Assessment regarding energy saving and decoupling for different AHU (air handling unit) and control strategies in the hot-humid climatic region of Iraq

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  • lable at ScienceDirect Energy 74 (2014) 762e774 Contents lists avai Energy journal homepage: www.elsevier .com/locate/energy Assessment regarding energy saving and decoupling for different AHU (air handling unit) and control strategies in the hot-humid climatic region of Iraq Raad Z. Homod* Department of Petroleum and Gas Engineering, University of Basrah, Qarmat Ali Campus, 61004 Basrah, Iraq a r t i c l e i n f o Article history: Received 31 January 2014 Received in revised form 9 July 2014 Accepted 16 July 2014 Available online 12 August 2014 Keywords: Decoupling HVAC system Improving control performance PMV model HVAC energy efficiency Optimal thermal comfort * Tel.: þ964 7821731696; fax: þ964 60 389212116. E-mail addresses:, raad.h 0360-5442/© 2014 Elsevier Ltd. All rights reserved. a b s t r a c t In a hot and humid climate, HVAC (heating, ventilating and air conditioning) systems go through rigorous coupling procedures as a result of indoor conditions, which are significantly affected by the outdoor environment. Hence, a traditional method for addressing a coupling setback in HVAC systems is to add a reheating coil. However, this technique consumes a significant amount of energy. Three different stra- tegies are designed in a hot and humid climate region, such as Basra, for AHUs (air handling unit), and their evaluations of decoupling are compared. The first and second strategies use the same feedback control references (temperature and relative humidity), except the second one also uses a reheating coil and a wet main cooling coil. The AHU (air handling unit) of the third (proposed) strategy is equipped with a dry main cooling coil and a wet pre-cooling coil to dehumidify fresh air, which allows the controller to handle the coupling problem. Furthermore, the proposed strategy utilises the PMV (pre- dicted mean vote) index as a feedback control reference to increase optimisation parameters that provide more flexibility in meeting the thermal comfort sensation. The adaptive control algorithm of nonlinear multivariable systems is adopted to coordinate these three policies of optimisation. The results of the three strategies show that the proposed scheme achieved the desired thermal comfort, superior per- formance, adaptation, robustness and implementation without using a reheating coil. © 2014 Elsevier Ltd. All rights reserved. 1. Introduction In recent decades, studies on the parameters of HVAC (heating, ventilating and air conditioning) systems, such as the temperature, PMV (predicted mean vote), HVAC system structure volume and control strategies, have demonstrated high performance in HVAC systems, particularly in regard to saving energy [1]. Temperature is commonly used as the thermal comfort control objective in early HVAC systems [2,3]. However, temperature alone does not ensure a person's thermal comfort [4]. Temperature and relative humidity are coupled; hence, it is difficult to control both factors when each has its own strict set point [5]. But, the demands for modern HVAC systems regarding highly systematic products, material integration and energy integration have resulted in strictly coupled processes. This coupling has exposed many of the uninvited characteristics of HVAC systems, which are reflected in the limitations of the classical controllers, such as PID (Proportional Integral Derivative), that are used to manipulate the AHU (air handling unit) inputs. Further- more, the currently used PID tuning techniques are inadequate when dealing with MIMO (multi-input, multi-output) processes [6,7]. PI (Proportional Integral) and PID controllers are commonly used in HVAC systems due to their simplicity in structure and their relative effectiveness; additionally, the units can be easily under- stood, which makes them practical to implement [8]. Usually, the decouplingmethod is adopted to release or alleviate the coupling of two ormore of the control objectives in two ormore of the interlaced loops, which is a difficult task for most of the plant model because all of the decoupling techniques have limitations [9,10]. The conventional solution includes adding a reheating coil to address this coupling setback. However, the use of a reheating coil increases the power consumption through the control of the RH (relative humidity) in the conditioned space when the thermal comfort is maintained at an acceptable level [11,12]. Generally, two types of decoupling control systems are currently used: static and dynamic. Static decouplers are effective when high response con- trols are not required to oversee the processes [13]. Additionally, the design of static decouplers is straightforward, and their implementation is based on the inverse process of steady state Delta:1_given name
  • Nomenclature Symbols A surface area, m2 C heat capacitance, J/�C dEs/dt rate of change in storage energy of the system, J/s E;in energy rate entering the system, J/s E:out energy rate leaving the system, J/s M mass, kg Cp specific heat, J/kg�C m: mass flow rate, kg/s Mcp heat capacitance, J/�C T temperature, Co u humidity ratio, kgw/kgda h latent heat/heat transfer coefficient, J/kg, W/(m2�C) Q : cooling load, WC CF surface cooling factor, W/m2 U construction U-factor, W/(m2�C) DT cooling design temperature difference, �C OFt, OFb, OFr opaque-surface cooling factors DR cooling daily range, �C CFfen surface cooling factor, W/m2 UNFRC fenestration U-factor, W/(m2�C) PXI peak exterior irradiance, W/m2 SHGC solar heat gain coefficient IAC interior shading attenuation coefficient FFs fenestration solar load factor Et, Eb, EDpeak total, diffuse, and direct irradiance, W/m2 Tx transmission of the exterior attachment Fshd fraction of the fenestration shaded by overhangs or fins L site latitude, �N SLF shade line factor Doh depth of the overhang, m Xoh vertical distance from the top of the fenestration to the overhang, m Fcl shade fraction closed (0e1) j exposure (surface azimuth), measured as degrees from south V; volumetric flow rate, L/s DF infiltration driving force, L/(s cm2) < thermal resistance, �C/W Noc number of occupants Nbr number of bedrooms aroof roof solar absorbance t time constant, s I infiltration coefficient Du indooreoutdoor humidity ratio difference, kgw/kgda Subscripts m air in mixing box r room/return o outside os outside supply i inside He heat exchanger a air w water aHe air in the heat exchanger L leakage Win water input Wout water output Wl wall room inside room out outside room g glass fg heat of vaporization Opq opaque inf infiltration fen fenestration f indoor and outdoor t at time t flue flue effective es exposed ul unit leakage ig internal gains l latent s sensible/supply fur furniture cl closed R.Z. Homod / Energy 74 (2014) 762e774 763 gains. However, static decouplersmay not always be able to provide satisfactory control performance. In contrast, dynamic decouplers require detailed process models, but they provide better perfor- mance than static decouplers provide [14,15]. For practical opera- tions, the emphasis is typically placed on suitability and causality needs, which makes precise configurations difficult to achieve, especially for high-dimensional MIMO processes. To settle these difficulties, most of these methodologies focus on TITO (two input and two output) systems [16,17]. The main shortcoming of the dynamic methods lies in the complexities of the decoupler ele- ments, which are obtained from the apparent process model. The difficulty becomes greater