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Building and Environment 61 (2013) 149e159

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Building and Environment

journal homepage: www.elsevier .com/locate/bui ldenv

Development of an artificial neural network model based thermalcontrol logic for double skin envelopes in winter

Jin Woo Moon a,1, Sung-Hoon Yoon b,2, Sooyoung Kim c,*

aDepartment of Building & Plant Engineering, Hanbat National University, Daejeon, Republic of KoreabDepartment of Architecture, Cheongju University, Cheongju, Republic of KoreacDepartment of Housing & Interior Design, Yonsei University, 134 Shinchondong Seodaemunku, Seoul 120-749, Republic of Korea

a r t i c l e i n f o

Article history:Received 6 September 2012Received in revised form2 December 2012Accepted 19 December 2012

Keywords:Artificial neural networkModel optimizationPredictive and adaptive controlsDouble skin envelopeThermal environments

* Corresponding author. Tel.: þ82 2 2123 3142; faxE-mail addresses: gilerbert73@gmail.com (J.W

(S.-H. Yoon), sooyoung@yonsei.ac.kr (S. Kim).1 Tel.: þ82 42 821 1183.2 Tel.: þ82 43 229 8506; fax: þ82 43 229 8476.

0360-1323/$ e see front matter � 2013 Elsevier Ltd.http://dx.doi.org/10.1016/j.buildenv.2012.12.010

a b s t r a c t

This study proposes an Artificial Neural Network (ANN)-based thermal control method for double skinenvelope buildings in winter. A thermal control logic for controlling heating systems and openings on theinternal and external envelopes of a double skin building was developed using the ANN-based predictiveand adaptive control model. Employing the predicted values for the future indoor air temperature (i.e.,the air temperature rise or drop by the next control cycle), the control logic predetermines the operationof the heating system and the opening conditions of internal and external envelopes of a double skinbuilding.

After the parametrical optimization of the initial ANN model, the performance of the optimized ANNmodel was tested for prediction accuracy and adaptability using the data measured from an actualdouble-skinned envelope building. The analysis results revealed that the ANN model proved itsprediction accuracy and adaptability for the different climate conditions and envelope orientations. Thedeveloped control logic and model in this study are effectively applied for thermal control of doubleskinned envelope buildings.

� 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Curtain wall structures sealed with light material such as glassare used for high-rise buildings due to their light structural loads.Despite themerits of such systems, buildings constructed using thisstructure revealed critical problems in terms of energy consump-tion due to easy heat gain and loss between outdoor environmentsand indoor space.

To mitigate the increased energy consumption, double skinenvelopes (DSE) containing a cavity space that functions asa thermal buffer zone between the outdoor and indoor space areapplied to curtain wall structures. Research results showed that theDSE effectively contributed to energy savings under unpredictableweather conditions [1e3]. The effects of DSE were analyzed interms of heat transfer and ventilation rates to optimize controloptions for the DSE. Diverse control strategies, such as rule-based

: þ82 2 313 3139.. Moon), shyoon@cju.ac.kr

All rights reserved.

approaches or optimal control theories have been suggested todetermine appropriate control options for the cavity space in theDSE [4e6].

Among these various control strategies, the rule-based controlshave been the most widespread method for controlling openingson the DSE. Since the openings are controlled based on relativelysimple rules and devices, such as temperature sensors, the controllogic and system hardware are easily developed and have actuallybeen employed in buildings [4e6].

However, the control options for the cavities based on thevariation of cavity temperature were not considered enough. Thecavity space should be optimally controlled by appropriate controlstrategies to maximize the energy saving effect from the DSE. Assuch, optimal solutions for the best building performance should bedetermined logically for advanced thermal quality and energyefficiency, since the intuitive rules govern the control process.Thermal quality and energy efficiency in buildings with a DSE couldbe improved by optimum control strategies under various outdoorweather conditions.

Artificial intelligence (AI) could be a potential solution fordeveloping the optimal strategy for controlling openings on theDSE based on the accurate prediction of building environmentalfactors [7e9]. Diverse AI methods have been studied for their

Nomenclature

DTemperature Indoor temperature change from amoment tothe next cycle

TEMPIN Indoor air temperature measured in the testbuilding [�C]

TEMPPR Indoor air temperature change predicted by ANNmodel to the next cycle [�C]

TEMPME Indoor air temperature change measured in the testbuilding to the next cycle [�C]

Ni Number of neurons in the input layerNh Number of neurons in the hidden layerNo Number of neurons in the output layerNd Number of training data sets

Fig. 1. A logic for controlling heating systems and openings of the double skinenvelope.

