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Comparative study of artificial intelligence-based building thermal control methods – Application of fuzzy, adaptive neuro-fuzzy inference system, and artificial neural network
Applied Thermal Engineering 31 (2011) 2422e2429
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Applied Thermal Engineering journal homepage: www.elsevier.com/locate/apthermeng
Comparative study of artificial intelligence-based building thermal control methods e Application of fuzzy, adaptive neuro-fuzzy inference system, and artificial neural network Jin Woo Moon a, Sung Kwon Jung b, Youngchul Kim b, Seung-Hoon Han a, * a b
School of Architecture, Chonnam National University, 300 Yongbongdong, Bukgu, Gwangju 500-757, South Korea 2000 Bonisteel Blvd, TCAUP, University of Michigan, Ann Arbor, MI 48105, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 21 October 2010 Accepted 7 April 2011 Available online 20 April 2011
This study’s aim is to develop diverse Artificial Intelligence-based (AI-based) thermal control logics and to compare their performances for identifying potentials as an advanced thermal control method in buildings. Towards that aim, three AI-based control logics have been developed: i) Fuzzy-based control; ii) ANFIS-based (Adaptive Neuro-Fuzzy Inference System-based) control; and iii) ANN-based (Artificial Neural Network-based) control. The last-mentioned two were adaptive methods employing iterative self-tuning process during system operation. Each method’s performance was tested in a typical twostory residential building in USA, via computer simulation incorporating IBPT (International Building Physics Toolbox) and MATLAB. In analysis of test results for indoor air temperature, thermal comfort profiles, and amount of heat supply and removal, two adaptive control methods e ANFIS-based and ANN-based e significantly stabilized thermal conditions by the increased comfort period and the decreased deviations from the set-point compared to the Fuzzy-based non-adaptive method. No control method showed significant energy saving effects over the other. In conclusion, adaptive AI-based control methods have potential to maintain interior air temperature more comfortably. Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: Artificial intelligence Fuzzy control Neuro-fuzzy systems Artificial neural network Building thermal control
1. Introduction Artificial Intelligence (AI) has been applied to advanced building environmental controls such as thermal-, lighting-, and air-quality controls. With the substantial advantage of applicability in nonlinear systems or systems with unclear dynamics, AI-based control methods incorporating Fuzzy Logic (FL), Adaptive NeuroFuzzy Inference System (ANFIS), and Artificial Neural Network (ANN) successfully have created more comfortable environments in buildings [1,2]. In thermal quality, advanced logics using AI models are being investigated for optimal control of thermal conditions with improved energy efficiency. 1.1. Fuzzy logic (FL) Fuzzy Logic (FL), developed by Zadeh, deals with the degree of truth or falsity of phenomena. FL comprises three basic processes: 1) Fuzzification (input stage) for obtaining input values and translating them to Fuzzy type via transfer function/s such as triangular, * Corresponding author. Tel.: þ82 62 530 0639; fax: þ82 62 530 1915. E-mail address: [email protected] (S.-H. Han). 1359-4311/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.applthermaleng.2011.04.006
trapezoidal, Gaussian distribution curve, sigmoidal functions, etcetera; 2) Inferencing (processing stage) for generating inferred results based on previously fuzzified values and linguistic IF/THEN statements; and 3) Defuzzification (output stage) for totaling the result by linguistic rules, then converting it to specific output values. Benefiting from the non-requirement of precise and noise-free inputs resulting in the implementation of cheaper sensors, low resolution A/D converters and cheap microcontroller chips, Fuzzy-based control methods are being applied to building thermal controls [3,4]. The FL method, employing two inputs (E: difference between desired temperature and current temperature and dE: difference between current error and previous error) for creating one output (heating/cooling power), was comparatively tested with on/off control, PI, and PID controller for HVAC in a hospital building, using computer simulation. The FL controller showed less overshoot, oscillation, and power consumption than did conventional on/off, PI and PID methods. In addition, Fuzzy controllers provided better performance when system parameters were changed [5]. In a similar study comparatively testing an FL model with a classical closed-loop control method in an office building, the FL-based method maintained interior temperature better following the settemperature both in occupied- or non-occupied periods [6].
