Application of Artificial Neural Network for the Optimum Control of HVAC

energies

Article Application of Artiﬁcial Neural Network for the Optimum Control of HVAC Systems in Double-Skinned Oﬃce Buildings

1, 1, 2 1, Byeongmo Seo † , Yeo Beom Yoon †, Jung Hyun Mun and Soolyeon Cho * 1 School of Architecture, College of Design, North Carolina State University, Raleigh, NC 27695, USA; [email protected] (B.S.); [email protected] (Y.B.Y.) 2 Sun & Light R&D Center, Seoul 06648, Korea; [email protected] * Correspondence: [email protected] These authors contributed equally to this work. † Received: 20 November 2019; Accepted: 11 December 2019; Published: 13 December 2019

Abstract: Double Skin Façade (DSF) systems have become an alternative to the environmental and energy savings issues. DSF offers thermal buffer areas that can provide benefits to the conditioned spaces in the form of improved comforts and energy savings. There are many studies conducted to resolve issues about the heat captured inside DSF. Various window control strategies and algorithms were introduced to minimize the heat gain of DSF in summer. However, the thermal condition of the DSF causes a time lag between the response time of the Heating, Ventilation, and Air-Conditioning (HVAC) system and cooling loads of zones. This results in more cooling energy supply or sometimes less than required, making the conditioned zones either too cold or warm. It is necessary to operate the HVAC system in consideration of all conditions, i.e., DSF internal conditions and indoor environment, as well as proper DSF window controls. This paper proposes an optimal air supply control for a DSF office building located in a hot and humid climate. An Artificial Neural Network (ANN)-based control was developed and tested for its effectiveness. Results show a 10.5% cooling energy reduction from the DSF building compared to the non-DSF building with the same HVAC control. Additionally, 4.5% more savings were observed when using the ANN-based control.

Keywords: Double Skin Facade; HVAC optimal control; EnergyPlus; load prediction; artiﬁcial neural network

1. Introduction There is currently international cooperation for the reduction of greenhouse gases under the paradigm of climate change. The international community also agreed that each country should reduce greenhouse gases to a speciﬁc target by 2020 [1]. According to the Energy Outlook 2019, the total carbon dioxide emissions in the US are classiﬁed into industrial, transportation, residential, and commercial. More than 50% of the electrical energy currently used in the US is produced via fossil fuels, the largest source of greenhouse gas emissions. Urban areas account for most fossil fuel consumption; in other words, most of the carbon dioxide generated by fossil fuels are generated from the energy use of urban space. Accordingly, considering the combined CO2 emissions from residential and commercial buildings that make up cities, the construction sector is expected to emit about 40% more carbon dioxide than the transportation sector. Moreover, compared to residential buildings in 2019, commercial buildings have about 10% higher carbon dioxide emissions, which is expected to remain steady and not decrease until 2050. These data stress that measures to reduce greenhouse gas emissions from commercial buildings need priority over residential buildings [2].

Energies 2019, 12, 4754; doi:10.3390/en12244754 www.mdpi.com/journal/energies Energies 2019, 12, 4754 2 of 22

The window factor is one of the main factors that increase the building energy consumption due to high U-value and solar heat gain. To solve this issue, many studies have been conducted on high-performance windows, smart windows, and shading systems in both industry and academic fields. Energy reduction through the Double Skin Façade (DSF) system has been verified by previous studies [3–16]. If a DSF system is installed in a building, the building has additional space, and it works as a thermal buffer. Through the thermal buffer, window heat loss is reduced in winter. Window heat loss is calculated by multiplying the U-value of the window, the area of the window, and the temperature differences between indoor and outdoor air. With the installation of the DSF system, the outside condition of the building is changed from the weather condition to the indoor air of the DSF system, which has a higher temperature than outdoor air in winter. Through these temperature differences, window heat loss is reduced with the installation of the DSF system. In summer, on the other hand, window opening control is needed to reduce cooling energy consumption by ventilating the hot air inside the DSF system to the outside [3]. Many researchers have conducted studies related to energy saving through DSF system installation. Yoon et al. analyzed heating energy savings by the installation of the DSF system in a high-rise apartment at different floor levels in Seoul, which is a heating dominant area. Results show that the first-floor apartment unit consumes the least heating energy, and the 25th floor consumes the most [4]. Joe et al. analyzed the impact of the DSF design, regarding glazing type and depth of the cavity, on the energy consumption of adjacent conditioned zones. The model in which the optimal DSF design is applied resulted in a 5.62% reduction in energy consumption [5]. Kim et al. conducted the simulation study about thermal and daylighting effects of the DSF system with interior and exterior blinds in Daejeon, South Korea, with the latitude of 36◦210 N and longitude of 127◦230 E. Results showed that the DSF system could save up to 40% for heating load, 2% for cooling load, and 5% for total loads compared to the base model. Also, the DSF system and the exterior blind models could potentially reduce the thermal loads and lighting energy consumption by around 27%–52% [6]. Shameri et al. argued that many experts widely agree that the DSF system is more cost-effective in the long run. Because it is long-lasting and more durable than a single glass facade, it is argued that the DSF system has great potential to reduce energy consumption in a wide range of research areas [7]. Raji et al. aimed to find energy-saving solutions for the envelope design of high-rise office buildings in the Netherlands climates. They selected glazing type, window-to-wall ratio, sun shading, and roofs for the energy saving solutions. They resulted in a high-performance envelope design that offers considerable energy-savings by around 42% for total energy use, 64% for heating, and 34% for electric lighting [8]. Joe et al. conducted an experimental study about the actual thermal behavior of a multi-story DSF in South Korea. The results showed savings of 15.8% for heating energy and 7.2% for cooling energy compared to the single skin facade [9]. Choi et al. measured the actual behavior of a multi-story DSF during the heating season in an office building. They found that significant energy savings are possible if the multi-story DSF is integrated with an HVAC system as a preheating space [10]. Chan et al. reported the findings on the energy performance of DSF applied to the office building in Hong Kong. The results showed that the DSF system with single clear glazing as the inner pane and double reflective glazing as the outer pane could save around 26% in building cooling energy, as compared to a single skin facade with single absorptive glazing [11]. Hamza Neveen compared the performance of the reflective DSF to a single skin façade. The DSF was predicted to decrease total annual cooling loads by approximately 30% in annual cooling loads [12]. Souza et al. compared temperature differences between outside air temperature, indoor air temperature, and temperature of the DSF system. The main result was that the DSF system had a positive effect on the indoor air temperature due to captured heat inside of the DSF system [13]. Zomorodian and Tahsildoost applied the DSF system with louver system to the office building to reduce the energy consumption of an office building in Teheran, Iran. They argued that the DSF system could reduce 14.8% of total building energy compared to the single-layer window system [14]. Gratia and Herde analyzed heating and cooling energy depending on the installed direction of the DSF. Energies 2019, 12, 4754 3 of 22

The cooling load was increased regardless of the installed direction of the DSF system even though the heating load was decreased [15]. Gelesz and Reith argued that the DSF system could reduce up to 12% of cooling energy in Central Europe, compared to the triple glazing system [16]. The previous studies related to the DSF window control are as follows. Moon et al. proposed a control method to improve indoor thermal comfort and energy efficiency in buildings equipped with the DSF system [17]. Moon et al. investigated the optimal control strategies to maintain the indoor thermal environment and improve energy efficiency based on the ANN-based control [18]. Moon et al. proposed an ANN-based thermal control method for the DSF system in winter [19]. Moon et al. investigated various thermal control algorithms for DSF windows opening cooling systems of buildings in summer. The fuzzy logic-based control algorithm can improve up to 49.4% of building energy efficiency, compared to other algorithms [20]. Moon proposed a control method that can provide an appropriate indoor thermal comfort through the ANN-based control of the cooling system and the DSF windows [21]. Moon et al. used the ANN model to investigate the thermal performance of buildings with the DSF system under various window opening conditions [22]. Alberto et al. suggested that the essential variable of the DSF system is the airflow path [23]. Most previous studies have analyzed that the installation of the DSF system can save the heating and cooling energy; however, there are only a few studies that deal with heating and cooling energy savings through the installation of the DSF system in terms of the optimal control of HVAC systems. The air temperature inside of the DSF system is changed because of the weather condition, such as air temperature, diffused solar radiation, and direct solar radiation changes in real-time. The air temperature inside of the DSF system affects the indoor heating and cooling loads, which result in a time lag of the HVAC system and indoor thermal conditions. Due to the time lag of the HVAC system and the thermal condition of the space, over operating and under operating occur out of the set-point temperature ranges [17–22]. Therefore, if the HVAC system is operated without considering the indoor thermal environment changes due to the thermal buffer, there is a possibility that the cooling and heating energy savings by the installation of the DSF system will be lower than expected. Therefore, to maximize the physical benefits of the DSF system, which is the building energy savings, every variable, constraint, and cooling and heating loads that change due to DSF’s thermal buffer must be accurately predicted and reflected in the HVAC system for optimal control. However, the existing temperature-based on/off control method for an HVAC system, which is a conventional HVAC system control method, has difficulty in estimating the future state of the system by considering all variables and accurately reflecting it in actual measurement or simulation [24–26]. For the future predictive control described above, HVAC system control based on the ANN, which is increasing in application to solve complex problems in various fields, is needed. The ANN has the ability to learn and analyze mapping relationships, including non-linear phenomena [24–26]. In addition, ANN enables more accurate prediction than the mathematical model through adaptability to external changes and enables accurate and efficient control based on this [26]. The main goal of this study is to predict the cooling load changes with the installation of the DSF system and to achieve cooling energy savings through optimal HVAC system controls. To predict the cooling loads, an ANN-based load prediction model is established using the Python program. An office building simulation model, which is based on ASHRAE Standard 90.1-2004, and a DSF system, which has been validated in the previous study, are developed by using the EnergyPlus simulation program. This study analyzes and compares the cooling energy consumptions between an ANN-based load prediction control and a conventional temperature fixed control. This study also proposes the optimal HVAC control method for an office building that has a DSF system installed. Energies 2019, 12, 4754 4 of 22

