Enhancing energy efficiency in HVAC systems through precise heating load forecasting and advanced optimization algorithms

The importance of energy-efficient building management strategies has grown in study and practice today. To address the urgency, this study integrates exact heating demand projections with powerful optimization algorithms to provide a complete solution. This research explores the complex task of energy optimization in HVAC systems, requiring careful analysis and creative problem-solving. This study highlights the importance of accurate heating load forecasting in improving HVAC system efficiency, energy conservation, and cost efficiency. The SVR model is fused with 2 complex optimization algorithms, the Coronavirus Herd Immunity Optimizer (CHIO) and the Honey Badger Algorithm (HBA), in a groundbreaking methodology. The main goals are to improve heating load calculations and streamline HVAC system optimization. This study validates the importance of accurate heating load forecasting for cost-effectiveness, energy efficiency, and environmental sustainability in building operations. The SVHB model outperforms other models with a low RMSE value of 0.860 (kW) and a maximum R² value of 0.993, indicating higher predictive accuracy and explanation. To meet the growing demand for energy-efficient building management, this research combines advanced algorithms with accurate heating load estimates for HVAC systems. These findings highlight the importance of precise heating demand forecasts for both cost-effective building operations and environmental responsibility.