for sophisticated plants because the technique incorporates the determinant of the model transfer function [18]. Additionally, the requirement for the decoupler is that all of its elements must be proper, causal and stable [19]. A few studies in the literature have focused on the inverted decoupling methods that are used to reduce variable interactions in the process [18e22]. Gagnon [10] demonstrated that the performance of inverted decoupling depends on the scheme of implementation. When inverted, decoupling is implemented with a lead-lag and delay function process, and the control performance retreats. Normalised decoupling control design methodology was used by Shen [23]. For this type of decoupling, the ETF (equivalent transfer function) of each element in the transfer function matrix was required to derive the closed-loop of the plant model, including the algorithm of the control system. Then, the decoupler was obtained by multiplying the inverse of the ETF by a stable, proper and causal ideal-diagonal transfer function. This paper seeks to analyse and discover the paramount choice of controlled parameters in the HVAC systems, which are reflected in optimisation controller performances. However, the controller's performance is related to buildings' energy efficiency, which is most directly affected by the decoupling problem. Therefore, in this study, the extensive and elaborate models of a building that has HVAC system components are used to simulate a real system. Deriving the matrices of decoupling, inverted decoupling or ETF from such a complex model is challenging because all of its ele- ments must be proper, causal and stable. In concision, the HVAC control systems use both temperature and RH as references instead of using temperature only, which is what the earlier mode did. Because temperature and RH are coupled, it is difficult to control them separately for a certain desired value [11].
  • R.Z. Homod / Energy 74 (2014) 762e774764 It is possible to solve a problem in which the variables of tem- perature and relative humidity are coupled. The first modification in AHUs is the addition of a fresh air pre-cooling coil that is used to alleviate the coupling intensity, which is particularly necessary in humid climates. The secondmodification for control objectives is the increase of the optimisation parameters of the output controller by adding amodel of the PMV index in order to evaluate indoor thermal comfort. Next, decoupling and reduction in energy are simulated by comparing three different systems under real weather conditions within certain set point comfort limits. The first system is a con- ventional system in which the objective is to achieve the tempera- ture and relative humidity that are within the limits of the desired conditions. The second system is similar to the first, with the only difference being the addition of a reheating coil and a wet main cooling coil in AHUs that are used to solve the coupling problem. However, these additional reheating and wet main cooling coils double the energy consumption of the unit due to the addition of two processes: an implemented sub-cooling process that reduces the RH and reheating the supplied air in order to meet the desired levels of thermal comfort. The third system is the same as the first, but it has an additional pre-cooling coil and controller objective where a PMV model is added to facilitate the controller optimisation for four outputs (i.e., the dry bulb temperature, the radiant temperature, the relative air velocity and the relative humidity for an indoor condi- tioned space). Controller (TSKFIS (TakagieSugenoeKang fuzzy inference system)) optimisation is achieved bymanipulating the five AHU inputs (control outputs), which are in the form of the flow rate of chilled water for the pre-cooling coil and themain cooling coil, the flow rate of the supply air (fresh air and return air) and the fan speed of the supply air. Additionally, the PMV model strategy does not require the use of a reheating coil for decoupling purposes. The main contribution of this paper is to address the coupling problem, which arises in the hot and humid climatic region of South Iraq, bymodifying the AHU and applying the algorithm of the adaptive multi-variable control TSKFF (the Takagi-SugenoeKang fuzzy forward). 2. Control system design The present paper attempts to address the shortfall on energy savings and decoupling for buildings with HVAC control systems in the hot and humid climatic region of Iraq. Careful assessments in simulated environments are considered. The PMV model is added to enable controller decoupling of temperature and RH. Increasing manipulation parameters are used to compensate for any bounded variations that may arise due to the limitation of the dampers range. This is considered as a limitation because the HVAC control systems have set upper and lower control limits for the dampers range in order to maintain ventilation for acceptable indoor air quality, according to the ANSI/ASHRAE 62 standard [24]. 2.1. TSKFF controller The industry standard PID controller exhibits the inability to control the objectives of the HVAC system that have inherently adverse characteristics, such as a nonlinear, large-scale systemwith a large thermal inertia, a pure lag time, constraints and factors of uncertain disturbances. Additionally, the indoor thermal comfort must be decoupled from the temperature and relative humidity. Hence, fuzzy logic controllers are used due to their flexibility and intuitive use [25] in controlling the aforementioned characteristics. 2.1.1. Basic description of the control system The most important motivation for adopting this type of controller is due to it being able to treat multi-controlled variables because it converts a TSKFIS (TakagieSugenoeKang fuzzy inference system) model into a memory layers parameters (TKS) model. The output routine of the classical TSKFISmodel requires numerical and logical operation tasks, and these tasks take a long time to be completed. However, the TSK model uses the gradient algorithm, which is a faster online tuning method that requires less mathe- matical manipulations than other traditional methods, such as the backpropagation method for neural networks. The most important aspect of online tuning is that it can tune a multivariable controller with multiple outputs; this tuner can improve the controller's ability to deal with MIMO models that possess a large-scale nonlinear aspect, are heavily coupled, have a pure lag time, contain large thermal inertia, possess uncertain disturbance factors and have constraints, which are common properties in HVAC sys- tems. For the purpose of this study, each strategy of the control structure is developed by upgraded layers of memory in order to coordinate the modification of AHUs, which follows a change in the online tuning system. 