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applicability to the building thermal condition field, in particularArtificial neural network (ANN), fuzzy and adaptive neuro-fuzzyinference system (ANFIS) proved their potentials to be success-fully applied in the building thermal control logics using theaccurate prediction of the future indoor air temperature [9]. TheseAI methods better stabilized indoor air temperature conditionsnear the set-point compared to the conventional two-positioncontrol method.

Among the diverse AI methods, the ANN method has importantadvantages over fuzzy and ANFIS methods. Fuzzy and ANFISmethods have a limitation on the number of inputs becausemembership functions of variables and the if-then inference rulesare exponentially complicated as the number of inputs increases.On the other hand, ANN method does easily handle the largenumber of inputs, thus diverse relative factors for determiningindoor air temperature conditions could be considered. Therefore,this study employed ANN method for developing thermal controlmethod for DSE buildings.

Artificial neural networks (ANN), which were developed byWarren McCulloch and Walter Pitts, are analogous to the humanneural structure and its learning process. Based on superiorpredictability and adaptability, it has been successfully employed innon-linear systems and systems with unclear dynamics [10].

Studies which adopted ANN-based predictive and adaptivethermal control methods have been conducted and proved theANN model’s advantages over mathematical models such asregression models or proportional-integral-derivative (PID)controllers. ANN models in these studies have been successfullyapplied for the control of diverse heating and cooling systemssuch as radiant underfloor water heating systems (i.e., Ondol),hydronic heating systems of solar buildings, and air-conditioning,and also have proved their superiority over conventionalmethods [11e18].

In particular, Moon and Kim developed an ANN-based thermalcontrol logic that was designed to control heating, cooling, humid-ifying, and dehumidifying systems [19e22]. As the heat and mois-turewere controlled simultaneously, thepredictedmeanvote (PMV)level in space was proven to be controlled more effectively. Inaddition, through comparative analysis between the conventionalcontrol method and the proposed ANN-based method, the methodusing the ANN model showed advanced stability of the thermalconditions and adaptability for the diverse disturbances around thebuildings.

Based on the findings of previous studies, this studyproposes an Artificial Neural Network (ANN)-based thermalcontrol method for the optimal control of openings on internaland external skins of the DSE as well as heating systems. Four

research phases were employed in this study to achieve theresearch objectives. First, a thermal control logic for heatingsystems and openings on the internal and external envelopewas proposed. Secondly, an ANN model, which would beapplied to the control logic, was developed as a predictive andadaptive control method. Thirdly, the structure and the learningmethod of the developed ANN model were optimized. Lastly, theprediction accuracy and adaptability of the optimized ANN modelwere tested and compared with data monitored from an actualbuilding with the DSE.

The ANN model developed in this study employs predictivecontrols of the heating system and openings under various weatherconditions. The adaptive control, which is applied to the buildingsof diverse backgrounds based on the self-tuning process, is alsoincluded in the models. Thus, the proposed method using the ANNmodels in this study would improve the limitations of the currentlyexisting rule-based control strategies.

2. Development of thermal control logic

Thermal control logic for control of heating systems and cavityopenings was developed in this study as a part of the ANN-basedthermal control method. The purpose of the thermal control logic

Table 1Opening options.

Cases Opening conditions

Internal skin External skin

1 Closed Closed2 Closed Open3 Open Closed4 Open Open

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was to optimally operate the internal and external openings as wellas the heating systems.

The flow chart for the control logic, which consists of threeessential phases, is shown in Fig. 1. First, the future indoortemperature, which results from various opening strategies forcavities and operable windows, was predicted. The futuretemperature is the summation of the current temperature and thechange in indoor temperature from a moment to the next cycle(TEMPPR). Variation in temperature rise or drop to the next controlcycle was predicted for each case of opening options (given inTable 1) using the ANN models developed in this study, which isdescribed further in Section 3. Specifically, the ANN model predictsthis change of indoor temperature (TEMPPR) by these four openingoptions based on a series of inputs which include openingconditions.

Next, the optimal opening strategy is determined in the controllogic according to the operation conditions of the heating systems.If a heating system is currently working, the optimal strategy of theopenings is the one that raises the indoor temperature mostsignificantly. For instance, the changes in indoor temperature(TEMPPR) for each of the four cases shown in Table 1 are0.5 �C, �1.5 �C, 2.5 �C, and �3.0 �C, respectively. In this case,the optimum opening conditions selected by the control logic isCase 3.

