<![CDATA[Early-stage estimation of heating and cooling loads supports energy-efficient building design, but complex predictive models may impose unnecessary computational costs for small tabular datasets. This study benchmarks four lightweight regression models, such as Linear Regression, Ridge Regression, Random Forest, and Gradient Boosting, using the UCI Energy Efficiency dataset containing 768 simulated building configurations, eight design variables, and two continuous targets. Model accuracy was evaluated with shuffled 10-fold cross-validation using mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination (R²). Computational efficiency was assessed through training time, prediction latency, and serialized model size, while permutation importance was used for interpretation. Random Forest achieved the lowest heating-load RMSE (0.4624) and an R² of 0.9978; however, its difference from Gradient Boosting was not statistically significant. Gradient Boosting was approximately 96 times smaller and 20 times faster at inference. For cooling load, Gradient Boosting achieved the lowest RMSE (1.4903) and an R² of 0.9751, significantly outperforming Random Forest in fold-level RMSE. Relative compactness was the most influential heating-load feature, whereas overall height dominated cooling-load prediction. The results indicate that Gradient Boosting offers the strongest overall accuracy-efficiency trade-off for lightweight smart-building prediction systems.]]>