2.1.2. Model identification architecture The main concept of the TSKFF (Takagi-SugenoeKang fuzzy forward) structure is based on obtaining the consequent parame- ters bymapping them from the antecedent space to the consequent space. The obtained parameters of the consequent space are organised as layers in the memory space. The parameters in these layers function to the inputs of themodel. These inputs calculate the outputs' data set, which can be clustered into seven groups within a time frame of 24 h, where each cluster for each output is repre- sented by TSK rules. The outputs Yj(X) must fit the data set. This can be achieved by modulating the nonlinear equation for each output yi. The modulation can be attained by tuning the parameters ai and bi. The offline tuning method is performed by using the GNMNR (GausseNewton Method for the Nonlinear Regression) algorithm, which has the capability to express the knowledge that is acquired from inputeoutput data in the form of layers of parameters. The Equation of the final model's outputs is characterised by aggre- gating the clusters' outputs and obtaining the singleton fuzzy model, which belongs to a general class of the universal model output. Subsequently, the outputs Yj(X) can be obtained as follows: Yj ðX Þ ¼ XN i uiai � 1� e�bix � (1) where X ¼ [x1, x2 … xm]T is the input variables vector, i is a rule number subscript, ai and bi are the Tagaki-SugenoeKang parame- ters functions, ui is the basis function (weight), and j is the cluster number subscript. The TSK model can be structured in layers f (x; ai bi) and the weights framework that is shown in Fig. 1 where f (x; ai bi) is a nonlinear function of the TSK parameters and the independent variable x. The TSKFF is modelled by collecting training data from the building and the HVAC system equipment. Learning of the pa- rameters in the TSKFFmodel is accomplished by the offline GNMNR algorithm. One of the advantages that the GNMNR algorithm offers is the real-time implementation of computational cost reduction. This is possible because the proposed method requires a lower number of iterations to perform the learning/training procedure; therefore, the tuning time will be reduced when it is implemented in real-time [5]. The controller method is realised by the TSKFF feed forward model to increase the response and time steady state control for the HVAC system. Additionally, the feed forward model is tuned online by using the gradient algorithm to enhance the stability and to reject the disturbances and uncertain factors. By using the gradient algorithm, a faster online tuning method is
  • Fig. 1. Schematic diagram of the TSK model as layers of memory. R.Z. Homod / Energy 74 (2014) 762e774 765 found that requires less mathematical manipulations than other methods do, such as the backpropagation method for neural net- works. Themost important aspect of this online tuning is that it can tune a multivariable controller with multiple outputs [11]. 2.2. Decoupling problem and objectives' setting The cooling coils in AHUs are categorised into dry andwet types. The temperature and relative humidity of air that is introduced to the AHU that has a dry cooling coil are characterised by coupling loops due to the constant air humidity ratio. Once the temperature is decreased, the relative humidity will be increased and vice versa. The thermal comfort can be controlled through the PMV index by using this type of AHU, with either air temperature or air relative humidity being a control variable (but not with both being control variables at the same time). The rest of the PMV variables are considered to be disturbances. It is desirable to control temperature and relative humidity independently and accurately in certain in- door conditions. In these cases, the AHU with a wet cooling coil is used; both temperature and RH are varied independently based on the flow rates of air and chilled water. It is impossible to set one variable without affecting the other when the design of the AHU does not take into account the coupling dynamics between these variables; therefore, the importance of decoupling techniques that are used to implement an appropriate AHU is realised. The proposed strategy is implementing a twin cooling coil AHU and an advanced multi-variable control system. The pre-cooling coil (wet) is equipped to cool and dehumidify the fresh air intake. The main cooling coil (dry) is used to cool the supply air. The deeply chilled water is only necessary for (pre-cooling coil) removing the moisture from the fresh air. The main cooling coil requires moderately cool water, according to the building load. This type of order helps in save energy for buildings with HVAC systems because higher chilled water temperatures indicate better COPs (coefficients of performance). Furthermore, the use of the PMV index (the indoor air temperature, the radiant temperature, the relative air velocity and the relative humidity for an indoor condi- tion space) as a desired objective enables the control system to optimise the input plant by controlling air velocity and manipu- lating the flow rate of fresh air in regard to thermal comfort levels. The main difference between the proposed strategy and the other two strategies is in their control objectives of the operating system and AHU equipment. The AHU for the conventional strategy is similar to what it is for proposed strategy, but there are two differences: first, it does not contain a pre-cooling coil, and second, the controlled variables include two variables that have restricted values. These variables are temperature and relative humidity; both of them are set at desired specific values. The objective of this control strategy acts as a control reference of the online tuning that reflected negatively on its performance due to stiff references and a limited number of input plant variables that are used for optimi- sation. The controlled variables for the third (adding the reheating coil) strategy are similar to those of the conventional strategy, but the difference is that the AHU is equipped with a wet main cooling coil and a reheating coil to consolidate the controller for the decoupling problem. The objectives of this paper are to: 1. Assess the feasibility of using the proposed strategy in a South Iraq climate 2. Characterise the energy savings and decoupling of the proposed system 3. Test the potential of the controller for multi-objective optimi- sation in the HVAC system. These aims will be achieved by comparing three scenarios of the AHU control system in order to assess the decoupling problem and energy savings of the simulated HVAC system. 3. Analysis of energy and mass flows of a building The purpose of the control strategy is to minimise the total power consumption of the HVAC system by optimising the vari- ables of the indoor thermal comfort (i.e., the indoor air tempera- ture, the radiant temperature, the relative air velocity and the relative humidity for the indoor condition space). Generally, the electric power consumption of the HVAC system is a function of the COP (coefficient of performance) of the chillers, the EER (energy efficiency ratio) of the building and the cooling load of the building. The EER and COP are constants for a specified building and chiller, respectively, whereas the total cooling loads of the building vary, depending on the disturbances and the controllable variables. Therefore, the total electric power consumption can be summarised by Equation (2) [26,27]: EP ¼ XN i chli copi þ EPAHU ¼ TBCL EER (2) where EP is the total electric power consumption, N is the number of chillers, chl is the chiller power, EPAHU is the electric power that is consumed by AHU, and TBCL is the total building's cooling load. From Equation (2), it can clearly be observed that the EP can be derived by using two different methods that are based on the energy and mass balance equations of the building's fabric (the right term of Equation (2)) and of the AHU subsystems' equipment (the middle term of Equation (2)). Therefore, the theories regarding the conservation of energy and mass are applied to thermally analyse and model the overall behaviour of an HVAC system. These theories are based on the fact that in the control volume of any subsystem, energy is transferred from/to a sub- system by two types of processes: mass transfer and conventional