Finally, the decision for operating heating systems is made andoptimum opening strategies are conducted according to thetemperature change (TEMPPR). If the sum of the predicted indoortemperature change (TEMPPR) and the current indoor tempera-ture is over the upper limit of the target indoor temperature, forinstance 23 �C, the logic determines the heating system should beturned off before the indoor temperature reaches the upper limit.Further, openings on internal and external envelopes follow theoptimized strategy determined in the previous step. A similarprocess is conducted when the heating system is not currently

Fig. 2. Conceptual air temperature profile by

working. If the sum of the predicted temperature change(TEMPPR) and the current indoor temperature is under the lowerlimit of the heating range, for instance 20 �C, the heating systemsbegin to work before the temperature actually reaches the lowerlimit.

Fig. 2 conceptually illustrates the indoor air temperature profilesby two control logics. The dashed line means the temperaturemovement by the conventional logic without the ANN model. Aheating system is turned on and off when the temperature reachesor passes the marginal degrees (e.g., 20 or 23 �C). Due to the timelag of the system and the thermal inertia of the space, overshootsand undershoots occur out of the specified operating range, whichmeans the uncomfortable period of the indoor air temperature. Onthe other hand, the temperaturemovement by the ANN-based logic(solid line in the Figure) is better stabilized within the operatingrange.

At every control cycle, ANN models predict the indoor temper-ature change (TEMPPR), and the control logic uses this predictedtemperature as well as the current indoor temperature. When thesum of these two values reaches or passes the marginal degrees ofthe operating range, logic predetermines to turn on or off theheating system. Overshoots or undershoots also occur, but thetemperature discomfort will be less or not be created. In conclusion,by the predictive operations, indoor thermal conditions could bemore stably controlled within the designated thermal comfortrange [11,19,20].

3. ANN model development

3.1. Factors influencing thermal conditions

Indoor thermal conditions of the building are derived by a heattransfer process that is affected by diverse factors, such as outdoorweather conditions, ventilation and infiltration rates, and internalheat gain. Outdoor weather conditions include air temperature,humidity, wind speed, and solar radiation. Internal heat gainincludes the heat from occupants, lighting fixtures andequipment.

Table 2 summarizes the factors influencing heat transfer indouble skinned envelope buildings. Conduction through theenvelope, ventilation rate, infiltration rate, solar radiation, andinternal loads are the major components determining the amountof heat transfer. The relative factors with these major componentsare outdoor, cavity and indoor air temperatures, solar radiation,

the conventional and ANN-based logics.

Table 2Factors in double skin envelope buildings affecting heat transfer.

Heat transfer components Relative factors in the double skin envelope buildings

Outdoorair temp.

Cavityair temp.

Indoorair temp.

Solarradiation

People, lighting,equipment conditions

Surface openingconditions

Size and property ofenvelope components

Conduction through envelope O O O OVentilation/infiltration O O O O OSolar radiation O OInternal loads O

J.W. Moon et al. / Building and Environment 61 (2013) 149e159152

number of people, lighting and equipment conditions, internal andexternal surface opening conditions, and size and properties of theenvelope components. An ANN model employs these factors asinput variables for calculating the indoor temperature change(TEMPPR) as an output variable.

3.2. Development of an initial ANN model

An initial ANN model was developed using Matlab [23] and itsneural network toolbox. The structure, transfer function, andtraining method of the model are summarized in Table 3.

The model consists of an input layer, a hidden layer, and anoutput layer where 7, 15 and 1 neurons are assigned, respectively.The neurons for input are chosen from Table 2, and have normal-ized values between 0 and 1. Table 4 summarizes the ranges of eachinput variable. The number of hidden neurons was calculated using

Table 3Descriptions of the developed initial ANN model.

Structure InputLayer

<Number of neurons: 7

i) indoor air temperatureii) indoor air temperature change

from preceding 10 miniii) outdoor air temperatureiv) cavity temperaturev) solar radiationvi) opening condition of inner surfacevii) opening condition of outer surface

HiddenLayer

<Number of neurons: 15 usingNh ¼ 2 � Ni þ 1 [24,25]

where,Nh: number of hidden neuronsNi: number of input neurons

OutputLayer

<Number of neuron: 1 (TEMPPR)

TransferFunctions

HiddenNeurons

< Tangent Sigmoid

OutputNeurons

< Pure Linear

Training Method < Training goals: 0.01 �C for air temperature(Mean Square Error)

< Epoch: 1000 times< Learning rate: 0.75 [12]<Momentum: 0.9 [12]< Algorithm: LevenbergeMarquardt [19e22]<Number of data sets: 121 using

Nd ¼ (Nh � (Ni þ No)/2)2 [26]

where,Nd: number of data setsNi: number of input neuronsNh: number of hidden neuronsNo: number of output neurons

<Data Managing Technique: sliding-windowmethod

the equation shown in Table 3. Using the tangent sigmoid and purelinear transfer functions, the amount of temperature change by thenext control cycle (TEMPPR) was produced. The period assigned fora cycle in this study was 5 min.

