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Energies 2018, 11(7), 1643; doi:10.3390/en11071643
Article
Artificial Neural Network–Based Control of a Variable Refrigerant Flow System in the Cooling Season
^{1}
Department of Architectural Engineering, ChungAng University, Seoul 06974, Korea
^{2}
Department of Architectural Engineering, Hanbat National University, Daejeon 34158, Korea
^{3}
Department of Digital Appliance R&D Team, Samsung Electronics, Suwon 16677, Korea
^{4}
School of Architecture and Building Science, ChungAng University, Seoul 06974, Korea
^{*}
Author to whom correspondence should be addressed.
Received: 23 May 2018 / Accepted: 20 June 2018 / Published: 24 June 2018
Abstract
:This study aimed to develop a control algorithm that can operate a variable refrigerant flow (VRF) cooling system with optimal setpoints for the system variables. An artificial neural network (ANN) model, which was designed to predict the cooling energy consumption for upcoming next control cycle, was embedded into the control algorithm. By comparing the predicted energy for the different setpoint combinations of the control variables, the control algorithm can determine the most energyeffective setpoints to optimally operate the cooling system. Two major processes were conducted in the development process. The first process was to develop the predictive control algorithm which embedded the ANN model. The second process involved performance tests of the control algorithm in terms of prediction accuracy and energy efficiency in computer simulation programs. The results revealed that the prediction accuracy between simulated and predicted outcomes proved to have a low coefficient of variation root mean square error (CVRMSE) value (10.30%). In addition, the predictive control algorithm markedly saved the cooling energy consumption by as much as 28.44%, compared to a conventional control strategy. These findings suggest that the ANN model and the control algorithm showed potential for the prediction accuracy and energyeffectiveness of VRF cooling systems.
Keywords:
variable refrigerant flow (VRF) cooling systems; artificial neural network (ANN); predictive control algorithm; optimal setpoints of system variables1. Introduction
The amount of energy consumed worldwide has been increasing, causing serious environmental problems, such as global warming, urban heat island phenomenon, resource depletion, and air pollution [1]. The supplyanddemand situation of energy in the Republic of Korea is highly unstable, being the eighth highest country in the world regarding energy use with an annual 6.6% energy consumption growth rate, which is the highest among all Organization for Economic Cooperation and Development (OECD) countries [2,3]. As precautionary measures, the Korean government has suggested many policies and established various institutions for energy reduction, focusing on the building sector, in which the proportion of energy use is about 24% of the total national energy consumption [4,5]. Since the building sector accounts for a large portion of total energy consumption and has a relatively higher potential to reduce energy than other sectors, this could be an effective strategy in reducing energy consumption [6,7].
However, the amount of building energy consumed has been increasing with economic growth, since buildings have recently become larger and higher [8]. The annual electricity consumption in the building sector has continuously increased, from a 558,000 Ton of Oil Equivalent (TOE) in 2000 to 1,655,000 TOE in 2015, which accounts for more than 66% of the total building energy use within 15 years by this sector [9]. In addition, the cooling equipment use has been increasing due to the demands for more comfortable thermal environments [10]. Therefore, sophisticated cooling control systems and strategies are important factors for reducing cooling energy.
In accordance with these efforts, variable refrigerant flow (VRF) cooling systems have been increasingly implemented in large buildings due to their high efficiency, flexible forms, and modest space requirements [11,12]. VRF cooling systems can be grouped into two types: (1) water cooling systems and (2) air cooling systems. Direct expansion (DX) air handling unit (AHU)water source VRF systems are composed of multiple outdoor units, a cooling tower, a DX AHU, fans, and pumps as shown in Figure 1. The refrigerant is evaporated in the DX coil in AHU which is connected to multiple outdoor units. The compressor unit is watercooled. It is connected to a cooling tower. The variable refrigerant flow compressor is controlled by a variable speed drive which operates more energy efficiently than conventional compressors of the same size [13]. The conditioned air in the AHU is supplied to each room using fans.
One of the noticeable differences of the VRF systems compared to the conventional cooling systems is their high efficiency in partial load operation. In addition, they enable individual control of single rooms by controlling the refrigerant flow rate to various indoor units via electronic expansion valves [14]. The VRF systems circulate refrigerant through a DX coil instead of a water coil, and the refrigerant expands directly in an evaporator through the DX coil via the installed outdoor units of the heat pumps. Therefore, the VRF systems can provide a more comfortable and stable indoor thermal environment by actively responding to the dynamic variation of outdoor weather and can also reduce cooling energy consumption due to their part load variation [15].
