<![CDATA[The increasing energy consumption and urbanization of the built environment necessitates better energy management solutions to combat climate change. Despite the optimal energy savings offered by modern approaches such as Reinforcement Learning (RL) and Model Predictive Control (MPC), their real-world implementation is often limited by high computational costs, complex thermal modeling, and the requirement of extensive training data. To bridge this gap, this paper proposes a data-driven, multi-variable adaptive closed-loop control architecture for real-time energy efficiency optimization in smart buildings. The system dynamically adjusts power consumption by leveraging occupancy proxies and external climatic parameters, particularly wind speed and dew point, to account for air infiltration and latent heat loads, using a lightweight decision logic. The system's operational constraints are strictly tuned to ASHRAE 55 standards to guaranty accurate thermal comfort. By numerical simulations with UCI Appliances Energy Prediction dataset, the proposed adaptive system achieves a significant 28.01% reduction in overall energy consumption, and a 26.85% reduction in peak demand compared to baseline operations. This efficiency is estimated to have reduced the system's carbon footprint by 28.01%. These findings demonstrate that the integration of multi-variable climatic data within an easily deployable, heuristic adaptive control logic represents a highly responsive and sustainable alternative to computationally intensive predictive systems, greatly improving modern building management with no compromise to occupant well-being.]]>
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