  • R.Z. Homod / Energy 74 (2014) 762e774766 heat transfer (conduction, convection and radiation). These pro- cesses are dominant in HVAC systems. In this research study, the system is subdivided into the building's and the AHU's control volumes. The building's energy and mass transfer can be demon- strated by Fig. 2. To evaluate the sensible heat gain of the building, the following thermal balance equation is applied to the building's control volume: _Qs z}|{Cooling load ¼ _Qair þ _Q fur zfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflffl{Accumulation or storage of energy þ _Qopq þ _Q fen þ _Q slab þ _Q inf þ _Qig;s zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{Difference between input and output of energy (3) The term on the left side of Equation (3) denotes the output of the AHU, which represents the heat and mass that is transferred to the building's control volume. On the right side of Equation (3), the first part (the accumulation or storage of energy) represents the thermal mass that is stored in the inner wall, indoor air and furniture, while the second part (the difference between the input and output of energy) represents other inputs/outputs to the con- trol volume of the building. The latent heat gain of the building is related to the moisture transfer, which can be evaluated by applying the conservation of time-dependent mass law to the control volume of the building, which is shown in Equation (4); _ms � ur;t � us;t �zfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflffl{rate of moisture withdrawal by AHU ¼ dMrur;t dt zfflfflfflffl}|fflfflfflffl{ rate of moisture change þ _Qig;l hfg zffl}|ffl{rate of moisture generation þ _minfuo;t � _mrur;t zfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflffl{rate of moisture transfer (4) _mw;tcpw � Two;t � Twin;t �zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{energy absorbed by the coil ¼ MHecpHedTh;tdt zfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflffl{energy accumulation in the metal mass of coil þ _mo;tcpa � To;t � Tos;t �zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{sensible energy delivered by airþ _mo;t � uo;t � uos;t � hfg zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{latent energy delivered by air dehumidification (6) _mw;tcpw � Two;t �Twin;t �zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{energy absorbed by the coil ¼ MHecpHedTh;tdt zfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflffl{energy accumulation in the metal mass of coil þ _mo;tcpa � To;t �Tos;t �zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{sensible energy delivered by airþ _mcon:;thfg zfflfflfflfflfflffl}|fflfflfflfflfflffl{latent energy delivered by moisture withdrawal (5) The term on the left side of Equation (4) is the rate of moisture that is absorbed by the AHU. On the right side of Equation (4), the first part (the rate of moisture change) is the change in the rate of air moisture in the building at time interval dt, and the other terms are related to the indoor input/output and the generated moisture. To evaluate the sensible and latent heat gains of the building, it is necessary to calculate the left-hand sides of Equations (3) and (4), which can be obtained byapplying the laws of conservation of energy and mass to the control volume of the AHU. The AHU is subdivided into three subsystems: the mixing air chamber, the pre-cooling coil and themain cooling coil. Energy is only consumed in the pre-cooling andmaincoolingcoils, so calculations for theenergyandmass control volumes are applied on these two subsystems, as follows: The term “energy absorbed by the coil” in Equation (5) refers to the sensible and latent heat load that is exerted by the pre-cooling coil. On the right side of the equation, the first term (energy accumulation in the metal mass of the coil) refers to the rate of change for the heat storage in the coil mass, while the second term (the sensible energy delivered by air) refers to the sensible cooling load of the fresh air, and the third term (the latent energy delivered bymoisturewithdrawal) refers to the latent energy that is absorbed by the coil due to the condensation of moisture. The third term on the right side of Equation (5) can be evaluated by applying the law of mass conservation to the air flow stream that is used for the pre- cooling coil. The following is obtained: By using the same procedure as was used for the pre-cooling coil to obtain the sensible and latent heating loads for the dynamic subsystem equations, the main cooling coil can be written mathe- matically by using the time-dependent equation of the control volume, as follows:
  • Fig. 2. Representation of building energy and mass transfer for prototypical buildings with HVAC systems. _mmw;tcpw � Two;t � Twin;t �zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{energy absorbed by the coil ¼ MmHecpHedTh;tdt zfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflffl{ energy accumulation in the metal mass of coil þ _mm;tcpa � Tm;t � Ts;t �zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{sensible energy delivered by airþ _mm;t � um;t � us;t � hfg zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{latent energy delivered by air dehumidification (7) R.Z. Homod / Energy 74 (2014) 762e774 767 The rate of thermal energy transfer (the sensible cooling load) from the building by the mechanical ventilation air flow (Qvent) is calculated by using Equation (8). _Qvent ¼ _ms;tcpa � Tr;t � Ts;t � (8) The power of the air supply system in the mechanical ventila- tion state (the transmission power) is mainly from the power supply for the fan, which can be calculated by the application of the law of conservation of energy on the control volume of the AHU. This equation can be calculated as follows [27]: Fig. 3. The control signal percentage for the main cooling co _Q fan ¼ _ms;tcpa Ts;t � To;t (9) � � According to the energy balance for the indoor conditioned space of Equation (3), the values of thermal energy flow from (1) opaque-surfaces, (2) transparent fenestration surfaces, (3) infiltra- tion, (4) indoor load and (5) ventilation are calculated by using the steady state conditions of Equation (3), whereby all of the thermal energy flow values are equal to the cooling load that is extracted by the HVAC systems or the mechanical ventilation, which equals the left-hand side of Equation (3); in turn, Equation (3) can be calcu- lated by summing Equations (6)e(9). The instantaneous cooling load of the building can be obtained from the simulation process after modelling the HVAC system. Additionally, the instantaneous cooling loads of the building directly impact the outputs of the controller signals. Therefore, the method of calculation that is employed in this research study is based on the output signals of the controller. The output signals of the controller manipulate the valves of the pre-cooling coil, the main cooling coil, the reheating coil and the dampers of the return and fresh air to track the objective of the HVAC system. The valves and dampers are designed according to the heating/cooling load of the building. The opening position of the valves and dampers is recorded as a percentage of the fullest extent (as shown in Fig. 3) that represents the main cooling coil valve's opening position over 24 h. The percentage of the opening position is related to the maximum flow rate of the valves and dampers. This signal opening position is implemented in Matlab to obtain the energy con- sumption of the HVAC system. The advantage of using Matlab/Simulink is in the ability to use a graphical programming language that is based on different block categories with different properties of each block. Matlab and its il's chilled water valve for each of the three strategies.