Despite the significant advantages of the VRF systems, they have not been fully operated with optimal control strategies. The major control variables of the VRF systems, such as the supply air temperature setpoint (TEMP_{SA}) of the air handling unit, condenser fluid temperature setpoint (TEMP_{COND}), and condenser fluid amount setpoint (AMOUNT_{COND}) are determined by heuristic methods of the operators. Furthermore, the VRF systems have been operated by these control variables that are set constantly without considering energyefficiency. Therefore, efforts to operate the VRF systems are essential by setting optimal control variables for improving system performance in terms of energyefficiency and the provision of comfortable indoor thermal environments.
The aim of this study is to develop a control algorithm capable of operating the VRF cooling systems in an energy efficient manner by setting control variables that are optimally determined by an artificial neural network (ANN) model. Two major steps were involved to meet this aim. In the first step, a control algorithm was developed that embedded the ANN model to identify the optimal combination of the VRF system’s control variables. The embedded ANN model, which was developed in the preceding study [16], was designed to predict the cooling energy consumption for the operating setpoints of the VRF systems. The principal process for developing the ANN model in the preceding study is summarized in Section 2.2.
The second step served to test the performance of the predictive algorithm and the embedded ANN model. Prediction accuracy of the ANN model was validated from the coefficient of determination (R^{2}) value and the coefficient of variation root mean square error (CVRMSE) value, which are criteria specified in the American Society of Heating, Refrigerating and AirConditioning Engineers (ASHRAE) guidelines. In addition, the performance of predictive algorithm was evaluated by analyzing the cooling energy savings compared to the conventional control method.
2. ANN Model for Predicting Cooling Energy of the VRF System
2.1. ANN Literature Review on Building Thermal Controls
Building environment controls have been improved due to remarkable development in computer science and information technology. Artificial intelligence (AI) methods and applications have recently received a lot of attention due to their abilities to solve highly complex problems at high speed [17,18]. Among various AI methods such as ANNs, support vector machine, particle swarm optimization, genetic algorithms, and fuzzy logic, ANN method has shown outstanding abilities in selflearning with adaptability for prediction [19,20].
The ANN, which was first proposed by Warren McCulloch and Walter Pitts in 1943, is based on the human neural system [21]. ANN is a mathematical engineering tool used for a variety of tasks: classification, data mining, prediction, pattern recognition, function approximation, optimization, and association [22,23]. A multilayer feedforward network, a representative model of the ANN [24], consists of an input layer, one or more hidden layer(s), and an output layer as shown in Figure 2. Each layer includes various nodes, and each node in one layer is connected to all nodes in the next layer with connection strength, which is referred to as a “weight.” The nodes constituting each layer are calculated as the sum of the input values multiplied by the weight when receiving the signal from the previous layer. The output layer produces an output value through the calculation of a transfer function [25].
A backpropagation algorithm, which was first introduced by Werbos [26] and later developed by Rumelhart and McClelland [27], is a learning method to calculate the error contribution of each neuron. At the beginning of the learning stage, all weights in the network are initialized by reducing them to small random values. This propagates the signal backward and adjusts the weight of each layer according to the error between the output value and the target value. A gradient descent method is employed to modify the weight by minimizing the error. An iteration process then produces the desired output value [28]. Figure 3 shows the principle of the backpropagation algorithm.
ANNs have been increasingly applied to building thermal controls. Various ANN models have been developed to predict the energy consumption of heating, ventilation, and airconditioning (HVAC) systems. Further research has been conducted to advance the control algorithms of HVAC systems which embed the ANN model. A literature review of recent studies relevant to the topic follows.
Azadeh et al. [29] considered environmental and economic factors for the optimum estimation and forecasting of renewable energy. To demonstrate the applicability and superiority of the proposed an ANN model, monthly data were collected for 11 years (1996–2006) in Iran. The results revealed a prediction accuracy of approximately 99.9% in comparison with conventional and fuzzy regression models. Deb et al. [30] used an ANN to predict the diurnal cooling energy load of institutional buildings. Three institutional buildings were examined and the energy consumption data of each were analyzed for two years. The findings revealed that the ANN model trained and accurately predicted nextday energy use (R^{2} = 0.94) based on the data of the five previous days. Kalogirou [31] reviewed various ANN applications in a wide range of fields for modeling and predicting energy systems: a solar steamgenerator, solar water heating systems, HVAC systems, solar radiation and wind speed predictions, as well as power generation systems.