2. Simulation Modeling

2.1. Simulation Program This study used two software programs: EnergyPlus to build the simulation model and Python to develop an ANN model. EnergyPlus is a simulation program developed by the US Department of Energy; it combines the merits of Building Loads and System Thermodynamics (BLAST) in the load analysis part and the advantages of DOE-2 in the systems analysis part [27]. In addition, the EnergyPlus simulation program uses the heat balance calculation, which is recommended by ASHRAE; it also has the advantage of dynamic analysis of radiative, thermal conduction and convective heat transfer under an unsteady state. EnergyPlus can perform simulation analysis based on an integrated heat and mass balance that cannot be implemented in existing simulation programs [26,28,29]. Meanwhile, Python is an interpreted language developed by Amsterdam’s Guido Van Rossum in 1990. Python is used across the entire spectrum of social computing, including web programming, numerical computation, data analysis, object-oriented programming, Graphical User Interface (GUI) programming, system utility building, and software development [26,30].

2.2. Simulation Model The simulation model was implemented using the EnergyPlus simulation program. The left side of Figure1 shows the large o ffice building simulation model. The size of each floor of the building is 50 35 m (1750 m2), and the floor-to-floor height is 4 m. The three-story office building has a × 50% window-to-wall ratio (WWR). The size of the south zone, which is attached to the DSF system, is 45.5 4.5 m (204.75 m2) and is shown on the right side of Figure1. Air wall is used for the interior × wall between the interior zone and exterior zone for simulation modeling and between exterior zones to divide a thermal zone. To attach the DSF system to the base model, this paper selected the DSF system that has been calibrated in the previous study [6]. This study only attached the DSF system to the second floor, which is minimally affected by outdoor conditions. To determine how much cooling energy consumption can be saved with the installation of the DSF system, this paper only considered the south zone of the building to which the DSF system is attached. Moreover, to reduce cooling energy consumption, window opening control is necessary to vent out high-temperature air inside the DSF system. Two input parameters are necessary for proper window opening control: first, leakage values, and second, the window opening control schedule. For leakage values, we followed ASHRAE’s leakage values, which is 2.4 cm2/m2 for reference office room [6,31]. For the leakage values of the DSF system, we followed the same values used in the previous study [6]. The leakage value for walls without windows was set at 2.6 and at 4.1 cm2/m2 for walls with windows [6,32]. The DSF system is constructed as four zones to simulate airflows driven by buoyancy and wind pressure. The top zone of the DSF system is represented as a plenum zone. Horizontal openings are used for each horizontal surface between stacked zones within the DSF system. In this study, vertical opening is controlled by an open and close schedule. The vertical opening schedule is the same as the schedule used in the previous study [6] to simulate the DSF system. The vertical windows, which are located on the bottom and top of the DSF system, are opened when the inside air temperature of the DSF system is higher than the outdoor air temperature, if lower than the outdoor air temperature, then windows are closed. Discharge coefficient values Cd for the window opening are set to 0.65 and 0.2 for vertical and horizontal openings, respectively; these values were obtained from references [33,34]. Energies 2019, 12, 4754 5 of 22 Energies 2019, 12, x FOR PEER REVIEW 5 of 22

Figure 1. Simulation model. Figure 1. Simulation model. 2.3. Simulation Conditions 2.3. Simulation Conditions For this study, a double-glazed window was selected; it consists of 6 mm clear glass, 13 mm air, and 6For mm this clear study, glass. a double-glazed The frame is window an aluminum was selected; window it frameconsists with of 6 a mm thickness clear glass, of 0.0572 13 mm m andair, anda projection 6 mm clear length glass. of 0.0254The frame m from is an the aluminum glass window. window The frame construction with a thickness and material of 0.0572 properties m and ofa projectionthe simulation length model of 0.0254 are shownm from in the Table glass1. window Information. The on construction the window, and exterior material wall, properties interior of wall, the simulationﬂoor, ceiling, model and roofare shown are based in Table on ASHRAE 1. Information Standard on 90.1-2004the window, Climate exterior Zone wall, 1A, interior where it wall, is hot floor, and ceiling,humid. and The representativeroof are based city on isASHRAE Miami, Florida, Standard and 90.1-2004 the information Climate on Zone the DSF 1A, structure,where it suchis hot as and the humid.frame of The the representative DSF system and city the is exterior Miami, windowFlorida, ofand the the DSF information system, are on based the DSF on thestructure, previous such study as theto construct frame of thethe sameDSF system DSF system and the as in exterior the previous window study of the [6]. DSF The zonesystem, cooling are based set-point on the temperature previous study to construct the same DSF system as in the previous study [6]. The zone cooling set-point is at 24 ◦C in occupied hours. temperature is at 24 °C in occupied hours. Table 1. Construction Properties. Table 1. Construction Properties. U-Value Visible Solar Heat Gain Construction 2 Reference U-Value (W/m ) TransmittanceVisible CoeSolarﬃcient Heat Gain Construction Reference Exterior Wall(W/m2) 0.79Transmittance X XCoefficient Interior Wall 5.68 X X Exterior WallRaised Floor 0.79 1.74 X X X X ASHRAE Standard Interior WallCeiling Slab 5.68 1.88 X X X X 90.1-2004 (Climate Zone 1A) Roof 0.38 X X ExteriorRaised Floor window for the building 1.74 5.84 X 0.11 0.25 X ASHRAE Standard 90.1- Ceiling SlabDSF Frame 1.88 6.64 X X X X 2004[6] (Climate Exterior window for the DSF system 2.31 0.18 0.22 Roof 0.38 X X Zone 1A) Exterior window 5.84 0.11 0.25 for theThe building conditions of internal heat gain are shown in Table2. Figure2 shows the schedules of occupants, lights, and equipment during weekdays, which are based on the ASHRAE Standard 90.1-2004.DSF Frame The HVAC system 6.64 operates from 07:00 X to 22:00 on weekdays. X Exterior window [6] for the DSF 2.31 Table 2. Indoor0.18 condition. 0.22 system Type Input The conditions of internal heat gain are shown in Table 2. Figure 2 shows the schedules of 2 occupants, lights, and equipment duringPeople weekdays, which0.057 are based Person on/m the ASHRAE Standard 90.1- Light 11.840 W/m2 2004. The HVAC system operates fromEquipment 07:00 to 22:00 on weekdays.10.333 W /m2 Cooling set-point temperature 24 ◦C Table 2. Indoor condition.