Since the optimal ANN model could be designed througha heuristic process, the parameters for training methods such aslearning rate (0.75), moment (0.90), LevenbergeMarquardt algo-rithm, and number of data sets (121) were determined for thisinitial model based on the values suggested in the previous studies[7,8,11,20,21]. Initial values would be modified by the optimizationprocess which is described in Section 3.3.

Data for training, optimization and checking were collectedfrom an actual building with a double skin envelope located inAnsan, Korea (latitude: 37�1700, longitude: 126� 4900). The layout ofthe building is shown in Fig. 3. The conditions of building set upfor data monitoring were similar to those used in previousstudies [1,2]. The first and second floors are used for research, andthe third floor is used for office space. The long axis of thebuilding is rotated counterclockwise by 26� from the northesouth axis. Double skin envelopes were installed on both longfaçades of the building and covered the first floor in the desig-nated area.

The internal and external skins were covered with glazing, andthe dimensions of the cavity were 5.7 m wide, 0.5 m deep and3.6 m high. The sides of the surfaces between the internal and theexternal skins were also covered with glazing to separate thecavity from the adjacent double skin envelope. Heat transfercoefficient, solar heat gain coefficient, absorption and reflectioncoefficient of glass used for the internal envelope were2.83 W/m2 K, 0.755, 0.101, and 0.126, respectively. Those fourproperties of the glass used for the external envelope were5.68 W/m2 K, 0.855, 0.095, and 0.075, respectively. Venetianblinds were installed on the outside of the internal skin in thecavity to control penetration of irradiance from the sun and sky.The depth and distance of each blind slat was 2.54 cm. The blindslats remained horizontal and covered all outside surfaces of theinternal skin.

Openings for the air inlet and outlet were installed at thecenter of the top and bottom of the cavity. The dimensions of theopenings were 0.60 mwide and 0.35 m deep. The remaining areas

Table 4Normalization of the input variables of the ANN model.

Conditions Normalized value

0 w 1

Indoor temperature [�C] �10 w 40Temperature change from the

proceeding 10 min [�C]�10 w 10

Outdoor temperature [�C] �20 w 40Cavity temperature [�C] �20 w 80Solar radiation to vertical surface [W/m2] 0 w 1100Opening condition on internal surface Closed: 0 Opened: 1Opening conditions on external surface Closed: 0 Opened: 1

Fig. 3. Conditions of tested building.

J.W. Moon et al. / Building and Environment 61 (2013) 149e159 153

of the top and bottom surfaces were finished with transparentplastic panels. The opening for the air inlet was 0.3 m above theground. The air outlet was unobstructed and positioned at the topsurface, so air was exhausted naturally. All windows and openingsin the internal skin were closed. Under these conditions, outdoorair was induced into the cavity and naturally circulated.A conceptual explanation for the airflow through the envelope isshown in Fig. 3.

Data monitoring was performed from December 2007 toDecember 2008 on a daily basis to examine the influence of thedouble skin envelope on the heating loads and ventilation ratesin winter. The daily monitoring periods were from 00:00 to24:00, and the monitoring interval was 1 minute. Outdoortemperature, indoor temperature and temperature in the cavitywere measured for monitoring thermal conditions relative to thedouble skin envelope. In addition, photometric and irradiancesensors were used to measure outdoor illuminance and irradi-ance to investigate the effect of irradiance on the variation oftemperature. The sensitivity deviations of the sensors werewithin 1% [27].

Among data monitored, January 1st, 2008, from the eastern sideof the building was used as the training data for the initial ANNmodel. A sliding-windowmethodwas adopted for a data managingtechnique, thus when new training data sets were added, theoldest sets are removed to reflect the new conditions moreeffectively.

3.3. Optimization of the ANN model

The initial ANNmodel was parametrically optimized to producethe least errors of the predicted values from the actuallymonitoredvalues. The optimal number of hidden layers, number of neuronsin the hidden layers, learning rate, and moment were determinedby the comparison of their Root Mean Square (RMS) and MeanSquare Errors (MSE). The 288monitored data sets from the easternside of the building on January 5th, 2008 were used for eachoptimization process. The model was initialized again to resetinitial weights between neurons before each optimization step inorder to avoid the results being affected by the previous optimi-zation process.