Moon and Jung [32] developed an ANN model for predicting the optimal start moment of the setback temperature during a normal occupied period. They also developed an algorithm embedded ANN model to enhance indoor thermal comfort and building energy efficiency. The results demonstrated the excellent prediction performance of the ANN model with an R^{2} value greater than 0.999 in the simulated results. Additionally, ANNbased algorithms were shown to be superior to conventional algorithms for improving indoor thermal comfort and building energy. Kwon [33] analyzed the cooling energy consumption based on the actual and meteorological data of large complex buildings and proposed an optimal operating strategy for a chiller plant by using an ANN predictive model. Based on information provided by the Korea Meteorological Administration (KMA), the weather data were inputs for the ANN model, such as the outside temperature, relative humidity, solar radiation, cloudiness, wind speeds, and rainfall events.
2.2. Development Process of the Predictive Model
The predictive model development was advanced from preceding research [16] by the addition of three major steps: (1) analysis of the relevant factors for cooling energy consumption, (2) development of initial model, and (3) optimization of the initial model. Table 1 summarizes the outcomes in each step and Figure 4 shows the final ANN model in the preceding study [16].
The ANN model was designed to predict the amount of cooling energy for the next control cycle which was assigned for 1 h. For cooling energy prediction, the ANN model used seven input variables. Among these input variables, TEMP_{OUT}, HUMID_{OUT}, and TEMP_{IN} are average values for the last one hour using measured values every five minutes. LOAD_{COOL} is the internal load generated from the cooling tower (kWh) for the last one hour. The remaining three variables (TEMP_{SA}, TEMP_{COND}, and AMOUNT_{COND}) were setpoints of the cooling system. They will be used for the next control cycle.
To decrease prediction errors, the ANN model was tuned for its structure and learning method. Numbers of hidden layer and neurons as well as learning rate and moment were optimized using parametrical process in a coupled fashion. The final model after tuning process employed two hidden layers and 15 hidden neurons in each layer. A total of 200 training datasets with slidingwindow data management method were used for model training [15] with LevenbergMarquardt learning method that only required just a few seconds. Thus, it would not be a problem for application.
In a preceding study [16], prediction performance of the developed ANN model was verified by comparing predicted and the measured energy consumption. Numerical indicators R^{2} and the CVRMSE suggested in the ASHRAE Guideline 14Measurement of energy and demand savings [34] were evaluation standards for the ANN model. They were used as a measure of difference between actual and predicted values. R^{2} of 0.8 or higher and CVRMSE below 30% were judged to be appropriate.
The cooling system in the test building was operated for 188 h from 1 July to 31 August. During operating hours, the amount of predicted cooling energy was similar to the amount of energy actually consumed. The value of R^{2} was 0.8136, verifying its prediction accuracy based on ASHRAE guideline. In addition, the CVRMSE value was 11.3% which was within allowable range suggested by ASHRAE. Thus, the applicability of the proposed ANN model was proven to have acceptable accuracy level [16].
3. Development and Evaluation of the Predictive Control Algorithm
The operation algorithm of the VRF system, in which the ANN model was embedded, was developed in this study to determine the optimal combination for the setpoints of the system variables. The predictive control algorithm was developed using the MATLAB software and its neural network toolbox. Figure 5 displays the flowchart of the predictive control algorithm. The indoor and outdoor thermal climate conditions (TEMP_{OUT}, HUMID_{OUT}, and TEMP_{IN}) as well as the system operating condition (LOAD_{COOL}) are collected, followed by the cooling energy prediction for the three setpoints of the system variables (TEMP_{SA}, TEMP_{COND}, and AMOUNT_{COND}), which progress through the ANN model. Each optimal setpoint value of the system variables was derived by comparing the energy consumption for the different setpoint combinations. The cooling system then operates following the optimal setpoints for the next control cycle which was assigned 1 h.
