• David Atomode

Energy efficiency in HVAC (Heating, Ventilation, and Air Conditioning) systems is crucial for reducing energy consumption and minimizing environmental impacts in residential and commercial buildings. This paper investigates the application of machine learning (ML) techniques to enhance the energy efficiency of HVAC systems, focusing on predictive maintenance and optimization of operational parameters. Through a comprehensive review of current methodologies and real-world case studies, we demonstrate how ML algorithms can predict equipment failures, optimize temperature and humidity controls, and adjust airflow rates in real time. These advancements result in significant energy savings, reduced operational costs, and improved indoor comfort. The study also addresses challenges related to data quality, model interpretability, and system integration, proposing solutions for overcoming these obstacles. The findings highlight the transformative potential of ML in HVAC systems, paving the way for broader adoption and further research in this critical area of energy management.

Energy efficiency, machine learning, HVAC systems, predictive maintenance, operational optimization, data quality, model interpretability, system integration

"ENERGY EFFICIENCY IN MECHANICAL SYSTEMS: A MACHINE LEARNING APPROACH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.p441-p448, May-2024, Available :http://www.jetir.org/papers/JETIR2405G61.pdf

"ENERGY EFFICIENCY IN MECHANICAL SYSTEMS: A MACHINE LEARNING APPROACH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppp441-p448, May-2024, Available at : http://www.jetir.org/papers/JETIR2405G61.pdf