Stochastic Optimal Control of HVAC System for Energy-Efficient Buildings
نویسندگان
چکیده
The heating, ventilation, and air-conditioning (HVAC) system account for substantial energy use in buildings, whereas a large group of occupants is still not actually feeling comfortable staying inside. This poses the issue developing energy-efficient HVAC control, i.e., reduce (cost) while simultaneously enhancing human comfort. brief pursues objective studies stochastic optimal control subject to uncertain thermal demand (i.e., weather occupancy). Particularly, we involve elaborate predicted mean vote (PMV) comfort model optimization. problem computationally challenging due nonlinear nonanalytical constraints imposed by dynamics PMV model. We make following contributions address it. First, formulate as Markov decision process (MDP) which desirable modeling technique capable handling complexities. Second, propose gradient-based learning (GB-L) method progressively policy off-line store it on-line execution. Third, prove method’s converge policies theoretically, its performance cost, comfort, computation) via simulations. comparisons with existing predictive based relaxation (MPC-R) assumed accurate future information supposed provide near-optimal bounds show that though there exists some discount cost reduction 6.5%), proposed can enable efficient implementation (less than 1 s) high probability under uncertainties.
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ژورنال
عنوان ژورنال: IEEE Transactions on Control Systems and Technology
سال: 2022
ISSN: ['1558-0865', '2374-0159', '1063-6536']
DOI: https://doi.org/10.1109/tcst.2021.3057630