The performance of the predictive control algorithm in which the ANN was embedded was evaluated to validate the prediction accuracy and the cooling energy savings. The ANNbased predictive control algorithm was compared with a conventional building control algorithm that used fixed setpoints for the VRF system variables, which is the normal strategy applied in the field. Table 2 shows the setpoints of the control variables for the conventional and predictive control algorithms and Figure 6 conceptually presents the process conducted for determine the optimal setpoint combination in the predictive control algorithm. In the predictive control algorithm, the ENERGY_{TOT} for a series of TEMP_{SA}, TEMP_{COND}, and AMOUNT_{COND} were compared and the most energy efficient combination was selected. TEMP_{SA} and TEMP_{COND} were compared for every 1 °C and the AMOUNT_{COND} was compared for every 500 L/min, thus the total number of case for comparison was 1287. This process was conducted in one second and repeated for every control cycle. The heating systems would work following the optimal setpoint combination, and the amount of energy consumption would be compared with that of the conventional algorithm.
Comparison tests between the conventional and predictive control algorithms were conducted using the following computer simulation programs: EnergyPlus, MATLAB, and Building Controls Virtual Test Bed (BCVTB). Figure 7 shows an overall diagram of the cosimulation process between EnergyPlus and MATLAB via BCVTB. Each program was operated by interacting with each other as follows. First, EnergyPlus created the model of a DX AHU and outdoor units of the VRF cooling system and the test building to read outdoor climate conditions and produce indoor thermal environment.
Second, MATLAB created the model of a cooling tower and a pump of the VRF cooling system. In addition, ANN model was developed using the neural network toolbox in MATLAB and a control algorithm was developed.
Finally, BCVTB functioned as a connection tool for transferring data between EnergyPlus and MATLAB. When EnergyPlus sent data regarding indoor and outdoor thermal conditions, the ANN model and control algorithm in the MATLAB environment determined optimal setpoints of system variables. These determined optimal setpoints were sent to VRF cooling systems in EnergyPlus for working. New indoor thermal condition was then created and sent to MATLAB. This process was repeated at every control cycle. In addition, EnergyPlus calculated the amount of cooling energy consumption.
The test building was the R&D center located in Seoul, Republic of Korea (37.33° N latitude and 126.58° E longitude), as shown in Figure 8. The building was an 11story office building composed of a podium, a typical floor, and a roof. The VRF system type implemented in the building was the DX AHUwater source VRF system. One cooling tower, 11 AHUs, and 27 outdoor units composed the VRF system for covering the whole building.
Figure 9 displays the configuration of actual VRF systems for 11 zones. The capacity of each VRF system after calculating part load ratio and efficiency is summarized in Table 3. Zonebased individual control was applied to the test building. Each room applied its own indoor setpoint temperature using thermostat while the indoor setpoint temperature was fixed at 26 °C during working hours and 29 °C during the nonworking hours for the whole building.
VRF system parameters such as TEMP_{SA}, TEMP_{COND}, and AMOUNT_{COND} for entire zones were identically changed. Datasets for the ANN model were collected for diverse combinations of these system parameters. Trained ANN model using these datasets could predict the total cooling energy by the entire VRF systems to provide a comfortable thermal environment based on the designated indoor setpoint temperature. In addition, the control algorithm could compare total energy for different setpoint combinations and determine the optimal combination.
Different amount of interior heat gain was applied based on Table 4 and Table 5. Table 4 summarizes the baseline of internal heat gain while Table 5 shows timebased ratio of internal load. Using the baseline and timebased ratio, the internal load in the simulation was considered differently during the whole day.
The modeling process was conducted through applications of the reference schedule and internal loads from the office building based on ASHRAE Standard 90.1 [35]. Table 6 shows input conditions which were applied to the building simulation. The cooling systems were operated for 24 h to analyze the energy performance according to the part load operation of the cooling systems.
The AHU and outdoor units were modeled by the energy management system (EMS) of EnergyPlus. However, to model the VRF system, EnergyPlus cannot provide a tool for the water source VRF system. Therefore, an air source VRF system (Coil:Cooling:DX) was substituted as a workaround.