  • Table 1 Properties of the materials used for construction of the model. Component Description Factors Roof/ceiling Flat wood frame ceiling (insulated with R-5.3 fiberglass) beneath vented attic with medium asphalt shingle roof U ¼ 0.031 18 W=ðm2KÞ a roof ¼ 0.85 Exterior walls Wood frame, exterior wood sheathing, interior gypsum board, R-2.3 fiberglass insulation U ¼ 51 W=ðm2KÞ Doors Wood, solid core U ¼ 2.3 W=ðm2KÞ Floor Slab on grade with heavy carpet over rubber pad; R-0.9 edge insulation to 1 m below grade Rcvr ¼ 0.21 W=ðm2KÞ; Fp ¼ 85 W=ðm2KÞ Windows Clear double-pane glass in wood frames. Half fixed, half operable with insect screens (except living room picture window, which is fixed). 0.6 m eave overhang on east and west with eave edge at same height as top of glazing for all windows. Allow for typical interior shading, half closed. Fixed: U ¼ 2.84 W=ðm2KÞ; SHGC ¼ 0.67. Operable: U ¼ 2.87 W=ðm2KÞ; SHGC ¼ 0.57; Tx ¼ 0.64; IACcl ¼ 0.6 Construction Good Aul ¼ 1.4 cm2/m2 R.Z. Homod / Energy 74 (2014) 762e774768 toolboxes are adopted to perform all of the identification processes and simulations in this work, as well as in our previous works [28e31]. System identification and control system toolboxes were used to identify and build the model, while the fuzzy logic toolbox was used for the TSK model identification. The obtained models are then introduced in the Matlab/Simulink environment for simula- tion and analysis. These categories include the input/output, transfer functions, arithmetic functions, state space models and data handling. The building model is represented in the form of ODE (ordinary differential equation) solvers, which are automati- cally configured during the Simulink model's run-time. The algo- rithm of the controller is designed by using Matlab m-files, parameter layer memory and S-functions, which are based on on- line parameter tuning. The technique for calculating the cooling loads is easily implementable, whereby the thermal balance Fig. 4. Matlab blocks for the simu equation is derived from the arithmetic functions, from which the energy consumption can be obtained. 4. Simulation results and discussion 4.1. Physical and theoretical model description The simulated building model is a typical one-story house with a simple structure. The house consists of heavyweight construction (brick and concrete) that measures 4.5 m in height, with 248.6 m2 of gross ground floor area. The net floor area of the entire building is 195.3 m2, excluding the garage area; the gross exposed area of the windows and wall is 126.2 m2, while the net area of the exterior wall is 108.5 m2. The overall volume of the house, excluding the garage and suspended ceiling space, is 781.2 m3 Table 1 shows the physical properties of the components of the building. The dry bulb temperature varies according to the spring season's climate in Basrah city, which ranges from 18 �C to 32 �C, and the humidity ratio varies from 0.01 to 0.01909 kg of moisture per kg of dry air. The buildingmodel's transfer function and the PMV, or thermal comfort sensor model, are presented in Appendices A and B [32,33]. To reduce the design cost, as well as the cost that is needed to fabricate the three HVAC systems, simulation methods are imple- mented in order to test and analyse the results. The identification approach of the model demonstration is based on the multi-zone model of the RLF (residential load factor) method. The identified model is simulated by three different controller strategies in order to study their levels of indoor thermal comfort and energy con- sumption. The first system is a conventional control system (the control variable objectives are temperature and relative humidity). The second is a conventional system that includes the addition of a reheating coil and awet main cooling coil, while the third system is similar to the first system, but it includes an addition of a pre- cooling coil and a PMV index in order to measure the objective of the controller. The three types of systems are run together in order to study their performance and energy consumption (as shown by the simulation block diagram in Fig. 4, which presents the simu- lation in the evaluation of performance and energy consumption of the three systems). lations of all three systems.
  • Fig. 5. PMV comparisons of the results of the three different systems with different objectives and designs. R.Z. Homod / Energy 74 (2014) 762e774 769 The mean radiant temperature is a more complicated quantity that depends on the temperature of the surrounding surfaces, as well as on angle factors of the surrounding surfaces. Therefore, the plant model leads to the output of the plug-in model of the PMV index, except the mean radiant temperature requires an interme- diate sub-model where its output is taken into account because it is one of the main factors that affects thermal comfort. This sub- model estimates the mean radiant temperature by using two methods: theoretical and numerical. For the theoretical method, the mean radiant temperature is estimated from the measured temperature of the surrounding walls and surfaces and the angle factors of these surrounding surfaces. All of the indoor surfaces are assumed to be black because most building materials have a high emittance ε, and it is assumed that small temperature differences exist between the surfaces of the enclosure (i.e., linear combination of system states). Therefore, the following equation is used [34]: MRT ¼ T1FP�1 þ T2FP�2 þ/þ TnFP�n (10) whereMRT is theMean Radiant Temperature, Tn is the temperature of surface ‘n’ and Fp-n is the angle factor between a person and surface ‘n’. Fig. 6. Indoor temperature comparisons of the results between the For the numerical estimation, a black-globe thermometer sensor is used. 4.2. Decoupling results and discussion The plant model is dynamically subjected by many thermal disturbance factors, such as the K2 solar radiation, f4 inside sensible, FDR fenestration, etc. Three simulation sets are conducted over 24 h and include nominal, noise and sensor deterioration, as well as an uncertainty operation, for the three systems' behaviours to be observed and studied for the different conditions. The main objective of this work is to validate the decoupling of the proposed strategy. 4.2.1. Nominal operating conditions Pre-cooling coils are added to the proposed AHU of the HVAC system in order to economically control the indoor relative hu- midity in a humid climate. Additionally, the proposed system has four control variables for an indoor conditioned space (i.e., the indoor-air temperature, the indoor-air velocity, the indoor-air hu- midity and the flow rate of fresh air). These control variables are optimised by the controller to provide economical indoor-air three different systems with different objectives and designs.