Type Input People 0.057 Person/m2 Light 11.840 W/m2 Equipment 10.333 W/m2 Cooling set-point temperature 24 °C

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Figure 2. Internal heat gain and HVAC system schedules for weekdays. FigureFigure 2. 2. Internal Internal heat heat gain gain and and HVAC HVAC system system schedules schedules for for weekdays weekdays. . The HVAC system of the simulation model is an Air Handling Unit (AHU)-based conventional The HVAC system of the simulation model is an Air Handling Unit (AHU)-based conventional HVACThe system HVAC thatsystem consists of the of simulation a hot water model coil and is an a reheatAir Handling coil receiving Unit (AHU)-based hot water from conventional the district HVAC system that consists of a hot water coil and a reheat coil receiving hot water from the district HVACheating system system that and consists a cooling of coila hot receiving water coil chilled and watera reheat from coil the receiving district coolinghot water system, from the as shown district in heating system and a cooling coil receiving chilled water from the district cooling system, as shown heatingFigure 3system. The ideal and a load cooling system coil is receiving assigned chilled to the buildingwater from except the district for the cooling south zone. system, The as OA, shown RA, in Figure 3. The ideal load system is assigned to the building except for the south zone. The OA, RA, inand Figure EA refer3. The to ideal outdoor load air, system return is air, assigned and exhaust to the airbuilding in Figure except3. The for AHU the south consists zone. of The a mixing OA, RA, box, and EA refer to outdoor air, return air, and exhaust air in Figure 3. The AHU consists of a mixing andcooling EA refer coil, heatingto outdoor coil, air, and return supply air, fan. and The exhaus controlt air point in Figure in this 3. studyThe AHU is located consists after of thea mixing supply box, cooling coil, heating coil, and supply fan. The control point in this study is located after the box,variable cooling air volumecoil, heating fan, which coil, and is called supply AHU fan. discharge The control air. point in this study is located after the supplysupply variable variable air air volume volume fan, fan, which which is is called called AHU AHU discharge discharge air. air.

FigureFigure 3. 3. System SystemSystem diagram diagram diagram.. . 2.4. Three Simulation Cases 2.4.2.4. Three Three Simulation Simulation Cases Cases There are three simulation cases developed. Case 1 is the base case, which does not have the DSF ThereThere are are three three simulation simulation cases cases developed. developed. Case Case 1 1 is is the the base base case, case, which which does does not not have have the the DSF DSF system in the building and has a ﬁxed AHU discharge air temperature. To determine the physical systemsystem in in the the building building and and has has a a fixed fixed AHU AHU discha dischargerge air air temperature. temperature. To To determine determine the the physical physical advantageadvantage of of a a DSF DSF system, system, Case Case 2 2 has has a a DSF DSF syst systemem attached attached to to Case Case 1 1 and and has has an an AHU AHU discharge discharge

Energies 2019, 12, 4754 7 of 22 advantage of a DSF system, Case 2 has a DSF system attached to Case 1 and has an AHU discharge air temperature. For the proposed ANN-based HVAC system control, Case 3 applies ANN-based HVAC system control to Case 2. By comparing Case 1 and Case 2, the cooling energy savings due to the DSF installation can be analyzed. Meanwhile, by comparing Case 2 and Case 3, the additional cooling energy savings due to the ANN-based HVAC system control can be analyzed.

2.5. ANN Modeling In this study, we developed an ANN model using Numerical Python (NumPy) and Scientiﬁc Python (SciPy) libraries of Python in the same way as previous studies [26]. When designing the ANN model, it is essential to select the activate function depending on the application. The activate function is used to simulate the behavior of biological neurons. In this paper, we used the sigmoid function, which is commonly used for various activate functions. The sigmoid function calculates the result value between 0.00 and 1.00. The sigmoid function is used for non-linearly separable problems, such as load predictions and energy consumption predictions [18–22,26]. Before making the ANN model, the learning method should also be determined. This study used supervised learning, one of the learning methods commonly used in ANN-based prediction models. Supervised learning requires both “input” and “correct answer” datasets. “Correct answer” datasets are result values given to the ANN model. They are used to determine the reliability of the predicted or calculated result data from the input datasets given to the ANN model. In the case of a model for predicting cooling loads, input datasets consist of the outdoor dry bulb temperature, outdoor relative humidity, and solar radiation; in the ANN model, outputs are the predicted cooling loads. The learning process in supervised learning consists of updating the weight factors so that error is reduced by comparing the output values of the ANN model with the “correct answer” values. Therefore, when speciﬁc datasets are input, the ANN model, which has learned via supervised learning, processes the patterns of the input values and generates the calculated results, which are determined to be closest to “correct answer” datasets. In addition, each neuron is connected by weight factors. Each neuron adds the product of the input values and the weight factors that are connected and passes this value to the input value of the activate function. If the input values passed onto the previously calculated activate function are not large enough to exceed a certain threshold, it will not output anything. Conversely, if the input value given to the active function exceeds the threshold, the neuron is activated to transmit the data to the next step. Accordingly, ANN computes the results by analyzing the interaction between data, weight factors, and activate function [21,26].

2.6. Development Process of the Predictive ANN Model The predictive ANN model has two different parts: the ANN part and the control logic part. The ANN part consists of three steps, as shown on the left side of Figure4[ 26]. The purpose of the ANN part is to predict output datasets. The first step in predicting output datasets is selecting variables that are highly correlated with the output to be predicted. The correlation analysis of variables is needed to select the variables through objective indicators, and correlation analysis can be increased by the efficiency of training. Input variables are selected with a relatively high correlation by analyzing r2, which represents the correlation between all the computable input variables and the output variable [24–26,35]. The second step is training and testing. Initializing the weight factors, which is the heart of learning, is the starting point of training. Then, the sum of the values obtained by multiplying the input data by the weights is added to each node and bias. This value is input into the activate function to output values [24–26,35]. The error rate between the result of the ANN and the “correct answer” is reflected in the next training, and the training is repeated to reduce the error rate by the epoch value. Epoch represents the number of training repetitions. As mentioned above, the goal of learning is to get the weight factors that have the lowest error rate between the output of the ANN and the “correct answer”. Unlike the previous learning part, the test process does not adjust the weight factors Energies 2019, 12, x FOR PEER REVIEW 8 of 22

The last step is optimization. The optimization of ANN means adjusting hyper-parameters of the ANN model, such as the bias value, the learning rate, the number of hidden neurons, and the number of hidden layers. The purpose of optimization is to verify the predictive performance and reliabilityEnergies of2019 the, 12 ,ANN 4754 model. To confirm this, the statistical term or the coefficient of variance8 of 22of the root mean square error (CVRMSE) is used in conjunction with the tolerance range proposed in the Americanbased onSociety the error of rateHeating, between Refrigerating, the ANN results and and Air-Conditioning the “correct answer”; Engineers nonetheless, (ASHRAE) it confirms Guideline the 14. Thepredicted tolerance accuracy ranges rate (or of theerror ANN rates) model pertain that has to the been prediction trained. accuracy of the result of the ANN model comparedThe last step to the is optimization. result of the The EnergyPlus optimization data. of ANNThe closer means the adjusting CVRMSE hyper-parameters value is to 0%, of the betterthe prediction ANN model, performance such as the is. bias The value, ASHRAE the learning recommends rate, the numbera CVRMSE of hidden value neurons, of less than and the30% as the errornumber rate of tolerance hidden layers. of time The data. purpose This ofvalue optimization includes is the to verifyrecommended the predictive r2 value performance of 0.80 or and higher to indicatereliability correlation of the ANN [35]. model. If the To CVRMSE confirm this, value the ex statisticalceeds 30%, term the or theuser coe repeatsfficient a of series variance of operations of the to adjustroot meanthe hyper-parameter square error (CVRMSE) values isand used performs in conjunction the optimization with the tolerance process range until proposedthe tolerance in the value American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) Guideline is less than 30% or reaches a certain value (or goal). Equations (1) and (2) show the calculation 14. The tolerance ranges (or error rates) pertain to the prediction accuracy of the result of the ANN formulas of RMSE and CVRMSE, respectively [24]. model compared to the result of the EnergyPlus data. The closer the CVRMSE value is to 0%, the better predictionThe control performance logic part is.consists The ASHRAE of four recommendssteps, as shown a CVRMSE on the valueright ofside less of than Figure 30% 4. as The the errorfirst step of therate control tolerance logic of time part data. is gathering This value includesindoor and the recommendedoutdoor condition r2 value data of 0.80 through or higher experiments to indicate or simulationcorrelation models. [35]. If the CVRMSE value exceeds 30%, the user repeats a series of operations to adjust the hyper-parameterThe second step values of the andcontrol performs logic thepart optimization is predicting process the output until the through tolerance the value input is datasets, less than and this step30% oris reachesconnected a certain to the value ANN (or part. goal). In Equations this study, (1) and the (2) output show thedatasets calculation are the formulas cooling of loads. RMSE The thirdand step CVRMSE, is finding respectively the optimal [24 ].value for the controlled parameters to achieve control after finishing the optimizationThe control process. logic partIn this consists paper, of the four AHU steps, discharge as shown air on set-point the right temperature side of Figure is4. the The controlled first parameterstep of to the save control on logiccooling part energy is gathering consumption. indoor and Th outdoore final conditionstep is operating data through a cooling experiments system or using simulation models. an optimal controlled parameter.