The EnergyPlus models (AHU and outdoor units) and MATLAB models (the boiler and the pumps) were calibrated to verify the accuracy of the models. Test data were compared to threemonth measurements from the building energy management system (BEMS) in the actual R&D center. Among the four models of the AHU, the outdoor units, the boiler, and the pumps, the performance of modeling the outdoor units was analyzed using three performance curves: the low temperature curve, the high temperature curve, and the boundary curve. After the calibration of the models, the CVRMSE between the simulated and measured amount of energy consumption was found to be 15.2%. Thus, the accuracy of the simulation models could be verified within the tolerance range (under 30%) suggested in ASHRAE Guideline 14Measurement of energy and demand savings [34].
4. Performance of the Predictive Control Algorithm
The performance of the predictive control algorithm was tested at three stages: (1) the prediction accuracy of simulation results in comparison with the predicted outcomes, (2) the optimal setpoints of the VRF system variables, (3) the results of the cooling energy amount from the conventional and predictive control algorithms.
First, the comparison between the simulated energy consumption (ENERGY_{SIM}) and the predicted energy consumption (ENERGY_{PRED}) for the entire building is shown in Figure 10. ENERGY_{SIM} was an EnergyPlus output, while ENERGY_{PRED} was derived from the ANN model. The number of cycles (iterations) during the cooling system operation was 1488 h, that is, 62 days from the 1 July to the 31 August. Figure 11 shows the coefficient of determination (R^{2}) between the ENERGY_{SIM} and ENERGY_{PRED}. R^{2} was calculated as 0.9462, which meets the ASHRAE guideline criteria of 0.8 or higher.
CVRMSE between ENERGY_{SIM} and ENERGY_{PRED} was 10.3%, which demonstrated the reliability of ANN prediction. In addition, the number of cases and the results of CVRMSE (%) were analyzed according to the difference between ENERGY_{SIM} and ENERGY_{PRED} in Figure 12. For more than 80% of cases, the difference between ENERGY_{SIM} and ENERGY_{PRED} was between −20 and 20 kWh and CVRMSEs for these cases were significantly smaller, which indicated the stability of the ANN prediction.
Second, the optimal setpoints of the system variables were determined by the predictive control algorithm, in which the ANN model was embedded and the prediction results were used. Figure 13 displays the setpoints that had resulted for the three system variables: TEMP_{SA}, TEMP_{COND}, and AMOUNT_{COND}. The optimal setpoint of TEMP_{SA} was in the stable range of 10–18 °C in the iteration progress, while the setpoints of TEMP_{COND} and AMOUNT_{COND} were within the ranges of 25–35 °C and 1000–7000 L/min, respectively.
While the setpoints of all system variables changed for each moment, each system variable was primarily set as a specific point or range: the setpoints of TEMP_{SA} were 12–14 °C, TEMP_{COND} 35 °C, and AMOUNT_{COND} 3000–5000 L/min.
Finally, the cooling energy consumption of VRF system components was compared between the conventional and predictive control algorithm. Figure 14 exhibits the results of the cooling energy consumption from two VRF system types, which were operated by conventional and ANNbased predictive control algorithms.
The results show the total cooling energy savings and the energy consumption of the outdoor units, the fans, the pumps, and the cooling tower in detail. The VRF system with ANN saved 28.44% of the total cooling energy, while the energy consumption of the outdoor units, the fans, the pumps, and the cooling tower was reduced by 30.57%, 26.59%, 51.78%, and 9.05%, respectively. Accordingly, the energy efficiency improved by 28.44% and the amount of energy savings was 95,509 kWh, which can be converted to 13,562,278 Korean won (12,054 U.S. dollars), since the unit cost of 1 kWh was 142 Korean won in 2017.
5. Conclusions
In this study, a control algorithm was developed to operate an intermittently working VRF cooling system in an energyeffective way. This algorithm determined the optimal setpoints of the system variables by using an ANN model to predict the cooling energy consumption for the upcoming control cycle. The performance of the predictive control algorithm was compared in computer simulations regarding the prediction accuracy, the setpoints’ determination, and the cooling energy savings. The study results are summarized below.
 (1)
 The prediction model embedded in the control algorithm showed an acceptable prediction accuracy. For the number of cycles (1488 h), the results revealed the CVRMSE of 10.3% between the simulated and predicted cooling energy.