J.W. Moon et al. / Building and Environment 61 (2013) 149e159154

The first step of the model optimization was to identify theoptimal number of hidden layers. For identification, the perfor-mance of the ANN model with a different number of hidden layersfrom 1 to 5 was compared, as shown in Table 5. The other param-eters such as number of neurons in the hidden layers, learning rate,and moment were fixed as in the initial model. Fig. 4 shows theRMS of errors between the values predicted by the ANN model andthe monitored value. All five models stably predicted the future

Table 5Performance of tested ANN models.

Variables RMS (rootmeansquare, K)

MSE (meansquareerrors, K2)

Maximumdifference

Positive(K)

Negative(K)

Number of hiddenlayers

1 0.0200 0.0004 0.4612 �0.38382 0.0228 0.0005 0.4501 �0.44993 0.0282 0.0008 0.4608 �0.45944 0.0175 0.0003 0.4562 L0.43155 0.0240 0.0006 0.4332 �0.4681

Number of neurons inthe hidden layers

10 0.0164 0.0003 0.4324 L0.460811 0.0208 0.0004 0.5544 �0.482712 0.0177 0.0003 0.4479 �0.427313 0.0206 0.0004 0.4648 �0.477314 0.0214 0.0005 0.4805 �0.402915 0.0175 0.0003 0.4562 �0.431516 0.0243 0.0006 0.5071 �0.453917 0.0230 0.0005 0.4556 �0.475818 0.0196 0.0004 0.4651 �0.395719 0.0220 0.0005 0.5989 �0.508920 0.0216 0.0005 0.4307 �0.4813

Learning rates 0.00 0.0211 0.0004 0.5475 �0.51400.05 0.0218 0.0005 0.4247 �0.48230.10 0.0192 0.0004 0.5805 �0.36030.15 0.0199 0.0004 0.4530 �0.37370.20 0.0224 0.0005 0.5631 �0.37080.25 0.0214 0.0005 0.5140 �0.45240.30 0.0202 0.0004 0.4420 �0.49870.35 0.0202 0.0004 0.5331 �0.51260.40 0.0174 0.0003 0.4885 �0.50480.45 0.0259 0.0007 0.4819 �0.44260.50 0.0167 0.0003 0.4892 �0.35230.55 0.0181 0.0003 0.4132 �0.48060.60 0.0234 0.0005 0.4618 �0.48250.65 0.0190 0.0004 0.4150 �0.51360.70 0.0231 0.0005 0.5125 �0.43610.75 0.0164 0.0003 0.4324 L0.46080.80 0.0204 0.0004 0.5468 �0.52260.85 0.0214 0.0005 0.4603 �0.50910.90 0.0254 0.0006 0.5133 �0.47310.95 0.0213 0.0005 0.4689 �0.32601.00 0.0218 0.0005 0.5385 �0.4813

Moments 0.00 0.0178 0.0003 0.4574 �0.44180.05 0.0162 0.0003 0.5223 �0.43980.10 0.0238 0.0006 0.4483 �0.43280.15 0.0168 0.0003 0.4380 �0.42840.20 0.0181 0.0003 0.4354 �0.46060.25 0.0245 0.0006 0.4734 �0.43260.30 0.0162 0.0003 0.4033 L0.40670.35 0.0195 0.0004 0.4234 �0.43450.40 0.0172 0.0003 0.4432 �0.45930.45 0.0181 0.0003 0.4711 �0.51240.50 0.0229 0.0005 0.5117 �0.44130.55 0.0182 0.0003 0.4547 �0.44290.60 0.0187 0.0004 0.5391 �0.43420.65 0.0208 0.0004 0.5824 �0.36830.70 0.0220 0.0005 0.4313 �0.46400.75 0.0192 0.0004 0.4699 �0.26970.80 0.0220 0.0005 0.4389 �0.37960.85 0.0225 0.0005 0.4941 �0.41740.90 0.0164 0.0003 0.4324 �0.46080.95 0.0181 0.0003 0.5008 �0.36011.00 0.0201 0.0004 0.4798 �0.4167

indoor temperature conditions, so the RMS values of theerrors were all within the designated training goal (RMS: 0.1 K,MSE: 0.01 K2). The maximum RMS and MSE values were producedby a 3-hidden layer model with 0.0282 K and 0.0008 K2, respec-tively, while the minimum RMS and MSE values were given bya 4-hidden layer model with 0.0175 K and 0.0003 K2. Thus, theoptimal number of hidden layers was determined to be 4, by whichthe maximum positive and negative differences between the pre-dicted and monitored DTemperatures were 0.4562 and �0.4315 K,respectively.