Figure 4. Processes in the predictive ANN model. Figure 4. Processes in the predictive ANN model. The second step of the control logic part is predicting the output through the input datasets,

and this step is connected∑ to the ANN part. In this study, the output datasets are the cooling loads. 𝑅𝑀𝑆𝐸 = , (1) The third step is finding the optimal value for the controlled parameters to achieve control after finishing the optimization process. In this paper, the AHU discharge air set-point temperature is the controlled parameter𝐶𝑉𝑅𝑀𝑆𝐸 to save = on cooling , energy consumption. The final step is operating (2) a cooling system using an optimal controlled parameter.

∑ s P 2 𝐴 = , ( S M ) (3) RMSE = − interval , (1) N where, interval

Energies 2019, 12, 4754 9 of 22

RMSEperiod CV(RMSE) = , (2) Aperiod sP period Minterval Aperiod = , (3) Ninterval where, S = ANN model prediction value, M = EnergyPlus simulation results, Ninterval = Number of EnergyPlus results, and Aperiod = Measurement period average.

2.6.1. Subsubsection Initial ANN Model Development The datasets are divided into (1) learning and (2) verification datasets. Training data was only used in the training part, and test data was used in the test part because if the same dataset is used in both the training and test parts, the ANN can predict well for specific conditions, but it may not predict well when a new pattern data is used. We, therefore, implemented the training and testing datasets by randomly dividing the EnergyPlus simulation results at a 9:1 ratio to confirm the ANN’s adaptability to the new patterns that were not experienced in the training part. In addition, input variables were selected using nine variables with relatively high R2 values through correlation analysis with the zone cooling loads as follows: outdoor air dry-bulb temperature, outdoor air relative humidity, site diffuse solar radiation rate per area, direct solar radiation rate per area, DSF inside space mean air temperature, DSF inside space air relative humidity, office building occupancy schedule, office building equipment schedule, and office building lights schedule. The learning data for the ANN model was built using EnergyPlus. Data, including weekends, from June 1 to August 31, were collected and used. Figure5 and Table3 show the structure of the initial cooling load prediction model. The structure of the initial model has nine input nodes, one hidden layer, nine hidden nodes, and one output node. The sigmoid function was used as an activate function. The learning rate was set at 20%, which means that only 20% of the error is used to update the weight factors when an error of 1 occurs. The reason for not fully reflecting 100% of the error is that the optimal weight factors have been set using the gradient descent method. This method has advantages, especially when it has to cope with a function with multiple parameters. This method uses a step-by-step approach to find optimal answers; hence, choosing a learning rate that is too high or too low may not lead to the value that minimizes the error. Bias is a variable that controls how easily a neuron is activated. Epoch refers to the number of repetitions of learning. For example, when learning is performed with Epoch 100 and 1000 training data, the 1000 100 times weight factors update is completed. Compared to the test data, the CVRMSE × of the cooling loads predicted by the ANN model was 20.34%, which is within the acceptable tolerance range of 30% recommended in the ASHRAE Guideline 14.

Table 3. Initial ANN structure and parameter values.

Division Range Initial Values Number of Hidden Layers 1–n 1 Number of Hidden Neurons 1–n 9 Learning Rate 0.01–1.00 0.2 Epochs 1–n 300 Energies 2019, 12, x FOR PEER REVIEW 9 of 22

S = ANN model prediction value, M = EnergyPlus simulation results, Ninterval = Number of EnergyPlus results, and Aperiod = Measurement period average.

2.6.1. Subsubsection Initial ANN Model Development The datasets are divided into (1) learning and (2) verification datasets. Training data was only used in the training part, and test data was used in the test part because if the same dataset is used in both the training and test parts, the ANN can predict well for specific conditions, but it may not predict well when a new pattern data is used. We, therefore, implemented the training and testing datasets by randomly dividing the EnergyPlus simulation results at a 9:1 ratio to confirm the ANN’s adaptability to the new patterns that were not experienced in the training part. In addition, input variables were selected using nine variables with relatively high 𝑅 values through correlation analysis with the zone cooling loads as follows: outdoor air dry-bulb temperature, outdoor air relative humidity, site diffuse solar radiation rate per area, direct solar radiation rate per area, DSF inside space mean air temperature, DSF inside space air relative humidity, office building occupancy schedule, office building equipment schedule, and office building lights schedule. The learning data for the ANN model was built using EnergyPlus. Data, including weekends, from June 1 to August 31, were collected and used. Figure 5 and Table 3 show the structure of the initial cooling load prediction model. The structure of the initial model has nine input nodes, one hidden layer, nine hidden nodes, and one output node. The sigmoid function was used as an activate function. The learning rate was set at 20%, which means that only 20% of the error is used to update the weight factors when an error of 1 occurs. The reason for not fully reflecting 100% of the error is that the optimal weight factors have been set using the gradient descent method. This method has advantages, especially when it has to cope with a function with multiple parameters. This method uses a step-by-step approach to find optimal answers; hence, choosing a learning rate that is too high or too low may not lead to the value that minimizes the error. Bias is a variable that controls how easily a neuron is activated. Epoch refers to the number of repetitions of learning. For example, when learning is performed with Epoch 100 and 1000 training data, the 1000 × 100 times weight factors update is completed. Compared to the test data, the EnergiesCVRMSE2019 ,of12 ,the 4754 cooling loads predicted by the ANN model was 20.34%, which is within10 ofthe 22 acceptable tolerance range of 30% recommended in the ASHRAE Guideline 14.

Energies 2019, 12, x FOR PEER REVIEW 10 of 22

Table 3. Initial ANN structure and parameter values.

Division Range Initial Values Number of Hidden Layers 1–n 1 Number of Hidden Neurons 1–n 9

Learning Rate 0.01–1.00 0.2 Figure 5. Initial ANN structure. Epochs Figure 5. Initial ANN1–n structure. 300

2.6.2. Subsubsection Initial ANN Model Development Optimized Performance Analysis of the ANN2.6.2. ModelSubsubsection Initial ANN Model Development Optimized Performance Analysis of the ANN Model ANN model optimization refers to the process of ﬁnding optimal learning rates, hidden nodes, hiddenANN layers, model and optimization epochs to optimize refers to prediction the process performance. of finding optimal There are learning no known rates, steps hidden to determine nodes, thehidden values layers, of a hyper-parameter and epochs to optimize for any problem. prediction Currently, performance. the best There approach are isno to known check thesteps results to anddetermine adjust the values learning of rate,a hyper-parameter hidden nodes, for hidden any pr layers,oblem. and Currently, epoch values the best through approach trial-and-error is to check processes.the results and The adjust process the is learning repeated rate, until hidden the error nodes, rate hidden achieves layers, the and acceptable epoch values uncertainty through rangestrial- proposedand-error in processes. the ASHRAE The Guidelineprocess is 14.repeated In this until study, the about error 100,000 rate achieves optimization the acceptable trials were uncertainty conducted toranges achieve proposed model optimization.in the ASHRAE Figure Guideline6 conceptually 14. In this represents study, about the 100,000 optimization optimization process trials conducted were toconducted determine to theachieve optimal model combination optimization. in the Figure cooling 6 conceptually load predictive represents algorithm. the optimization In the optimization process process,conducted the to CVRMSE determine values the optimal were compared, combination and in thethe combinationcooling load thatpredictive showed algorithm. lower CVRMSE In the wasoptimization selected. process, the CVRMSE values were compared, and the combination that showed lower CVRMSEFigures was7 and selected.8 show the structure and accuracy rate of the optimized prediction model. Table4 includesFigures the hyper-parameter 7 and 8 show the values structure with and the accuracy lowest CVRMSE rate of the and optimized the possible prediction input ranges. model. After Table the optimization4 includes the process, hyper-parameter the CVRMSE values value with decreased the lowest to CVRMSE 4.63% from and 20.32%. the possible Notably, input the ranges. error After rate in the optimization process, the CVRMSE value decreased to 4.63% from 20.32%. Notably, the error rate the current model is lower than that of the earlier model. The results indicate that the ANN model in the current model is lower than that of the earlier model. The results indicate that the ANN model developed for load prediction can be used for HVAC optimal control. developed for load prediction can be used for HVAC optimal control.

FigureFigure 6. Structure of the python python programming programming code code for for the the optimization optimization process process..

Energies 2019, 12, 4754 11 of 22 Energies 2019, 12, x FOR PEER REVIEW 11 of 22

Energies 2019, 12, x FOR PEER REVIEW 11 of 22

Figure 7. Optimal ANN structure. FigureFigure 7.7. Optimal ANN structure.

Figure 8. Comparison of simulation data and ANN results. Figure 8. Comparison of simulation data and ANN results. Table 4. Optimal ANN structure and parameter values.