 (2)
 The ANNbased predictive control algorithm determined the optimal setpoints of the VRF system. For most of the cases, TEMP_{SA}, TEMP_{COND}, and AMOUNT_{COND} were in the stable range of 12–14 °C, 35 °C, and 3000–5000 L/min, respectively
 (3)
 The algorithm also markedly saved the cooling energy consumption of the VRF cooling system during the 62 days from the 1 July to the 31 August. The total cooling energy saved was 28.44%, which corresponds to an electrical energy reduction of approximately 95,509 kWh or 13,562,278 Korean won (12,054 U.S. dollars).
From the study findings, it is concluded that the ANNbased predictive control algorithm possesses a verified prediction accuracy and that it is appropriate for operating a VRF cooling system in terms of energyeffectiveness. For further research, field tests will be needed to validate actual performance by implementing the ANNbased predictive control algorithm in existing buildings. In addition, diverse control algorithms such as PIDbased methods need to be developed and comparatively tested their performance with the proposed predictive algorithm in the further study.
Author Contributions
I.K., K.H.L., J.H.L., and J.W.M. participated in the preparation, research design, method, and analysis of this project. All authors discussed and finalized the analysis results to prepare this manuscript.
Funding
This research was funded by the Ministry of Land, Infrastructure and Transport of the Korean government.
Acknowledgments
The research was supported by a grant (code 18CTAPC12976202) from the Infrastructure and Transportation Technology Promotion Research Program.
Conflicts of Interest
The authors declare no conflict of interest.
Nomenclature
ANN  artificial neural network 
OECD  organization for economic cooperation and development 
TOE  ton of oil equivalent 
DX AHU  direct expansion air handling unit 
R^{2}  coefficient of determination 
CVRMSE  coefficient of variation root mean square error 
ASHRAE  American Society of Heating, Refrigerating and AirConditioning Engineers 
SA  supply air 
RA  return air 
EA  exhaust air 
OA  outdoor air 
TEMP_{SA}  air handling unit supply air temperature setpoint (°C) 
TEMP_{COND}  condenser fluid temperature setpoint (°C) 
AMOUNT_{COND}  condensing warm fluid amount setpoint (liter/minute) 
TEMP_{OUT}  average outdoor temperature (°C) 
HUMID_{OUT}  average outdoor humidity (%) 
SOLAR  average solar radiation (W/m^{2}) 
TEMP_{IN}  average indoor temperature (°C) 
LOAD_{COOL}  internal load that generated from the cooling tower (kWh) 
ENERGY_{TOT}  predicted total cooling energy for the next 1 h (kWh) 
NHL  number of hidden layer 
NHN  number of hidden neuron in each hidden layer 
LR  learning rate 
MO  moment 
References
 Meehl, G.A.; Tebaldi, C. More intense, more frequent, and longer lasting heatwaves in the 21st Century. Science 2004, 305, 994–997. [Google Scholar] [CrossRef] [PubMed]
 International Energy Agency. Energy Balances of OECD/NonOECD Countries; IEA Statistics: Paris, France, 2015. [Google Scholar]
 Korea Energy Agency. 2016 Korea Energy Handbook; KEA: Yonginsi, Korea, 2016. [Google Scholar]
 Ahn, B.L.; Kim, C.H.; Kim, J.Y.; Jang, C.Y. A study on the analysis of building energy rating considering the region. J. Korean Sol. Energy Soc. 2009, 29, 53–58. [Google Scholar]
 Jang, H.I.; Cho, Y.H.; Jo, J.H. Application and improvement of the renewable energy management system in existing buildings. J. Architect. Inst. Korea 2013, 29, 227–234. [Google Scholar]
 Korea Energy Economics Institute. 2014 Energy Consumer Survey; Korea Energy Economics Institute: Ulsan, Korea, 2015. [Google Scholar]
 National Institute of Environmental Research. Greenhouse Gas Reduction Technologies and Their Costs in Commercial and Public Sector; National Institute of Environmental Research: Tsukuba, Japan, 2010. [Google Scholar]
 Korea Energy Economics Institute. Building Energy Consumption Standing Sampling Survey; Korea Energy Economics Institute: Ulsan, Korea, 2016. [Google Scholar]
 Korea Energy Agency. 2015 Energy Usage Statistics; KEA: Yonginsi, Korea, 2016. [Google Scholar]
 Ministry of Land, Infrastructure and Transport. The 1st Green Building Basic Plan; Ministry of Land, Infrastructure and Transport: Sejong, Korea, 2014. [Google Scholar]
 Lee, J.H.; Song, Y.H.; Yoon, H.J.; Choi, D.S.; Tae, S.J.; Kim, I.K. A study on development and effectiveness verification of setpoint control algorithm for watercooled VRF System. Soc. AirCond. Refrig. Eng. Korea 2016, 399–402. [Google Scholar]
