Smart strategies for building energy efficiency : integrating occupancy-based HVAC control and machine learning predictions
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In the realm of commercial building energy consumption, Heating, Ventilation, and Air Conditioning (HVAC) systems play a pivotal role, around the globe. However, inefficiencies in HVAC control practices lead to significant energy wastage through practices like overcooling, overheating, and running HVAC systems in unoccupied spaces. Compounded by fixed HVAC scheduling due to limited occupancy data, this norm exacerbates energy overuse and ecological impact. This study addresses the challenge through occupancy-based HVAC control strategies, bolstered by machine learning predictions. The study delves into using occupancy insights for HVAC control, utilizing simulations to uncover potential energy savings. It extends its reach into time series forecasting, predicting energy patterns for short terms. This proactive approach empowers HVAC systems to optimize schedules, curbing wasteful consumption and enhancing overall efficiency. This integration of occupancy-based strategies and predictive modeling emerges as a pioneering framework to reduce energy waste in commercial buildings. By coupling real-time occupancy insights with advanced modeling, the research not only reveals untapped energy conservation potential but also charts a path toward sustainable and efficient energy management. In an era of mounting energy efficiency focus, this study promises economic and environmental gains in the commercial building sector.
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M.S.