  • Fig. 7. Indoor relative humidity comparisons of the results between the three different systems with different objectives and designs. R.Z. Homod / Energy 74 (2014) 762e774770 conditioning that yields the desired level of thermal comfort and indoor air quality, according to ASHRAE and ISO standards; this also reduces the cooling load during implementation in real-time. The other systems have two control objectives that are set at certain desired values for the indoor conditioned space (i.e., the indoor-air temperature and the indoor-air humidity). Fig. 4 shows the three designs of the HVAC system. The manipulation of each TSKFF for the five AHU inputs in the three designs of the HVAC system be- haves differently. In the proposed system, the input feedback sensor allows some degree of tolerance instead of requiring a specific value for the temperature and relative humidity, which is needed in the conventional HVAC systems. This optimisation overcomes the coupling effects (temperature and relative humidi- ty) perfectly by providing the desired level of thermal comfort, which is shown in Fig. 5. In regard to Fig. 5, it can be observed that the proposed (the model of the PMV index addition) system can track the desired objective and can achieve outstanding perfor- mance, while the systemwith the added reheating coil acts within an acceptable thermal comfort range that has an acceptable offset Fig. 8. PMV comparisons of the results of the three different systems ba from the set point. The conventional system was found to violate the ASHRAE Standard 55-92 [35] and ISO-7730 [36] for the desired level of indoor thermal comfort. These standards recommend that the acceptable levels of thermal comfort are limited to a range between�0.5 < PMV < 0.5. It is evident that this violation is caused by the coupling of temperature and relative humidity. The tem- perature curves of all three systems are similar to the PMV trend that is shown in the simulation results, which are tabulated in Fig. 6. The periodic effect of the coupling factors is apparent from 05:30 o'clock to 08:00 o'clock and from 19:00 o'clock to 24:00 o'clock. The effect can be more clearly observed in the behaviour of the relative humidity (as shown in Fig. 7), in which the conven- tional system fluctuates within a wide range, whereas the other systems remain in the range of approximately 50% RH. The rec- ommended range of RH, according to the ASHRAE Standard 55-92 and ISO-7730 for the indoor comfort condition, is 40%e60%. High humidity not only causes poor indoor air quality, but it also causes wood decay, metal corrosion and structural deterioration [37]. The calculations of energy consumption are based on the controller sed on the operating conditions of noise and sensor deterioration.
  • Fig. 9. PMV comparisons of the results of the three different systems in regard to the operating conditions when model uncertainties are present. R.Z. Homod / Energy 74 (2014) 762e774 771 signals. One of these signals is shown in Fig. 3. Fig. 3 shows the results of the simulation of the control signal variation for the main cooling coil of the chilled water valve, with respect to time. In Fig. 3, the signals for the conventional system with a reheating coil acts similar to a BangeBang control action. The modulating valve continuously fluctuates between ON-OFF, which will eventually wear out the valve and shorten its lifespan. It can be clearly observed that the proposed system signal works very efficiently, which provides good control performance. Figs. 5e7 show the transient response for the initial condition. This took approximately an hour because the plant model is dynamically affected by the thermal mass of the building structure and slab floors, which cre- ates a flywheel effect. The influence of this flywheel effect begins to fade and becomes less intense after the HVAC system starts, which can clearly be observed in the signal of valve open position in Fig. 3. 4.2.2. Operating conditions of noise and sensor deterioration Disturbance mode has tested decoupling through its validation of the rejection of noise and sensor deterioration. In noise and sensor deterioration, the controlled process parameters, sensors' Fig. 10. Psychometric chart comparisons of the results of the three different system gains, and noise signals are able to change in the same manner for each system and simulation that is conducted. Here, we suppose that sensors deteriorate with 20% fault, and the sensors' gain is changed to 0.8 (sensor gain ¼ 1 when the sensor performance is 100%). Additionally, to test the sensitivity of the proposed method to noise, each system is subjected to the same noisy environment by adding a 10% NSR (noise-to-signal ratio), which refers to the ratio of the continuous noise signal to the controlled signal. The sensor deterioration set and subjected noise signal are applied at the start of the simulation. Fig. 8 shows the three different systems to try to track a PMV set point, which changes under a square wave from �0.4, 0 and 0.4 during a 24 h time frame. By using this test, one can clearly observe the three systems' behaviour for the PMV, where the proposed system provides superior control performance and does not violate the ASHRAE 55-92 and ISO-7730 Standards. In contrast, the other systems exhibited deterioration in their per- formances and, consequently, violated the Standards of the indoor thermal comfort. Thus, the proposed system achieves significant results that verify the use of decoupling parameters rather than adding a reheating coil or using conventional decoupling methods, s in regard to the operating conditions when model uncertainties are present.
  • Fig. 11. A comparison of the energy consumption results based on the cooling coil load variation between the three different schemes. R.Z. Homod / Energy 74 (2014) 762e774772 which are extremely intricate and too impractical to solve numer- ically when the plant system model is complicated, which is the case for HVAC systems. 4.2.3. Operating conditions regarding the presence of model uncertainties In regard to robustness validation, the plantmodel encompasses a wide range of operating parameters, which vary as the HVAC systems undergo fluctuating loads due to changes in external dis- turbances during a typical day's operation. Therefore, in the pres- ence of uncertainties regarding themodelling of such parameters, it becomes necessary to use a robust intelligent controller, such as TSKFF, to obtain efficient operation in the HVAC systems. To vali- date the robustness of the TSKFF controller, the building heat loss coefficients, the heat transfer coefficients of the fan-coil units and pumps and the thermal time constant are changed. Before a simulation run begins, all of the model coefficients and the time constant are increased by 20%. Three TSK models of controllers are tuned based on the nominal plant model and then are integrated into a control algorithm that manipulates AHU parameters to Fig. 12. A comparison of the results of the power consum control indoor thermal comfort. The conventional strategy leads to theworst indoor ambient conditions and becomes less intense after adding a reheating coil, which is shown in Fig. 9. However, both strategies violate the standard limitation of ASHRAE 55-92 and ISO- 7730. The proposed scheme maintains asymptotic tracking of a given reference signal, and it occurs in the presence of the same parameter variations and model uncertainties when it does not use reheating and wet main cooling coils. For validation, psychometric charts are the most commonly used tool for indoor studies and outdoor air conditions. Fig. 10 describes the air states cycle of physical and thermodynamic properties for indoor conditions over 24 h. The proposed strategy seems to satisfy the TCZ (thermal comfort zone), whereas the other strategies frequently crossover TCZ. 4.3. Energy saving results and discussion The purpose of the model of the PMV index addition to the proposed system is to change the restricted conventional objective variables (temperature and relative humidity) of an HVAC system in ption between the three different system schemes.