 Aynur, T.N. Va riable refrigerant flow systems: A review. Energy Build. 2010, 42, 1106–1112. [Google Scholar] [CrossRef]
 Thornton, B.; Wagner, A. Variable Refrigerant Flow Systems; Pacific Northwest National Laboratory: Richland, WA, USA, 2012. [Google Scholar]
 Lee, K.H. A calculation method of the cooling performance for the direct expansion (DX) air handling unit (AHU)water source VRF system. Soc. AirCond. Refrig. Eng. Korea 2016, 45, 64–68. [Google Scholar]
 Zhang, D.; Zhang, X.; Liu, J. Experimental study of performance of digital variable multiple air conditioning system under part load conditions. Energy Build. 2011, 43, 1175–1178. [Google Scholar] [CrossRef]
 Chung, M.H.; Yang, Y.K.; Lee, K.H.; Lee, J.H.; Moon, J.W. Application of artificial neural networks for determining energyefficient operating setpoints of the VRF cooling system. Build. Environ. 2017, 125, 77–87. [Google Scholar] [CrossRef]
 Panja, P.; Velasco, R.; Pathak, M.; Deo, M. Application of artificial intelligence to forecast hydrocarbon production from shales. Petroleum 2018, 4, 75–89. [Google Scholar] [CrossRef]
 Gupta, A.K.; Kumar, P.; Sahoo, R.K.; Sahu, A.K.; Sarangi, S.K. Performance measurement of plate fin heat exchanger by exploration: ANN, ANFIS, GA, and SA. J. Comput. Des. Eng. 2017, 4, 60–68. [Google Scholar] [CrossRef]
 FerdynGrygierek, J.; Grygierek, K. Multivariable optimization of building thermal design using genetic algorithms. Energies 2017, 10, 1570. [Google Scholar] [CrossRef]
 Yang, J.; Rivard, H.; Zmeureanu, R. Building energy prediction with adaptive artificial neural networks. In Proceedings of the 9th International IBPSA Conference, Montréal, QC, Canada, 15–18 August 2005; pp. 1401–1408. [Google Scholar]
 McCulloch, W.S.; Pitts, W. A logical calculus of ideas immanent in nervous activity. Bull. Math. Biophys. 1943, 5, 115–133. [Google Scholar] [CrossRef]
 Basheer, I.D.; Hajmeer, M. Artificial neural networks: Fundamentals, computing, design, and application. J. Microbiol. Meth. 2000, 43, 3–31. [Google Scholar] [CrossRef]
 Nielsen, F. Neural NetworksAlgorithms and Application; Niels Brock Business College: København, Denmark, 2001. [Google Scholar]
 Zhang, G.; Patuwo, B.E.; Hu, M.Y. Forecasting with artificial neural networks: The state of the art. Int. J. Forecast. 1998, 14, 35–62. [Google Scholar] [CrossRef]
 Renno, C.; Petito, F.; Gatto, A. Artificial neural network models for predicting the solar radiation as input of a concentrating photovoltaic system. Energy Convers. Manag. 2015, 106, 999–1012. [Google Scholar] [CrossRef]
 Werbos, P. Beyond regression: New Tools for Prediction and Analysis in the Behavior Sciences. Ph.D. Thesis, Harvard University, Cambridge, MA, USA, 1974. [Google Scholar]
 Rumelhart, D.; McClelland, J. Parallel Distributed Processing: Explorations in the Microstructure of Cognition; MIT Press: Cambridge, MA, USA, 1986. [Google Scholar]
 Lippman, R.P. An introducing to computing with neural nets. IEEE ASSP Mag. 1987, 4, 4–22. [Google Scholar] [CrossRef]
 Azadeh, A.; Saberi, M.; Anvari, M.; Mohamadi, M. An integrated artificial neural networkgenetic algorithm clustering ensemble for performance assessment of decision making units. J. Intell. Manuf. 2011, 22, 229–245. [Google Scholar] [CrossRef]
 Deb, C.; Eang, L.S.; Yang, J.; Santamouris, M. Forecasting diurnal cooling energy load for institutional buildings using artificial neural networks. Energy Build. 2016, 121, 284–297. [Google Scholar] [CrossRef]
 Kalogirou, S.A. Applications of artificial neural networks for energy systems. Appl. Energy 2000, 67, 17–35. [Google Scholar] [CrossRef]
 Moon, J.W.; Jung, S.K. Algorithm for optimal application of the setback moment in the heating season using an artificial neural network model. Energy Build. 2016, 127, 859–869. [Google Scholar] [CrossRef]
 Kwon, H.S. Optimal Operating Strategy of a Hybrid Chiller Plant Utilizing Artificial Neural Network Based Load Prediction in a Large Building Complex. Ph.D. Thesis, Seoul City University, Seoul, Korea, 2013. [Google Scholar]
 American Society of Heating. Refrigerating, and AirConditioning Engineer, ASHRAE Guideline 14—Measurement of Energy and Demand Savings; ASHRAE Inc.: Atlanta, GA, USA, 2002. [Google Scholar]
 American Society of Heating. Refrigerating, and AirConditioning Engineer, Energy Standard for Buildings Except LowRise Residential Building; ASHRAE Inc.: Atlanta, GA, USA, 2015. [Google Scholar]
Figure 1.
