Data-Driven Smart Building Management Using Gradient Boosting for Sustainable Energy and Environmental Control

Sustainable and energy-efficient structures using advanced technologies are promising. The article describes real-time energy efficiency and environmental quality management using gradient-boosting approaches in next-generation smart building optimization. Climate change and carbon reduction priorities have driven built environment innovation. The data-driven study addresses these limits using gradient boosting. These algorithms improve building systems using real-time machines learning of huge sensor data. Monitoring temperature, humidity, occupancy, and other variables enhances energy efficiency and indoor air quality. Syncing data collection, feature engineering, model training, and real-time prediction are advised. IoT sensors gather data on interior dynamics within the building, which powerful feature engineering algorithms analyze. Gradient boosting methods like XGBoost or LightGBM are trained on labeled datasets to find complex input-outcome relationships. Control signals adjust building systems depending on the model's predictions, introducing predictive analytics into everyday operations. The method offers a feedback loop for system performance monitoring and model retraining to adapt to changing conditions.