes passive, active, and combined retrofit strategies and evaluates how climatic context influences their suitability and performance. Passive strategies typically involve envelope- or material-related upgrades, whereas active strategies focus on building systems. Energy efficiency, comfort, cost-effectiveness, and environmental impact are identified as the major performance metrics for retrofit evaluation. Sustainability metric such as life cycle assessment (LCA) has yet to be used adequately to evaluate retrofit measures, while social objectives are also less explored. SBMOO provides robust optimization but can be computationally intensive, whereas MBMOO improves computational efficiency through surrogate modeling but depends strongly on dataset quality, sampling strategy, and surrogate model selection. In contrast to earlier reviews that usually emphasize either optimization techniques or retrofit measures independently, this study integrates optimization pathway comparison with climate-based analysis of retrofit strategies. The review also finds that most studies are highly case-specific, limiting transferability across climates, building types, and retrofit contexts. Therefore, this work proposes a synthesized framework to support structured selection of baseline modeling and optimization pathways for future retrofit studies. Overall, the review identifies current methodological trends, key research gaps, and future directions for more consistent and climate responsive retrofit decision-making.

Residential building retrofit requires reliable building energy simulation and efficient performance prediction. This research presents an adaptable workflow for establishing two residential base model templates, namely corner space (CS) and intermediate space (IS). The models were assessed through National Construction Code of Australia-based verification, comparison with a documented Melbourne residential reference model, and Morris index convergence analysis. These models were coupled with an AI-assisted three-stage prediction framework and evaluated across seven additional Australian climates, with Melbourne as the primary case, to examine cross-climate applicability. Stage 1 developed a two-variable analytical model to predict annual energy consumption. Stage 2 extended this to a three-variable relation and compared its energy predictions with support vector machine (SVM). Stage 3 examined multivariable cases using four machine learning (ML) models—artificial neural network (ANN), SVM, random forest (RF), and decision tree (DT)—to predict performance metrics and identify the best model. For the primary Melbourne case, the two-variable relation reproduced the simulated response with mean absolute percentage errors (MAPEs) of 0.27% and 0.62% for CS and IS, respectively. In Stage 2, the analytical model achieved a MAPE of 8.38%, while SVM reduced the error to 0.75%. Cross-climate results confirmed that the staged workflow remained robust across different climates. In Stage 3, SVM was robust with limited data, while the ANN and RF became more competitive with larger samples. DT was prone to overfitting. The proposed framework supports early-stage residential retrofit assessment by enabling retrofit hotspot screening and suitable surrogate model selection before detailed optimization.