The second step was to determine the optimal number ofneurons in the hidden layers. A variation from 10 to 20 neurons ineach hidden layer was tested, and their accuracy and the results aregiven in Table 5. During the tests, the number of hidden layers wasfixed at 4, derived from the previous step, and other parameters(0.75 learning rate and 0.0 moment) were the same as the initialmodel. As indicated in Fig. 5 and Table 5, the RMS values of errorsand MSE values of all models were stabilized within the goal.Maximum values of RMS and MSE were 0.0243 K and 0.0006 K2 bythe 16 neuron model in the hidden layers, which is much less thanthe goal (0.1 K RMS and 0.01 K2 MSE). The most stable model wasthe model with 10 neurons. The RMS and MSE values for thismodel were 0.0164 K and 0.0003 K2, respectively, and itsmaximum differences between the predicted and monitoredvalues were 0.4324 K and �0.4608 K. Based on this accuracy, the

Fig. 4. RMS of errors by a different number of hidden layers.

Fig. 5. RMS of errors by a different number of neurons in the hidden layers.

J.W. Moon et al. / Building and Environment 61 (2013) 149e159 155

optimal number of neurons in the hidden layers was determinedto be 10.

The third step was to determine the optimal learning rate bycomparison of errors of a series of ANN models with a learningrate from 0.00 to 1.00 with a 0.05 increase size, as shown inTable 5. The number of hidden layers (4), the number of neuronsin hidden layers (10), and the moment (0.90) were fixedduring the comparative tests. Fig. 6 shows that all modelsstably predicted the output, thus the RMS values were all withinthe training goal (RMS 0.1 K). The maximum RMS was producedby a model with a 0.45 learning rate (RMS 0.0259 K). Aminimum value was created by a model with a 0.75 learningrate. Therein, the RMS, MSE, and maximum positive andnegative differences were 0.0164 K, 0.0003 K2, 0.4324 K,and �0.4608 K, respectively. Thus, the optimal learning rate wasdetermined to be 0.75.

The last step of the optimization process was conducted for themoment. A model with different moment rates from 0.00 to 1.00by a 0.05 increase size was tested and the results are given inTable 5. The number of hidden layers (4), the number of neurons inthe hidden layers (10), and learning rate (0.75) were fixed asdetermined in the previous optimization processes. As shown inFig. 7, all 20 models with different moment rates produced errorswithin the designated goal level. Compared to the goal of themodel (RMS 0.1 K), the maximum RMS was generated by a modelwith a 0.25 moment, i.e., 0.0245 K. The optimal moment rate was0.30 with 0.0162 K for RMS, 0.0003 K2 for MSE, and 0.4033 K

Fig. 6. RMS of errors by a different learning rate.

Fig. 7. RMS of errors by a different moment.

and �0.4067 K of maximum differences. Based on a series ofoptimization process, the optimal parameters for the ANN modelin this double skin envelope were determined to be 4-hiddenlayers, 10-neurons in the hidden layers, a 0.75-learning rate, anda 0.30-moment.

4. Performance tests of the developed ANN model

The performance of the optimally tuned ANN model wastested by the comparison of ANN predicting values and actualdata measured from the double skin building. New data sets,which were employed as checking data, were monitored fromthe eastern and western sides for two days representing clearsky (January 2, 2008) and overcast sky (January 11, 2008)conditions.

Variations in outdoor climate conditions under a clear sky dayare shown in Figs. 8 and 9. Vertical solar irradiance on the easternside and western side reached 287.1 W/m2 and 878.1 W/m2,respectively. The variation pattern for the irradiance is shown inFig. 8. The cavity temperature on the west side, which is given inFig. 9, was greater than that on east side due to the strong accu-mulation of solar irradiance during the time period in the after-noon. Since there were no heating operations in the double skinnedbuilding where data monitoring was performed, the indoortemperatures were not over 5.5 �C on the east side and 21.0 �C onthe west side, respectively. The outdoor air temperature variedfrom �10.3 to 3.7 �C.

Fig. 8. Variation of outdoor vertical irradiance (Clear sky).

Fig. 9. Variation of temperature in the tested building (clear sky).

Fig. 11. Variation of temperature in the tested building (Overcast sky).

