A novel study on power consumption of an HVAC system using CatBoost and AdaBoost algorithms combined with the metaheuristic algorithms.

Efficient power management is fundamental for organizations and systems to conserve resources, reduce costs, and improve environmental sustainability. To achieve the mentioned purposes, it is vital to perceive power consumption patterns and trends to make informed decisions about energy usage. By evaluating power consumption, inefficiencies and areas for improvement can be identified. In this regard, this paper presents a comprehensive study to predict power usage in a non-residential building HVAC system. The study employs the integration of machine learning models and optimization algorithms to achieve accurate predictions. The results demonstrate the effectiveness of the approach, with the CatBoost-AO model depicting superior performance in comparison with other models across a range of statistical evaluation metrics including NSE, JSD, RMSE, MAPE, VAF, KLD, as well as R2 and runtime. Remarkably, the CatBoost-AO model achieves the maximum R2 value of 0.91, indicating strong predictive capability. Overall, the presented study highlights the potential of employing machine learning and optimization techniques to improve power management and resource efficiency in HVAC systems, contributing to more sustainable and cost-effective operations in non-residential buildings. • Study conducts power consumption predictions using a comparative approach. • CatBoost and AdaBoost models integrated with optimizers for enhanced performance. • Aquila Optimizer, Arithmetic Optimization Algorithm, and Parallel Particle Swarm Optimization are utilized. • Hybrid models evaluated using metrics like NSE, JSD, RMSE, and R2. • CatBoost-AO model excels, achieving an R2 of 0.91 in testing, outperforming other models. [ABSTRACT FROM AUTHOR]