Aug 2026· Solar energy and sustainable development· Vol 15, pp. 118-132· 0 citations
Abstract
In addressing the imperative need to optimize heating, ventilation, and air-conditioning (HVAC) systems for improved indoor comfort and reduced energy expenditures within buildings, Model Predictive Control (MPC) emerges as a highly effective algorithm for the proactive management of intricate HVAC systems. This article introduces an MPC model designed with the dual objectives of guaranteeing thermal comfort and minimizing energy consumption in residential heating contexts. The model, developed and simulated using the MATLAB Simulink platform, achieves these objectives through meticulous adjustments of MPC parameters, ensuring optimal heating energy consumption while sustaining comfort levels. Compared to a traditional PID controller, the proposed MPC demonstrated superior energy efficiency, achieving up to 22.7% in energy savings. These findings underscore the promising potential of MPC for intelligent management of residential heating systems, striking a balance between comfort and energy efficiency. The results highlight the practical advantages of adopting MPC in residential settings, paving the way for more sustainable and comfortable living environments.
Heating, Ventilation, and Air Conditioning (HVAC) systems account for 60–70% of residential electricity consumption in Oman, where extreme desert climate, with temperatures regularly exceeding 45 °C create substantial cooling demands. Unlike general reviews of smart HVAC controls, this study specifically evaluates the...
Mohammed Abu Safaqah, J. Natarajan, Khalid Anwar· Buildings· 0 citations
The buildings sector accounts for 40% of global energy consumption and over 30% of carbon emissions, with HVAC (heating, ventilation, and air-conditioning) systems responsible for more than 51% of building energy use. To address the challenge of reducing HVAC energy consumption while maintaining thermal comfort and ind...
The increasing adoption of renewable energy technologies in residential buildings necessitates intelligent energy management strategies capable of improving energy efficiency while maintaining occupant comfort. This study presents a smart home energy management framework integrating a Heating, Ventilation, and Air Cond...
To address the limitations of existing central air conditioning energy-saving algorithms—such as their inability to achieve conventional optimization or adapt to grid peak shaving, coupled with nonlinear system dynamics, environmental uncertainties, and high-dimensional optimization challenges—we propose an integrated...
Junjie Lin, Zhuo-Fu Deng· International Conference on...· 0 citations
Educational buildings frequently face issues with overheating and high energy
consumption associated with HVAC systems, amid rising global temperatures and
increasing demands for thermal comfort. Night Ventilation (NV) has been recognized as
an effective passive or semi-passive cooling technique that utilizes cooler ou...
Marius Adam, A. Tokar, Alexandru Dorca et al.· Revista Romana de Inginerie...· 0 citations
A Smart Air Conditioning Management System based on a Deep Q-Network agent capable of dynamically balancing energy use and thermal comfort and demonstrates that reinforcement learning enables adaptive AC control, offering a scalable approach to energy-efficient building management.
Jason Harvey Lorenzo, Justin Kyle O. Ricafort, E. Q. Macabebe· IOP Conference Series: Earth...· 0 citations
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