  • R.Z. Homod / Energy 74 (2014) 762e774 773 addition to increasing its flexibility with respect to the indoor control parameters (temperature, fresh air flow rate, indoor air velocity and relative humidity). The model of the PMV index addition also enables the controller to improve its performance. The TSKFF controller exploits the flexibility of the control param- eters by optimising the parameters through the manipulation of the AHU parameters (inputs) to provide the desired levels of ther- mal comfort, while simultaneously reducing the energy con- sumption of the HVAC system. Furthermore, the velocity of indoor air can reduce the cooling load. This can be observed from the simulation results of the energy that is consumed by the cooling coil load, which is shown in Fig. 11. The simulation techniques that are used to calculate the cooling loads are straight forward: thermal balance equations are imple- mented by using arithmetic functions, and then the consumed energy can be obtained by using Equations (6)e(9). The simulation results of the consumed energy in the systemwith a reheating coil reveal higher energy consumption than the consumption of the other systems because the cooling process reduces the air tem- perature to the sub-cooling state before the reheating process overcomes the coupling effect and meets the demands of indoor thermal comfort. Although the conventional system is better in terms of energy savings than the system that has the addition of a reheating coil, the conventional system does not meet the desired level of indoor thermal comfort. However, the proposed system shows more favourable results than the other two systems with respect to achieving the desired level of thermal comfort and reducing energy consumption, simultaneously. Based on Fig. 11, it can be observed that the differences in energy consumption among the three sys- tems increase during the times periods that include the presence of a coupling effect. The average power consumption for the three different systems (the conventional system, the addition of a reheating system and the proposed system) are 10.713 kW, 13.27 kW and 9.016 kW, respectively. The average power costs that accompany the addition of a reheating system are 1.4718 times higher than that of the proposed system. Based on the data for 24 h of power consumption, the calculations for energy consumption for each of the three strategies show that energy consumption in the proposed strategy is 32.06% lower than the system with an added reheating coil, which is shown in Fig. 12. This result closely matches the results that were obtained by Yang and Su [38] in which an intelligent controller was developed to adjust the PMV index, which led to saving approximately 30% more of the energy con- sumption than the conventional methods. Furthermore, the simu- lation of the cooling coil load output is compared to the numerical results, which are based on the CLF/CLTDC (the cooling load factor for the glass/corrected cooling load temperature difference) method [39,40]. The calculation considers the effects of numerous outdoor environmental parameters on the indoor thermal loads. The cooling load of the building is calculated every 30min to obtain the absolute margin of error between the simulation results (the proposed system) and the numerical calculation, which was found to vary between 0.064 and 0.107 kW. To have a clearer assessment of the error between the simulation and numerical calculations of the cooling coil load output, in this study, the statistical index of the coefficient of determination (r2) was calculated based on Equation (11) and had a value of r2 ¼ 0.974. r2 ¼ ½N P yiyi � ð P yiÞð P yiÞ�2h� N �P y2i � � ðP yiÞ2 �� N �P y2i � � ðP yiÞ2 �i (11) where yi is the numerical result, yi is the simulation result, and N is the number of test samples. 5. Conclusion In this context, the simulation results of the comparison inves- tigated the use of a PMVmodel input as an objective optimisation of controller decoupling and of reductions in the energy consumed by an HVAC system. This was performed by considering all of the factors that are affected by indoor thermal comfort, which are re- flected by the PMV index. The control system that is proposed in this work includes, as part of its structure, a PMV model for the optimisation of the deviations in the parameters of indoor thermal comfort and of the generation of control actions that pertain to the AHU inputs. The task regarding the PMV index output is, therefore, to acquire controller output signals more accurately by exploiting the decision algorithm's flexibility for the PMV index input's ag- gregation. The weather in Basrah, a southern city of Iraq, was considered as a case study to test the system. The output controller signals were adopted to obtain the energy consumption for three different control objectives and strategies, which were evaluated with respect to typical and modified HVAC systems. Based on the results of the performed simulations, we can conclude that when using the indoor PMV as a variable objective for the HVAC system, the controller performs better and provides more energy savings, while still attaining the desired level of indoor thermal comfort. The multi-input of the AHU is manipulated by the TSKFF controller, which is characterised by the optimisation of the outputs for energy savings. Both the conventional and proposed systems show that energy is saved in comparison to the system that has an additional reheating coil in the AHU. The conventional HVAC system shows a savings of up to 19% of energy usage, which is 61.5 kWh/d less than the energy that is used by an HVAC system that has an additional reheating coil; in contrast, the proposed strategy can save up to 32.06% of energy usage, which is 102.1 kWh/d less than the energy that is used by the HVAC system that has an additional reheating coil. This is because a TSKFF that is equipped with a model of PMV index reduces the energy consumption of a building by as much as it can by utilising the outdoor climate in controlling the rate of fresh air flow. Meanwhile, the conventional control strategy adjusts the temperature and relative humidity to a predefined strict set point, which does not allow for optimisation of energy consumption for indoor thermal comfort. An important finding of this study is that the proposed strategy economically addressed the coupling prob- lem in addition to providing the desired level of thermal comfort. Furthermore, the procedure of using the PMV model and TSKFF is straightforward and easy to implement. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// References [1] Anastaselos D, Theodoridou I, Papadopoulos AM, Hegger M. Integrated eval- uation of radiative heating systems for residential buildings. Energy 2011;36(7):4207e15. [2] Prek M. Thermodynamical analysis of human thermal comfort. Energy 2006;31(5):732e43. [3] Mossolly M, Ghali K, Ghaddar N. Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm. Energy 2009;34(1):58e66. [4] Chiu CC, Tsai NC, Lin CC. Near-optimal order-reduced control for A/C (air- conditioning) system of EVs (electric vehicles). Energy 2014;66(1):342e53. [5] Homod RZ, Sahari KSM. Intelligent HVAC control for high Energy efficiency in buildings. LAP LAMBERT Academic Publishing; 2014. [6] Tashtoush B, Molhim M, Al-Rousan M. Dynamic model of an HVAC system for control analysis. Energy 2005;30(10):1729e45. [7] Zhao L, Michelsen FA, Foss B. Control design and dynamic simulation of an HMR pre-combustion power cycle based on economic measures. Energy 2013;51(1):171e83.