Division Range Optimize Values

Number of Hidden Layers 1–n 2 NumberFigure of Hidden 8. Comparison Neurons of Layer simulation 1 data1–n and ANN results. 11 Number of Hidden Neurons Layer 2 1–n 9 Learning Rate 0.01–1.00 0.1 Epochs 1–n 800

Energies 2019, 12, x FOR PEER REVIEW 12 of 22

Table 4. Optimal ANN structure and parameter values.

Division Range Optimize values Number of Hidden Layers 1–n 2 Number of Hidden Neurons Layer 1 1–n 11 Number of Hidden Neurons Layer 2 1–n 9 Learning Rate 0.01–1.00 0.1

Energies 2019, 12, 4754 Epochs 1–n 800 12 of 22

2.6.3. Initial ANN Model Development Optimized Performance Analysis of the ANN Model HVAC Control2.6.3. Initial Strategy ANN Based Model on Development the ANN Results Optimized Performance Analysis of the ANN Model HVAC Control Strategy Based on the ANN Results The determining method of the AHU discharge air temperature using the ANN-based cooling The determining method of the AHU discharge air temperature using the ANN-based cooling load prediction model in this study is as follows: load prediction model in this study is as follows: The appropriate AHU discharge air temperature is determined for each hour within the range The appropriate AHU discharge air temperature is determined for each hour within the range of 11.11 to 16.67 °C during the daytime, using the linear interpolation formula based on the cooling of 11.11 to 16.67 C during the daytime, using the linear interpolation formula based on the cooling load predicted by◦ the ANN model when the zone cooling set-point temperature is 24 °C. In addition, load predicted by the ANN model when the zone cooling set-point temperature is 24 C. In addition, the control logic is set such that the AHU discharge air temperature is fixed at 16.67 ◦°C because the the control logic is set such that the AHU discharge air temperature is ﬁxed at 16.67 C because the cooling load is low during nighttime when the cooling set-point temperature is 26.67 °C.◦ cooling load is low during nighttime when the cooling set-point temperature is 26.67 C. In summary, during the daytime, when the zone cooling set-point temperature is◦ 24 °C and the In summary, during the daytime, when the zone cooling set-point temperature is 24 C and the cooling load is high, the appropriate AHU discharge air temperature is determined according◦ to the cooling loadload. isDuring high, the the appropriate nighttime, AHUwhen dischargethe cooling air set-point temperature temperature is determined is 26.67 according °C, the toAHU the dischargecooling load. air temperature During the nighttime,is fixed at 16.67 when °C, the regardless cooling set-point of the amount temperature of the cooling is 26.67 load.◦C, the AHU discharge air temperature is ﬁxed at 16.67 ◦C, regardless of the amount of the cooling load. 3. Results Analysis and Discussions 3. Results Analysis and Discussions

3.1. Weather Weather Conditions This study used the typical meteorological year (TMY) weather data of Miami, one of the cooling-dominated regionsregions in in the the US. US. Figure Figure9 shows 9 shows the outdoor the outdoor air temperature air temperature and relative and humidity relative humidity patterns in Miami, Florida. The lowest outdoor air temperature is 5.3 °C, while the highest patterns in Miami, Florida. The lowest outdoor air temperature is 5.3 ◦C, while the highest is 35.35 ◦C. isThe 35.35 lowest °C. relative The lowest humidity relative is 20.42%, humidity and theis 20.42%, highest and is 100%. the Thehighest outdoor is 100%. air temperature The outdoor range air temperatureduring the summer range during season, the which summer is between season, June which 1 to is August between 31, June the analysis1 to August period 31, inthe this analysis paper, period in this paper, is 22.2–35.35 °C. The outdoor air relative humidity range during the analysis is 22.2–35.35 ◦C. The outdoor air relative humidity range during the analysis period is 40.42%–98.75%. period is 40.42%–98.75%.

Figure 9. OutdoorOutdoor air air dry-bulb dry-bulb temperature temperature and re relativelative humidity in Miami, Florida.

3.2. Analysis of the Summer Representative Day

3.2.1. Comparison of AHU Discharge Air Temperature Pattern on the Summer Representative Day For the summer representative day for this study, 21 July was chosen, which is a summer design day presented in the Miami weather ﬁle. Figure 10 shows the change in AHU discharge air temperature according to the cooling loads on the summer representative day. The pattern of the cooling loads on Energies 2019, 12, x FOR PEER REVIEW 13 of 22

3.2. Analysis of the Summer Representative Day

3.2.1. Comparison of AHU Discharge Air Temperature Pattern on the Summer Representative Day

EnergiesFor2019 the, 12 summer, 4754 representative day for this study, 21 July was chosen, which is a summer design13 of 22 day presented in the Miami weather file. Figure 10 shows the change in AHU discharge air temperature according to the cooling loads on the summer representative day. The pattern of the coolingthe summer loads representative on the summer day showedrepresentative similar day patterns showed in all similar cases, increasingpatterns in rapidly all cases, from increasing 09:00 and rapidlygradually from decreasing 09:00 and after gradually 17:00. The decreasing cooling loads after pattern17:00. The is similar cooling to loads the pattern pattern of theis similar internal toheat the patterngain schedules of the internal in Figure heat2. Thegain cooling schedules loads in atFigure 07:00 2. are The high cooling due to loads the thermostatat 07:00 are set high temperature due to the thermostatchange from set the temperature nighttime setback.change from the nighttime setback. The coolingcooling loads loads in in Case Case 2 and 2 and Case Case 3 are the3 are same the because same allbecause conditions all conditions are the same. are Meanwhile, the same. Meanwhile,the cooling loadsthe cooling in Case loads 1 are in higher Case 1 by are about higher 9.0% by than about those 9.0% of than Case those 2 and of Case Case 3. 2 Thisand trendCase 3. is Thisfurther trend described is further in Sectiondescribed 3.2.2 in. AllSection three 3.2.2. cases All have three the cases same have zone the cooling same set-point zone cooling temperature set-point of temperature24 ◦C, and the ofHVAC 24 °C, and system the operatesHVAC system from 07:00 operates to 22:00 from on 07:00 weekdays. to 22:00 However, on weekdays. for the However, set value for of the set AHU value discharge of the AHU air temperature, discharge air Cases temperature, 1 and 2 areCases ﬁxed 1 and at 11.112 are fixed◦C, regardless at 11.11 °C, of regardless cooling load of coolingchanges. load As changes. described As in described Section 2.6.3 in Section, in Case 2.6.3, 2, in although Case 2, although the cooling the loads cooling and loads the zoneand the cooling zone coolingset-point set-point temperatures temperatures change, change, the AHU the discharge AHU discharge air temperature air temperature is controlled is controlled between between 11.50 to 11.5015.47 ◦toC 15.47 from °C 07:00 from to 07:00 22:00 to by 22:00 cooling by loadcooling changes load changes when the when HVAC the system HVAC operates. system operates.

Figure 10. Comparison of the cooling loads and the AHU discharge air temperatures in each case on Figurethe summer 10. Comparison representative of the day. cooling loads and the AHU discharge air temperatures in each case on the summer representative day. Figure 11 shows the window heat gain rate on 21 July. The south zone with the DSF is 50 m in length and 4.5Figure m in 11 width. shows The the shapewindow of theheat south gain zonerate on is narrow21 July.and Thelong, south as zone shown with in the Figure DSF1 .is As 50 such, m in lengthwindow and heat 4.5 gain m in rate width. through The shape the exterior of the windowsouth zone has is a narrow dominant and e fflong,ect on as cooling.shown in As Figure previously 1. As such,shown window in Table heat1, the gain ASHRAE rate through Standard the 90.1-2004 exterior includeswindow ahas high a dominant U-value of effect 5.84 Won/ mcooling.2K for theAs previouslyexterior window. shown Asin Table a DSF 1, is the attached, ASHRAE the Standa U-valuerd 90.1-2004 of the exterior includes window a high is U-value changed of from5.84 W/m 5.842 toK for2.31 the W /exteriorm2K; a 600-mmwindow. air As cavity a DSF is is generated attached, by the the U-value DSF added. of the exterior window is changed from 5.84 toOne 2.31 of W/m the main2K; a 600-mm parameters air cavity of the is window generated heat by gain the DSF rate added. is the solar radiation rate. As solar radiationOne passesof the main through parameters the exterior of windowthe window of the heat DSF, gain direct rate solar is the turns solar into radiation diffuse solar, rate. andAs solar solar radiationheat gain passes in the zonethrough through the exteri the windowor window of Case of the 2 is DSF, considerably direct solar lower turns than into that diffuse of Case solar, 1 where and solar heat gain entersin the thezone room through directly the throughwindow theof Case window. 2 is considerably lower than that of Case 1 whereIn solar general, heat the gain inside enters temperature the room directly of the DSF through system the is window. higher than the outside air temperature in summer,In general, and this the can inside adversely temperature affect cooling of the DSF energy system consumption. is higher than Figure the 11 outside can explain air temperature this issue. in summer,The Outdoor and this Air can Temperature adversely (OAT)affect cooling shown inenergy Figure consumption. 12 refers to the Figure outside 11 can air temperature,explain this issue.while the PL refers to the plenum space of the DSF system. Top, Mid, and Bot represent the temperatures of the top, middle, and bottom spaces of the DSF system, respectively. The inside air temperature of the DSF system increases from bottom to top on the right side of Figure 12; however, the temperature difference is only 0.15 ◦C. The inside temperature of the DSF system is about 2 ◦C higher than the outside air temperature. In addition, the temperature range of the plenum space of the DSF system is between 24 to 40 ◦C in summer; it is higher than the inside air temperature of the DSF system and the Energies 2019, 12, x FOR PEER REVIEW 14 of 22