Fig. 12. Variation of indoor temperature change from a moment to next cycle (Clearsky).

Fig. 10. Variation of outdoor vertical irradiance (Overcast sky).

J.W. Moon et al. / Building and Environment 61 (2013) 149e159156

The solar irradiance and thermal conditions for an overcast skyday are shown in Figs. 10 and 11. The variation of irradiance was notsignificant over the entire day due to the overcast conditions whichprovided only diffused solar irradiance from sky surfaces. No directsolar irradiance from the sun was available under the sky condi-tions. The maximum irradiances on the eastern and western sidewere 30.7W/m2 and 34.1W/m2, respectively. Outdoor temperaturevaried stably and the cavity temperature changed to within thestable range.

The measured and predicted DTemperature and the differenceof DTemperature between the measurement (TEMPME) and ANNprediction (TEMPPR) for a clear sky day were visualized in Figs. 12(a), (b) and 14 (a). In general, the absolute values of measuredDTemperature for an eastern side on a clear day were slightly largerthan those by prediction. The range of measured values was �0.80to 0.80 Kwider than�0.58 to 0.58 K of the predicted values, and theaverages of measured negative and positive DTemperature (�0.17and 0.15 K) were larger than those of predicted (�0.12 and 0.11 K) aswell (Fig. 12 (a)). The RMS and MSE between the measuredand predicted values of the eastern side were 0.02 K and0.0004 K2, respectively. These values were all stably maintainedwithin the designated training goals (0.1 K RMS and 0.01 K2

MSE), which means that the ANN model predicted the TEMPPRaccurately enough, as intended. The maximum differences were

0.446 K and �0.353 K, and 96.2% of the values were within �0.3 K(Fig. 14 (a)).

The range of measured DTemperature for a western side ona clear day was larger with �1.70 to 1.00 K compared to �1.46 to0.98 K by prediction (Fig.12 (b)). The averages of measured negativeand positive DTemperature (�0.32 and 0.35 K) were also largerthan those of predicted (�0.24 and 0.26 K). The RMS andMSE of thewestern side were 0.036 K and 0.0013 K2, respectively, which weresignificantly reduced from the designated goals. This proves theadaptability of the developed ANN model for the different back-ground conditions on a clear sky day. The maximum differences,which were also slightly larger than those of the east side, were0.458 K and �0.554 K, respectively (Fig. 14 (a)).

The DTemperature by measurement and prediction as well asthe differences of DTemperature between the measurement andthe prediction for an overcast day are shown in Figs. 13 (a), (b) and14 (b). Since the input variables of the ANN model, i.e., outdoortemperature, cavity temperature and solar irradiance, were notsignificantly changed during the day, the ANN model for theeastern side was predictedmore accurately on an overcast day thana clear sky day. The RMS and MSE were reduced to 0.009 K and0.0001 K2, and the maximum differences also became smaller at0.248 K and �0.248 K, meaning all the values are within �0.3 K(Fig. 14 (b)). The RMS and MSE of the western side were similar tothose of the clear sky conditions, as shown in Fig. 14 (b). Thosevalues were 0.036 K and 0.0013 K2, whichwere alsowithin the ANNmodel’s goal. The maximum differences were reduced to 0.414 Kand �0.417 K.

Fig. 14. Variation of indoor temperature change from a moment to next cycle.Fig. 13. Variation of indoor temperature change from a moment to next cycle (Over-cast sky).

J.W. Moon et al. / Building and Environment 61 (2013) 149e159 157

Similar to the clear sky conditions, the values of measuredDTemperature were slightly larger than those by prediction. Theranges were �0.20 to 0.20 K by measurement and �0.16 to 0.16 Kby prediction for the eastern side (Fig. 13 (a)). And, the averagevalues of negative and positive DTemperature were �0.12 and0.10 K by measurement and �0.05 and 0.06 K by prediction. Onthe other hand, the range by measurement was smallerwith �0.50 to 0.50 K compared to �0.54 to 0.64 K by predictionfor the western side (Fig. 13 (b)). However, average values ofmeasurement (�0.21 and 0.23 K) were larger than prediction(�0.17 and 0.18 K).

In this study, field measurements for double skin envelopesystems were conducted under limited conditions. Hence, theinfluence of double skin envelope on the indoor air temperaturechange to next cycle were analyzed using ANNmodel developed forcontrol logic. Although the ANN model was proved to providereliable prediction results under various weather conditions,deviation between simulation andmeasurement was considered toexist due to limitation of computation algorithms applied to ANNprediction models.

