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Sudarno, Prabowo Wahyu
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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A Data-Driven Multi-Variable Adaptive Closed-Loop Control Framework for Real-Time Energy Efficiency Optimization Anang Prasetyo; Sudarno, Prabowo Wahyu; Dharaka, Kumara Pinasthika
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 8 No. 2 (2026): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v8i2.16471

Abstract

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.
Co-Authors Anang Prasetyo Dharaka, Kumara Pinasthika