The Outdoor Air Temperature (OAT) shown in Figure 12 refers to the outside air temperature, while the PL refers to the plenum space of the DSF system. Top, Mid, and Bot represent the temperatures of the top, middle, and bottom spaces of the DSF system, respectively. The inside air temperature of the DSF system increases from bottom to top on the right side of Figure 12; however, the temperature difference is only 0.15 °C. The inside temperature of the DSF system is about 2 °C higherEnergies than2019, 12the, 4754 outside air temperature. In addition, the temperature range of the plenum space14 of of 22 the DSF system is between 24 to 40 °C in summer; it is higher than the inside air temperature of the DSF system and the outdoor air temperature. This is because the plenum space of the DSF system is directlyoutdoor affected air temperature. by solar radiation This is becauseduring the the su plenummmer when space the of themaximum DSF system solar isaltitude directly angle affected is 85°. by Duesolar to radiation this high duringsolar altitude the summer angle, when the roof the of maximum the plenum solar space altitude received angle solar is 85 radiation◦. Due to directly. this high Becausesolar altitude of the angle,plenum the space roof of thethe plenumDSF system, space the received inside solarair temperature radiation directly. of the DSF Because system of theis relativelyplenum space lower of than the DSFthe inside system, air the temperature inside air temperature of the plenum of thespace DSF ofsystem the DSF is system. relatively Inlower addition, than sincethe inside the plenum air temperature space of ofthe the DSF plenum system space faces of the the return DSF system. plenum In space addition, of the since office the building, plenum space the highof the temperature DSF system inside faces thethe returnplenum plenum space spacedoes not of thedirectly office affect building, the conditioned the high temperature zone. inside the plenumFrom space these does results, not directlyit can be a seenffect thethat conditioned the installation zone. of the DSF system reduces cooling energy due toFrom the plenum these results, space it of can the be DSF seen thatsystem the and installation the low of window the DSF systemheat gain. reduces In addition, cooling energywhen the due exteriorto the plenum windows space located of the at DSF the system top and and bottom the low windowof the DSF heat system gain. In are addition, opened, when the theinside exterior air temperaturewindows located of the at DSF the system top and is bottom decreased of the by DSF abou systemt 0.6 to are 1.8 opened, °C compared the inside to the air temperature temperature when of the windowsDSF system are is closed; decreased this byis because about 0.6 the to 1.8outside◦C compared air and the to the inside temperature air of the when DSF windowssystem are are mixed. closed; Therefore,this is because cooling the energy outside is air further and the reduced inside air by ofopening the DSF the system exterior are mixed.windows Therefore, of the DSF cooling system. energy is furtherThrough reduced these byresults, opening the thecooling exterior load windows patterns ofcan the be DSF interpreted system. and are shown in Figure 10. As shownThrough in Figure these results,12, the theinside cooling temperature load patterns of the can DSF be interpretedsystem is higher and are than shown the inoutside Figure air 10 . temperature;As shown in however, Figure 12 the, the DSF inside system temperature prevents sola of ther radiation DSF system from isreaching higher the than zone the directly outside in air thetemperature; daytime. That however, is why the the DSF cooling system loads prevents of Case solar 2 are radiation lower than from that reaching of Case the 1. zone directly in the daytime. That is why the cooling loads of Case 2 are lower than that of Case 1.

FigureFigure 11. 11. WindowWindow heat heat gain gain rate rate in in the the zone zone through through the the south south exterior exterior window. window. 3.2.2. Comparison of the Fan Mass Flow Rate on the Summer Representative Day Figure 13 shows the fan mass flow rate change according to AHU discharge air temperature change on the summer representative day. In Case 1, the maximum fan mass flow rate was 0.7373 kg/s at 15:00, and the minimum fan mass flow rate was 0.3450 kg/s at 22:00. In Case 2, the maximum fan mass flow rate was 0.5941 kg/s at 16:00, and the minimum fan mass flow rate was 0.3135 kg/s at 22:00. In Case 3, the maximum fan mass flow rate was 0.7401 kg/s at 17:00, and the minimum fan mass flow rate was 0.5366 kg/s at 07:00. Comparing the fan mass flow rates of Case 1 and Case 2, Case 2 required 14.3% less fan mass flow rate. Comparing fan mass flow rate between Case 1 and Case 3, Case 3 needed 13.2% more fan mass flow rate than Case 1. In Case 1 and Case 2, the cooling set-point temperature and AHU discharge air temperature are fixed, regardless of the change in cooling loads. The cooling loads are covered by increasing or decreasing the fan mass flow rate. Case 1 has a higher fan flow rate than that of Case 2 and Case 3 because Case 1 had relatively higher cooling loads than the other two cases during the summer representative day. However, in Case 3, the temperature difference between the zone cooling Energies 2019, 12, 4754 15 of 22

set-point temperature and the AHU discharge air temperature continuously changes based on the

Energiescooling 2019 loads,, 12, x FOR and PEER the fanREVIEW mass ﬂow rate is also increased or decreased to cover the cooling15 loads. of 22

Energies 2019, 12, x FOR PEER REVIEW 16 of 22

algorithm provided the optimal energy consumption scenarios through determining both AHU discharge air temperature and the supply air amount. Although Case 3 consumed higher fan energy, it used less cooling energy in total due to larger energy consumption reduction from increasing discharge airFigure temperatures 12. Comparison while of meeting inside air space temperature cooling with loads. the installation of the DSF system. Figure 12. Comparison of inside air temperature with the installation of the DSF system.

3.2.2. Comparison of the Fan Mass Flow Rate on the Summer Representative Day Figure 13 shows the fan mass flow rate change according to AHU discharge air temperature change on the summer representative day. In Case 1, the maximum fan mass flow rate was 0.7373 kg/s at 15:00, and the minimum fan mass flow rate was 0.3450 kg/s at 22:00. In Case 2, the maximum fan mass flow rate was 0.5941 kg/s at 16:00, and the minimum fan mass flow rate was 0.3135 kg/s at 22:00. In Case 3, the maximum fan mass flow rate was 0.7401 kg/s at 17:00, and the minimum fan mass flow rate was 0.5366 kg/s at 07:00. Comparing the fan mass flow rates of Case 1 and Case 2, Case 2 required 14.3% less fan mass flow rate. Comparing fan mass flow rate between Case 1 and Case 3, Case 3 needed 13.2% more fan mass flow rate than Case 1. In Case 1 and Case 2, the cooling set-point temperature and AHU discharge air temperature are fixed, regardless of the change in cooling loads. The cooling loads are covered by increasing or decreasing the fan mass flow rate. Case 1 has a higher fan flow rate than that of Case 2 and Case 3 because Case 1 had relatively higher cooling loads than the other two cases during theFigureFigure summer 13. 13. ComparisonComparison representative of of fan fan massday. mass flowHowever, ﬂow rate rate accordin according in Caseg to 3, AHU the temperature discharge air differencetemperature between change. the zone cooling set-point temperature and the AHU discharge air temperature continuously changes based3.2.3. on Comparison the cooling of loads, the Pump and theChilled fan massWater flow (CHW) rate Mass is also Flow increased Rate on or the decreased Summer to cover the coolingRepresentative loads. Day In summary, the AHU discharge air setpoint temperatures in Cases 2 and 3 are different even Figure 14 shows the pump CHW mass flow rate change according to AHU discharge air though both cases have the same cooling loads. The AHU discharge air setpoint temperature in Case temperature change on the summer representative day. In Case 1, the average pump CHW mass flow 3 is often higher than Case 2 due to the ANN-based control, which resulted in cooling energy savings. rate was 0.4371 kg/s during the HVAC system operation. In Case 2, the average pump CHW mass However, Case 3 consumed more fan energy than Case 2 to meet the cooling loads. The ANN flow rate was 0.3785 kg/s. In Case 3, the average pump CHW mass flow rate was 0.2696 kg/s. Comparing the pump CHW mass flow rate of Case 1 and Case 2, Case 2 required 13.4% less pump CHW mass flow rate. Comparing the pump CHW mass flow rate of Case 1 and Case 3, Case 3 required 38.3% less pump CHW mass flow rate. The pump CHW mass flow rate is determined by the supply airflow rate and temperature differences between the AHU discharge air temperature and air temperature coming from the mixing box. The temperature differences between the air temperature from the mixing box and the AHU discharge air temperature increase when the latter is decreased, thus reducing the supply airflow rate but increasing the amount of heat that should be removed from the cooling coil. The pump CHW mass flow rate is increased to remove the increased amount of heat due to high-temperature differences between the air temperature from the mixing box and AHU discharge air temperature. Conversely, temperature differences between air temperature from the mixing box and AHU discharge air temperature decrease, thus increasing the supply airflow rate but decreasing the amount of heat that should be removed from the cooling coil. The pump CHW mass flow rate is decreased to remove the decreased amount of heat due to low-temperature differences between the air temperature from the mixing box and AHU discharge air temperature. Compared to Cases 1 and 2, as the AHU discharge air temperature in Case 3 increases, the amount of heat that should be removed from the cooling coil decreases. The pump CHW mass flow rate of Case 3 is, therefore, less than that of Cases 1 and 2.