Diagram of the direct expansion DX air handling unit (AHU)water source variable refrigerant flow (VRF) system. SA: supply air; RA: return air; EA: exhaust air; OA: outdoor air.
Figure 4.
Structure of the optimized ANN model [15].
Figure 11.
The regression line and coefficient of determination (R^{2}) between simulated energy consumption (ENERGY_{SIM}) and predicted energy consumption (ENERGY_{PRED}).
Figure 12.
Number of cases and CVRMSE (%) for difference between simulated energy consumption and predicted energy consumption.
Figure 14.
Cooling energy consumption comparisons between conventional and ANNbased control algorithms.
Steps  Outcomes 

(1) Factor analysis  Found 7 relevant factors to the cooling energy cost

(2) Initial model development 

(3) Model optimization 

Control Variables  Control Algorithms  

Conventional  Predictive  
TEMP_{SA}  16 °C  10~18 °C 
TEMP_{COND}  32 °C  25~35 °C 
AMOUNT_{COND}  6000 L/min  1000~7000 L/min 
Capacity (kW)  

VRF No. 1  VRF No. 2  VRF No. 3  VRF No. 4  VRF No. 5  VRF No. 6  
83.1  52.8  83.3  112.3  111.7  112.8  
VRF No. 7  VRF No. 8  VRF No. 9  VRF No. 10  VRF No. 11  
111.6  178.4  82.2  82.0  67.4 
Components  Unit  Input Value 

Occupants  Person/Area  0.078 person/m^{2} 
Lighting  Watts/Area  21.52 W/m^{2} 
Electric Equipment  Watts/Area  16.14 W/m^{2} 
Cooling Schedule  Occupants (Ratio)  Lighting (Ratio)  Electric Equipment (Ratio) 

Weekdays  00:00~06:00 (0.000) 06:00~22:00 (0.500) 22:00~23:59 (0.025)  00:00~23:59 (0.250)  00:00~23:59 (1.000) 
Saturday  00:00~06:00 (0.000)  00:00~06:00 (0.012)  00:00~06:00 (0.300) 
06:00~08:00 (0.500)  06:00~08:00 (0.025)  06:00~08:00 (0.400)  
08:00~12:00 (0.150)  08:00~12:00 (0.075)  08:00~12:00 (0.500)  
12:00~17:00 (0.050)  12:00~17:00 (0.037)  12:00~17:00 (0.350)  
17:00~23:59 (0.000)  17:00~23:59 (0.012)  17:00~23:59 (0.300)  
Sunday  00:00~23:59 (0.000)  00:00~23:59 (0.012)  00:00~23:59 (0.300) 
Category  Input Value 

Simulation program  EnergyPlus v8.5 
Site/weather location  Seoul/Republic of Korea 
Windowtowall ratio  40% 
Chiller based conventional AHU/watercooled VRF schedule  24 h 
Cooling setpoint  26 °C 
COP  4.787 
Pump motor efficiency  90% 
Infiltration  0.0003167 m^{3}/sm^{2} 
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