  • R.Z. Homod / Energy 74 (2014) 762e774774 [8] Seem JE. A new pattern recognition adaptive controller with application to HVAC systems. Automatica 1998;34(8):969e82. [9] Homod RZ. Takagi-sugeno fuzzy modelling and adaptive control of indoor Thermal Comfort in HVAC systems using predicted mean vote Index. PhD Thesis. Kajang, Malaysia: University of Tenaga Nasional; 2012. [10] Gagnon E, Pomerleau A, Desbiens A. Simplified, ideal or inverted decoupling? ISA Trans 1998;37(4):265e76. [11] Homod RZ, Sahari KSM, Almurib HAF, Nagi FH. Gradient auto-tuned Takagi- Sugeno fuzzy forward control of a HVAC system using predicted mean vote index. Energy Build 2012;49:254e67. [12] Kusiak A, Li M. Reheat optimization of the variable-air-volume box. Energy 2010;35(5):1997e2005. [13] Lee J, Kim DH, Edgar TF. Static decouplers for control of multivariable pro- cesses. AICHE J 2005;51(10):2712e20. [14] Pomerleau D, Pomerleau A. Guidelines for the tuning and the evaluation of decentralized and decoupling controllers for processes with recirculation. ISA Trans 2001;40(4):341e51. [15] Wallter M, Wallter JB, Wallter KV. Decoupling revisited. Indus Eng Chem Res 2003;42(20):4575e7. [16] Tavakoli S, Griffin I, Fleming PJ. Tuning of decentralized PI (PID) controllers for TITO processes. Control Eng Pract 2006;14(9):1069e80. [17] Xi X-C, Poo A-N, Chou S-K. Support vector regression model predictive control on a HVAC plant. Control Eng Pract 2007;15(8):897e908. [18] Garrido J, V�azquez F, Morilla F. An extended approach of inverted decoupling. J Process Control 2011;21(1):55e68. [19] Chen P, Zhang W. Improvement on an inverted decoupling technique for a class of stable linear multivariable processes. ISA Trans 2007;46:199e210. [20] Seborg DE, Edgar TF, Mellichamp DA. Process dynamics & control. New York: John Wiley & Sons; 1989. [21] Shinskey FG. Process control systems: application, design and adjustment. New York: McGraw-Hill; 1988. [22] Wade HL. Inverted decoupling: a neglected technique. ISA Trans 1997;36(1): 3e10. [23] Shen Y, Cai W-J, Li S. Normalized decoupling control for high-dimensional MIMO processes for application in room temperature control HVAC sys- tems. Control Eng Pract 2010;18(6):652e64. [24] ANSI/ASHRAE Standard 62.1-2004 Ventilation for Acceptable Indoor Air Quality incorporates a number of significant changes to the previous stan- dard; 62-2001. [25] Kristl Z, Kosir M, T.Lah M, Krainer A. Fuzzy control system for thermal and visual comfort in building. Renew Energy 2008;33:694e702. [26] Haines RW, Wilson Lewis. HVAC systems design, handbook. 4th ed. New York: McGraw-Hill; 2003. [27] Wang SK. Handbook of air conditioning and refrigeration. 2nd ed. United States of America: McGraw-Hill Companies; 2001. [28] Homod RZ, Sahari KSM, Almurib HAF, Nagi FH. Double cooling coil model for non-linear HVAC system using RLF method. Energy Build 2011;43:2043e54. [29] Homod RZ, Sahari KSM, Mohamed HAF, Nagi F. Modeling of heat and moisture transfer in building using RLF method. In: Conference on research and development, IEEE; 2010. p. 287e92. [30] Homod RZ, Sahari KSM, Almurib HAF, Nagi FH. RLF and TS fuzzy model identification of indoor thermal comfort based on PMV/PPD. Build Environ 2012;49:141e53. [31] Homod RZ, Sahari KSM, Mohamed HAF, Nagi F. Hybrid PID-cascade control for HVAC system. Int J Syst Control 2010;1(4):170e5. [32] Homod RZ, Sahari KSM. Energy savings by smart utilization of mechanical and natural ventilation for hybrid residential building model in passive climate. Energy Build 2013;60:310e29. [33] ASHRAE. Psychrometrics, handbook-fundamentals, Chp. 6. TC 1.1, thermo- dynamic and psychrometric. American Society of Heating, Refrigerating, and Air-Conditioning Engineers; 2005. [34] ASHRAE. Handbook-fundamentals. Atlanta: American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc.; 2009. [35] ANSI/ASHRAE Standard 55(1992). Thermal environment conditions for hu- man occupancy. Atlanta: American Society of Heating, Refrigeration and Air- Conditioning Engineers; 1993. [36] ISO 7730. Moderate thermal environments e determination of the PMV and PPD indices and the specifications of the conditions for thermal comfort. Geneve, Suisse: International Standard Organization; 2005. [37] Lü X. Modelling of heat and moisture transfer in buildings: I. Model program. Energy Build 2002;34(10):1033e43. [38] Yang KH, Su CH. An approach to building energy savings using the PMV index. Build Environ 1997;32(1):25e30. [39] Karan B, Souma C, Ram MG. Development of CLTD values for buildings located in Kolkata, India. Appl Therm Eng 2008;28(10):1127e37. [40] Pita Edward G. Air conditioning principles and systems. 4th ed. New York: McGraw-Hill; 2002. Assessment regarding energy saving and decoupling for different AHU (air handling unit) and control strategies in the hot-h ... 1 Introduction 2 Control system design 2.1 TSKFF controller 2.1.1 Basic description of the control system 2.1.2 Model identification architecture 2.2 Decoupling problem and objectives' setting 3 Analysis of energy and mass flows of a building 4 Simulation results and discussion 4.1 Physical and theoretical model description 4.2 Decoupling results and discussion 4.2.1 Nominal operating conditions 4.2.2 Operating conditions of noise and sensor deterioration 4.2.3 Operating conditions regarding the presence of model uncertainties 4.3 Energy saving results and discussion 5 Conclusion Appendix A Supplementary data References


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