Energies 2019, 12, 4754 16 of 22

In summary, the AHU discharge air setpoint temperatures in Cases 2 and 3 are diﬀerent even though both cases have the same cooling loads. The AHU discharge air setpoint temperature in Case 3 is often higher than Case 2 due to the ANN-based control, which resulted in cooling energy savings. However, Case 3 consumed more fan energy than Case 2 to meet the cooling loads. The ANN algorithm provided the optimal energy consumption scenarios through determining both AHU discharge air temperature and the supply air amount. Although Case 3 consumed higher fan energy, it used less cooling energy in total due to larger energy consumption reduction from increasing discharge air temperatures while meeting space cooling loads.

3.2.3. Comparison of the Pump Chilled Water (CHW) Mass Flow Rate on the Summer Representative Day Figure 14 shows the pump CHW mass flow rate change according to AHU discharge air temperature change on the summer representative day. In Case 1, the average pump CHW mass flow rate was 0.4371 kg/s during the HVAC system operation. In Case 2, the average pump CHW mass flow Energiesrate was 2019 0.3785, 12, x FOR kg/ s.PEER In CaseREVIEW 3, the average pump CHW mass flow rate was 0.2696 kg/s. 17 of 22

FigureFigure 14. 14. ComparisonComparison ofof pump pump CHW CHW mass mass flow flow rate rate according according to AHU to AHU discharge discharge air temperature air temperature change. change. Comparing the pump CHW mass flow rate of Case 1 and Case 2, Case 2 required 13.4% less pump 3.2.4.CHW Comparison mass flow rate. of the Comparing Cooling Energy the pump Cons CHWumption mass flowon the rate Summer of Case Representative 1 and Case 3, Case Day 3 required 38.3% less pump CHW mass flow rate. TableThe pump 5 shows CHW the cooling mass flow energy rate consumption is determined in byeach the case supply on the airflow summer rate representative and temperature day. Casesdifferences 1 and between2 consumed the CHW AHU about discharge 200.26 air kWh/day temperature and 182.13 and airkWh/day. temperature Case 3 comingconsumed from CHW the aboutmixing 172.68 box. kWh/day on the summer representative day. Comparing the CHW consumption of Case 1 andThe Case temperature 2, Case 2 consumed differences 9.1% between less CHW. the air Comparing temperature the fromCHW the of Case mixing 1 and box Case and 3, the Case AHU 3 consumeddischarge 13.8% air temperature less, and Case increase 3 consumed when the 5.2% latter less isCHW decreased, when compared thus reducing with Case the supply 2. Controlling airflow therate AHU but increasingdischarge air the temperature amount of through heat that the should ANN bemodel removed showed from a significant the cooling reduction coil. The in CHW pump consumptionCHW mass flow compared rate is increasedwith fixed toAHU remove discharge the increased air temperature. amount ofIn heatall cases, due toCHW high-temperature consumption accounteddifferences for between the large the part air of temperature the cooling fromenergy. the mixing box and AHU discharge air temperature. Conversely,Table 5 temperatureincludes the di chillerfferences electricity between consumpt air temperatureion with from the the assumption mixing box of and the AHU coefficient discharge of performanceair temperature (COP) decrease, of five thus for increasingchillers. This the supplyCOP value airflow is retrieved rate but decreasing as a default the value amount from of heatthe EnergyPlusthat should beprogram. removed When from all the electricity cooling coil. consumptions The pump CHW are combined mass flow for rate chiller, is decreased fan, and to removepump, Casethe decreased 2 showed amount 9.4% cooling of heat energy due to savings low-temperature and Case di3 ff12.9%erences compared between to the those air temperatureof Case 1. In from the casethe mixingof fans, box Case and 3 consumed AHU discharge the largest air temperature. energy for the reasons described in Section 3.2.3.

Table 5. Comparison of the cooling energy consumption in each case on the summer representative day.

Case 1 Case 2 Case 3 CHW use (kWh/day) 200.26 182.13 172.68 Chiller (COP 5) electric (kWh/day) 40.05 36.43 34.54 Fan electric (kWh/day) 2.65 2.34 3.13 Pump electric (kWh/day) 1.79 1.55 1.10 Total cooling energy (Chiller + Fan + Pump) (kWh/day) 44.49 40.32 38.77

Energies 2019, 12, 4754 17 of 22

Compared to Cases 1 and 2, as the AHU discharge air temperature in Case 3 increases, the amount of heat that should be removed from the cooling coil decreases. The pump CHW mass ﬂow rate of Case 3 is, therefore, less than that of Cases 1 and 2.

3.2.4. Comparison of the Cooling Energy Consumption on the Summer Representative Day Table5 shows the cooling energy consumption in each case on the summer representative day. Cases 1 and 2 consumed CHW about 200.26 kWh/day and 182.13 kWh/day. Case 3 consumed CHW about 172.68 kWh/day on the summer representative day. Comparing the CHW consumption of Case 1 and Case 2, Case 2 consumed 9.1% less CHW. Comparing the CHW of Case 1 and Case 3, Case 3 consumed 13.8% less, and Case 3 consumed 5.2% less CHW when compared with Case 2. Controlling the AHU discharge air temperature through the ANN model showed a significant reduction in CHW consumption compared with fixed AHU discharge air temperature. In all cases, CHW consumption accounted for the large part of the cooling energy. Table5 includes the chiller electricity consumption with the assumption of the coe fficient of performance (COP) of five for chillers. This COP value is retrieved as a default value from the EnergyPlus program. When all electricity consumptions are combined for chiller, fan, and pump, Case 2 showed 9.4% cooling energy savings and Case 3 12.9% compared to those of Case 1. In the case of fans, Case 3 consumed the largest energy for the reasons described in Section 3.2.3.

Table 5. Comparison of the cooling energy consumption in each case on the summer representative day.

3.3. Analysis of Cooling Energy Consumption in the Summer Season Figure 15 shows the total cooling energy consumption from 1 June to 31 August, which is the summer season, for the three cases. Case 2 consumed about 10.5% less cooling energy than Case 1. In addition, Case 3 consumed about 15.0% less energy than Case 1 and 4.5% less than Case 2. As described above, this paper used the district cooling system; however, we assumed the COP of the chiller as five, and we calculated chiller electric energy consumption to compare the total cooling energy consumption. Fan and pump energy consumption can be interpreted in Sections 3.2.2 and 3.2.3. The detailed analysis of the cooling energy consumption depending on each case can be interpreted in Figures 16–19. Figures 16–18 show the zone mean air temperature and relative humidity of the south zone in each case during the occupied hours, which are between 07:00 to 22:00, from 1 June to 31 August. All cases showed pleasant and appropriate relative humidity values. In Case 1, the indoor air temperature is decreased to 21.9 ◦C, which is about up to 2.1 ◦C lower than the thermostat setpoint temperature. In Case 2, the indoor air temperature is decreased to 22.5 ◦C, which is about up to 1.5 ◦C lower than the thermostat setpoint temperature. However, in Figure 18, when the ANN-based AHU discharge air temperature control is applied to the building where the DSF system is installed, the indoor air temperature converges at the cooling set temperature, which is 24 ◦C in the summer season. This is considered to be a result of the fan minimum air flow rate determination method described in Section 3.2.2. Air is continuously supplied to the room, and the amount of supplied air is about 25% of the maximum fan air mass flow rate. In Cases 1 and 2, the AHU discharge air temperature is fixed at 11.11 ◦C, and the maximum fan supply air mass flow rate is determined according to the maximum cooling loads. The determined maximum fan supply air mass flow rate affects the minimum fan supply air mass flow rate. Accordingly, Energies 2019, 12, 4754 18 of 22

