Comparison of Nature-Inspired Optimization Models and Robust Machine-Learning Approaches in Predicting the Sustainable Building Energy Consumption: Case of Multivariate Energy Performance Dataset

Accurate prediction of building energy loads is essential for smart buildings and sustainable energy management. While machine learning (ML) approaches outperform traditional statistical models at capturing nonlinear relationships, most studies primarily optimize prediction accuracy, overlooking the importance of computational efficiency and feature compactness, which are critical in real-time, resource-constrained environments. This study aims to evaluate whether hybrid nature-inspired feature-selection techniques can enhance the accuracy and computational efficiency of ML-based building energy load prediction. Using the UCI Energy Efficiency dataset, eight ML models (LightGBM, CatBoost, XGBoost, Decision Tree, Random Forest, Extra Trees, Linear Regression, Support Vector Regression) were trained under feature subsets obtained from the Butterfly Optimization Algorithm (BOA), Grey Wolf Optimization Algorithm (GWO), and a hybrid BOA-GWO approach. Model performance was evaluated using three metrics (MAE, RMSE, and R2), along with training time, prediction time, and the number of selected features. The results show that gradient-boosting models consistently yield the highest accuracy, with CatBoost achieving an R2 of 0.99 or higher. The proposed hybrid BOA-GWO method achieved competitive accuracy with fewer features and reduced training time, demonstrating its suitability for efficient ML deployment in smart building environments. Rather than proposing a new metaheuristic algorithm, this study contributes by adapting a hybrid BOA-GWO feature-selection strategy to the building energy domain and evaluating its benefits under a multi-criteria performance framework. The findings support the practical adoption of hybrid feature-selection-supported ML pipelines for intelligent building systems, energy management platforms, and IoT-based real-time applications.