Therefore, the indoor air temperature change to next cyclepredicted by the ANN model (TEMPPR) was validated by the indoorair temperature change to next cycle measured in field measure-ments (TEMPME) to investigate the deviation between them. Alinear regression method that minimizes the error sum of squaresand ANOVA tests were employed to examine the deviation. For thevalidation models, the measured indoor air temperature change tonext cycle was considered independent variables and the predicted

indoor air temperature change to next cycle was considered asdependent variables.

The regression results for eastern and western envelopeconditions under clear sky are shown in Fig. 15. Overall, thecorrelation between measured and predicted values was strongand the regression models were acceptable under a significancelevel of 0.01 as shown in the ANOVA test results in Table 6. Thecoefficient of determination (r2) for eastern and western envelopeconditions were 0.4996 and 0.7720 respectively. This resultindicates that the error variance in the indoor air temperaturechange predicted by the ANN model to next cycle (TempPR) wasreduced by 49.96% and 72.2% respectively, when the measuredindoor air temperature change to next cycle (TEMPME) was usedto predict the indoor air temperature change to next cycle by ANNmodels.

The linear correlation for western side under clear sky condi-tion was stronger than that for eastern side conditions. It appearsthat the stronger correlation occurred due to the continuousaccumulation of direct solar irradiance from the sun and diffusedirradiance from the sky surface as time passed by from morning.The regression models showed that the ANN models developed inthis study can be effectively applied to predict air change inbuildings.

In summary, the analysis results of performance tests providea strong foundation for using the ANN models to predict indoortemperature under a variety of control option for cavity space ofdouble skin envelope. Based on this accuracy of predictions andadaptability, the ANN model proved its potential to be applied inthe thermal control logic in double skin envelope buildings.

Table 6ANOVA test result for prediction models between TEMPME and TEMPPR.

Factors Clear sky, eastern side Clear sky, western side

Unstandardizedcoefficients

t Sig. Unstandardizedcoefficients

t Sig.

B Std. error B Std. error

(Constant) �0.0171 0.01 �2.45 0.01 0.0144 0.01 1.45 0.15TEMPME 0.5987 0.04 16.88 0.00 0.7778 0.02 31.12 0.00ANOVA R2 ¼ 0.4996, F(1, 286) ¼ 285.01,

Sig. ¼ 0.00R2 ¼ 0.7720, F(1,286) ¼ 968.25,Sig. ¼ 0.00

Fig. 15. Prediction models for the relationship between TEMPME and TEMPPR.

J.W. Moon et al. / Building and Environment 61 (2013) 149e159158

5. Conclusions and future works

This study aims at proposing an ANN-based thermal controlmethod for double skin envelope buildings. A thermal control logicfor controlling heating systems and openings on the internal andexternal envelope was developed using the ANN model. Thesummary of finding is as follows.

1) The proposed control logic using the ANN model can controlthe heating system and openings on the double skin based onthe predicted future thermal condition of the indoor space. Thelogic can also adaptively control the heating system and

openings through the self-tuning process for variations of thebuilding background conditions such as change of orientationsand weather conditions in winter.

2) The ANN model, employed in the control logic, was para-metrically optimized for the structure and for the learningmethods. Through the parametrical performance tests, theoptimal parameters of the ANN model to optimally conditionindoor thermal environment and to effectively save heatingenergy were determined as 4-hidden layers, 10-neurons in thehidden layers, a 0.75-learning rate, and a 0.30-moment.

3) Through the performance tests being conducted for differentorientations and sky conditions, the developed ANN modelproved its prediction accuracy and adaptability with stableRMS, MSE, and maximum values of the temperature differencewith the monitored data for the different orientations of thedouble skin envelope.

These findings regarding the prediction accuracy and adapt-abilities identified the proposed ANN model’s potential to beapplied in thermal control logic in double skin envelope buildings.Based on the outcomes of this study, further study is warranted foran expanded control logic, which can be applied for weatherconditions for the entire in year. The ANN model performanceshould be tested using data from the actual operations of thethermal control systems and the openings for all year. In addition,experimental and simulation research needs to be conducted forapplication of the developed logic to diverse building types.

Acknowledgments

This research was supported by the Basic Science ResearchProgram through the National Research Foundation of Korea (NRF)funded by theMinistry of Education, Science and Technology (grantnumber: 2012R1A1A1005272) and by the Sustainable BuildingResearch Center of Hanyang University, which was supported bythe SRC/ERC program of MEST (R11-2005-056-01003-1).

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