even if the cooling loads are low, in Cases 1 and 2, air with a temperature of 11.11 ◦C is supplied to the zone as the minimum supply fan air mass flow rate. Thus, Figures 16 and 17 are interpreted as the minimum fan air mass flow rate and fixed AHU discharge air temperature. In Case 3, where indoor air temperature converges to the thermostat setpoint temperature, it can be interpreted in Figure 19. Figure 19 represents cumulative hours and hourly average cooling loads depending on each range of the AHU discharge air temperature in occupied hours in Case 3. The AHU discharge air temperature in Case 3 was divided into six ranges for detailed analysis. As shown in Figure 19, in Case 3, the AHU discharge air temperature is changed depending on the cooling loads. Therefore, in Case 3, when the required cooling loads are low, the AHU discharge air temperature is Energies 2019, 12, x FOR PEER REVIEW 18 of 22 increased. This is why overcooling does not occur in Case 3, unlike Cases 1 and 2.

Energies 2019, 12, x FOR PEER REVIEW 19 of 22 FigureFigure 15. 15. ComparisonComparison of of the the total total cooling cooling energy energy consumption. consumption.

Figures 16–18 show the zone mean air temperature and relative humidity of the south zone in each case during the occupied hours, which are between 07:00 to 22:00, from 1 June to 31 August. All cases showed pleasant and appropriate relative humidity values. In Case 1, the indoor air temperature is decreased to 21.9 °C, which is about up to 2.1 °C lower than the thermostat setpoint temperature. In Case 2, the indoor air temperature is decreased to 22.5 °C, which is about up to 1.5 °C lower than the thermostat setpoint temperature. However, in Figure 18, when the ANN-based AHU discharge air temperature control is applied to the building where the DSF system is installed, the indoor air temperature converges at the cooling set temperature, which is 24 °C in the summer season. This is considered to be a result of the fan minimum air flow rate determination method described in Section 3.2.2. Air is continuously supplied to the room, and the amount of supplied air is about 25% of the maximum fan air mass flow rate. In Cases 1 and 2, the AHU discharge air temperature is fixed at 11.11 °C, and the maximum fan supply air mass flow rate is determined according to the maximum cooling loads. The determined maximum fan supply air mass flow rate affects the minimum fan supply air mass flow rate. Figure 16. Indoor air temperature and relative humidity of Case 1 in the summer season. Accordingly,Figure even 16. if Indoor the cooling air temperature loads are and low, relative in Cases humi 1 dityand of2, Caseair with 1 in thea temperature summer season. of 11.11 °C is supplied to the zone as the minimum supply fan air mass flow rate. Thus, Figures 16 and 17 are interpreted as the minimum fan air mass flow rate and fixed AHU discharge air temperature. In Case 3, where indoor air temperature converges to the thermostat setpoint temperature, it can be interpreted in Figure 19. Figure 19 represents cumulative hours and hourly average cooling loads depending on each range of the AHU discharge air temperature in occupied hours in Case 3. The AHU discharge air temperature in Case 3 was divided into six ranges for detailed analysis. As shown in Figure 19, in Case 3, the AHU discharge air temperature is changed depending on the cooling loads. Therefore, in Case 3, when the required cooling loads are low, the AHU discharge air temperature is increased. This is why overcooling does not occur in Case 3, unlike Cases 1 and 2.

Figure 17. Indoor air temperature and relative humidity of Case 2 in the summer season.

Figure 18. Indoor air temperature and relative humidity of Case 3 in the summer season.

Energies 2019, 12, x FOR PEER REVIEW 19 of 22

Figure 16. Indoor air temperature and relative humidity of Case 1 in the summer season. Energies 2019, 12, 4754 19 of 22 Figure 16. Indoor air temperature and relative humidity of Case 1 in the summer season.

Figure 17. Indoor air temperature and relative humidity of Case 2 in the summer season. Figure 17. Indoor air temperature and relative humidity of Case 2 in the summer season. Figure 17. Indoor air temperature and relative humidity of Case 2 in the summer season.

Energies 2019, 12, x FOR PEER REVIEW 20 of 22 Figure 18. Indoor air temperature and relative humidity of Case 3 in the summer season. Figure 18. Indoor air temperature and relative humidity of Case 3 in the summer season. Figure 18. Indoor air temperature and relative humidity of Case 3 in the summer season.

Figure 19. Cumulative operation hours and hourly average cooling load in each AHU discharge air Figuretemperature 19. Cumulative range. operation hours and hourly average cooling load in each AHU discharge air temperature range.

4. Conclusion The goal of this study was to verify potential cooling energy savings by applying ANN-based control of AHU discharge air temperature in DSF office buildings in a hot and humid climate. The ANN model predicted cooling loads within the CVRMSE value of 4.6% after the optimization process, which is within the tolerance range of 30% recommended by the ASHRAE Guideline 14. It was observed that a considerable amount of cooling energy could be saved from using ANN-based control compared to the simple fixed control method. Results showed that 10.5% of cooling energy savings can be achieved with the installation of the DSF itself to a non-DSF building. Additional cooling energy savings of 4.5% can be realized by applying an ANN-based AHU discharge air temperature control method to the DSF building. This study observed that overcooling occurred with the conventional HVAC control method due to the minimum fan air mass flow rate and the fixed AHU discharge air temperature. By the application of ANN-based AHU discharge air temperature control methods, the indoor air temperature is constantly maintained to the thermostat set temperature of 24.0 °C with minimal variations. Cooling energy savings were obtained from the removal of overcooling hours, which were possible from the optimal control of discharge air temperature using the ANN-based control algorithms. This paper presented energy savings results for the cooling season only. Because the air temperature inside the DSF system is higher than the outdoor air temperature, heating energy can also be saved in winter. In the future, annual energy savings analyses will be conducted, including cooling, heating, and swing seasons altogether by applying advanced ANN-based control algorithms. Also, this paper focused only on one floor in a multi-story building; however, a whole building energy analysis will be conducted in the future.

Funding: This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20172010000370).

Conflicts of Interest: The authors declare no conflict of interest.

Energies 2019, 12, 4754 20 of 22

4. Conclusions The goal of this study was to verify potential cooling energy savings by applying ANN-based control of AHU discharge air temperature in DSF office buildings in a hot and humid climate. The ANN model predicted cooling loads within the CVRMSE value of 4.6% after the optimization process, which is within the tolerance range of 30% recommended by the ASHRAE Guideline 14. It was observed that a considerable amount of cooling energy could be saved from using ANN-based control compared to the simple fixed control method. Results showed that 10.5% of cooling energy savings can be achieved with the installation of the DSF itself to a non-DSF building. Additional cooling energy savings of 4.5% can be realized by applying an ANN-based AHU discharge air temperature control method to the DSF building. This study observed that overcooling occurred with the conventional HVAC control method due to the minimum fan air mass flow rate and the fixed AHU discharge air temperature. By the application of ANN-based AHU discharge air temperature control methods, the indoor air temperature is constantly maintained to the thermostat set temperature of 24.0 ◦C with minimal variations. Cooling energy savings were obtained from the removal of overcooling hours, which were possible from the optimal control of discharge air temperature using the ANN-based control algorithms. This paper presented energy savings results for the cooling season only. Because the air temperature inside the DSF system is higher than the outdoor air temperature, heating energy can also be saved in winter. In the future, annual energy savings analyses will be conducted, including cooling, heating, and swing seasons altogether by applying advanced ANN-based control algorithms. Also, this paper focused only on one floor in a multi-story building; however, a whole building energy analysis will be conducted in the future.

Author Contributions: Conceptualization, B.S., Y.B.Y., and S.C.; methodology, B.S., Y.B.Y., and S.C.; software, B.S., and Y.B.Y.; formal analysis, B.S., Y.B.Y., and S.C.; writing—original draft preparation, B.S., and Y.B.Y.; writing—review and editing, S.C.; visualization, B.S., and Y.B.Y.; supervision, S.C.; project administration, S.C. and J.H.M.; funding acquisition, S.C. and J.H.M. Funding: This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20172010000370). Conﬂicts of Interest: The authors declare no conﬂict of interest.

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