Comparing different control strategies for indoor thermal comfort aimed at the evaluation of the energy cost of quality of building

Calvino, Francesco Maria; Gennusa, Maria La; Morale, Massimo; Rizzo, Gianfranco; Scaccianoce, Gianluca

doi:10.1016/j.applthermaleng.2010.06.008

Cited by 61 publications

(28 citation statements)

References 15 publications

(16 reference statements)

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“…Lee [11] employed the simplified energy analysis procedure to evaluate the fault level that can represent operation situation of HVAC system. Calvino [12] compared the energy consumption under various thermal comfort control strategies. The evaluation logic aimed to optimize the energy consumption and thermal comfort properly.…”

Section: Introductionmentioning

confidence: 99%

A dual-benchmark based energy analysis method to evaluate control strategies for building HVAC systems

Jin

Fang

et al. 2016

Applied Energy

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Section: Introductionmentioning

confidence: 99%

A dual-benchmark based energy analysis method to evaluate control strategies for building HVAC systems

Jin

Fang

et al. 2016

Applied Energy

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“…This type of model is regarded as a white-box model, which is useful for analytical processes such as prediction and extrapolation [58]. The variation behavior of PPD versus PMV is imperative for the HVAC system to control indoor desired conditions as adopted by many researchers [59][60][61][62][63][64].…”

Section: The Alternative Methodsmentioning

confidence: 99%

Review on the HVAC System Modeling Types and the Shortcomings of Their Application

Homod

2013

Journal of Energy

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The modeling of the heating, ventilation, and air conditioning (HVAC) system is a prominent topic because of its relationship with energy savings and environmental, economical, and technological issues. The modeling of the HVAC system is concerned with the indoor thermal sensation, which is related to the modeling of building, air handling unit (AHU) equipments, and indoor thermal processes. Until now, many HVAC system modeling approaches are made available, and the techniques have become quite mature. But there are some shortcomings in application and integration methods for the different types of the HVAC model. The application and integration processes will act to accumulate the defective characteristics for both AHU equipments and building models such as nonlinear, pure lag time, high thermal inertia, uncertain disturbance factors, large-scale systems, and constraints. This paper shows types of the HVAC model and the advantages and disadvantages for each application of them, and it finds out that the gray-box type is the best one to represent the indoor thermal comfort. But its application fails at the integration method where its response deviated to unreal behavior.

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“…The benchmark case study considered in this paper is the building heating control system, as described by Calvino et al [18] and Chaudhry and Das [19]. The building heating system has not been described in detail, but is represented by a simplified dynamic model [18].…”

Section: Application To a Benchmark Problemmentioning

confidence: 99%

“…The building heating system has not been described in detail, but is represented by a simplified dynamic model [18]. Starting from the instantaneous energy balance of the building heating system and some rough assumptions (which we copy here to allow comparison): (a) the energy balance accounts for thermal energy storage, thermal losses to the outside, internal gains and heat supplied by the heatdistributing devices, (b) only transmission losses are accounted for, no ventilation losses, (c) only indoor air capacity is included in the storage term (particular interest in fast variations of indoor air temperature), (d) correlation used for the thermal power of the heat-distributing device in non-nominal conditions (in this correlation the exponent of the system is set to 1 for computational convenience, imitating convection without enclosure), the following equation representing the dynamic behaviour of the building heating system is derived:…”

Section: Application To a Benchmark Problemmentioning

confidence: 99%

A convex approach to a class of non-convex building HVAC control problems: Illustration by two case studies

Atam

Helsen

2015

Energy and Buildings

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In this paper, a convexification approach is presented for a class of non-convex optimal/model predictive control problems more specifically applied to building HVAC control problems. The original non-convex problems are convexified using a convex envelope approach. The approach is tested on two case studies: a benchmark building HVAC system control problem from the literature and control of a hybrid ground-coupled heat pump (HybGCHP) system. For the first application, convexified model predictive control was used and results were compared with fuzzy and adaptive control results. For the HybGCHP system, convexified optimal control was applied and the results were compared with dynamic programming based optimal control. In the first case superior performance was observed over the corresponding fuzzy and adaptive control results from the literature. For the HybGCHP system the associated convexified optimal control gave almost global optimal results in terms of responses and cost criteria. The suggested method is especially useful for optimal building HVAC control/design problems which include non-convex bilinear or fractional terms. Since a polynomial expression can be recursively expressed as a system of bilinear equations, the proposed method, in principle, can be applied to all systems where polynomial non-convexities exist.

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Comparing different control strategies for indoor thermal comfort aimed at the evaluation of the energy cost of quality of building

Cited by 61 publications

References 15 publications

A dual-benchmark based energy analysis method to evaluate control strategies for building HVAC systems

A dual-benchmark based energy analysis method to evaluate control strategies for building HVAC systems

Review on the HVAC System Modeling Types and the Shortcomings of Their Application

A convex approach to a class of non-convex building HVAC control problems: Illustration by two case studies

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