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Nomenclature T TNEW TH TC E ENEW EOLD dE dENEW dEOLD U UNEW UOLD UTRN ni nh no nd
air temperature ( C) air temperature of current cycle ( C) set-point temperature for heating system ( C) set-point temperature for cooling system ( C) difference between air temperature from set-point temperature ( C) E of current cycle ( C) E of previous cycle ( C) change of E from previous cycle ( C) dE of current cycle ( C) dE of previous cycle ( C) output (ratio, unitless) U of current cycle (ratio, unitless) U of previous cycle (ratio, unitless) U for new training-data set (ratio, unitless) number of input neurons number of hidden neurons number of output neurons number of data sets
In the other study, employing internal PMV (Predicted Mean Vote), which describes the overall thermal comfort considering dry-bulb temperature, relative humidity, air velocity, mean radiant temperature, individual activity and clothing level, and external temperature as inputs for calculating operating signals for air heating, air cooling, and window opening angle, as outputs, the FL control-method targeted indoor PMV was investigated. In the computer simulation conducted for winter and summer seasons, PMV control with a Fuzzy model comfortably conditioned not only PMV, but also air temperature and humidity [7,8]. A similar Fuzzybased method was compared with a PID method. For the PMV control, the Fuzzy controller more quickly reached desired thermal conditions, was more energy efficient e saving some 20% of energy, and was more flexible in change of system parameters, than was the tuned PID controller [9]. In addition, a Fuzzy PID system using E (difference between desired PMV and current PMV) and dE (difference between current error and previous error) as inputs for generating a fan-rotating speed as output, proved its potentials using computer simulation and experiment [10]. Recently, Fuzzy logic began to be applied to controlling wholebuilding environment including thermal-, air-, and lighting- quality. An FL-based IEEMS (Indoor Environment Energy Management System) used PMV, outdoor temperature, indoor CO2 concentration, change ratio of CO2 concentration, and illuminance as inputs for generating heating/cooling operation, window opening, and shading-device operation as outputs. In the experiment and computer simulation for the existing building, CO2, PMV and illuminance levels were conditioned successfully and more than 30% energy was saved, compared to conventional on/off controls [11,12]. In another study, a Fuzzy Logic Controller (FLC) was implemented in the variable refrigerant volume (VRV) e variable air volume (VAV) A/C system for optimal thermal and air quality control, in which the FLC generated compressor speed (rpm), fan speed (rpm), and damper opening (%) as outputs. This system provided improved thermal comfort, air quality, and energy efficiency, compared to the conventional constant air volume (CAV) A/C system [13].
1.2. Adaptive neuro-fuzzy inference system (ANFIS) Inherently, Fuzzy logic has difficulty in developing optimal Fuzzy rules and membership functions, so, in many cases, they have
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been organized intuitively. To overcome this difficulty, a new approach incorporating FL and Neural Network has been introduced for developing a globally-applicable control model. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is one of the Neuro-Fuzzy approaches. Neuro-adaptive learning techniques are implemented for tuning membership function parameters, to permit the Fuzzy inference system to track given input/output data. The tuning process of membership function parameters are similar to those conducted in the ANN, which employs training-data sets comprising input/output data and training algorithm such as backpropagation. Based on this tuning process, ANFIS obtains the best parameters to produce output responding optimally to the given environment. In addition, by the iterative retuning process for adaptation, a global solution is obtainable [1,2,14]. A comparative study using ANN and ANFIS models was conducted for performance analysis of the evaporative condenser. In this study, ANN-based and ANFIS-based models were developed having the same inputs ([i] dry-bulb and [ii] wet-bulb temperatures of the air stream entering the condenser, mass flow rates of [iii] air, [iv] water and [v] refrigerant, and [vi] pressure and [vii] temperature of the refrigerant) for calculating the same output ([i] condenser heat rejection rate, [ii] refrigerant temperature leaving the condenser, [iii] dry-bulb, and [iv] wet-bulb temperatures of the air stream leaving the condenser). Statistical testing proved a slight superiority of the ANFIS model although both methods were successful in predicting condenser performance [15]. In another study, an ANFIS model was developed for controlling damper gap rates in the HVAC system based on indoor temperature and humidity conditions. Using error and change of error of temperature and humidity from required levels, the Neuro-Fuzzy model predicted ratios of two dampers e one for direct diffusion of supply air to the interior space, the other for humidification of supply air through the humidifier before diffusion. Using predicted ratios, this model proved faster, simpler and more efficient in controlling temperature and humidity conditions [16]. A similar study was conducted for implementing ANFIS for calculating damper ratio and fan speed in the HVAC system [17]. 1.3. Artificial neural network (ANN) Artificial Neural Network (ANN), analogous to human neural structure and its learning process, increasingly has been applied to advanced thermal control of buildings. ANN utilizes connectivity and transfer functions between input-, hidden-, and outputneurons for calculating optimal output. Different from mathematical models such as regression models or proportional-integralderivative (PID) controllers, ANN models have adaptability via a self-tuning process, so can decide accurately without external expert intervention for retuning model parameters [18,19]. Studies proved that, as a thermal control method, the ANNbased predictive control strategy has advantages over mathematical strategies in terms of accurate thermal control with reduced overheating and overcooling, and improved energy efficiency [20e28]. ANN-based algorithms were developed for determining optimal start- and stop-times of heating systems using predicted values from ANN in their algorithms. In one study, ANN models predicted optimal turn-on time of the heating system for restoring interior air temperature to a comfortable level and, in another, the length of time to drop the interior air temperature to the lower limit of comfort range. Using these predicted values, suggested control methods demonstrated advanced thermal comfort and energy efficiency [20,21]. A similar ANN model was implemented for predicting the end-of-setback moment of A/C systems [22]. ANN also successfully has been applied to hydronic heating systems of solar buildings with significant energy savings, in which
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Fig. 3. Membership function plots of U (output).
Fig. 1. Membership function plots of E (1st Input).
ANN models predicted outdoor temperature, solar radiation, indoor temperature, and supply temperature [23,24]. In particular, ANNbased predictive control methods effectively controlled residential water-heating systems and radiant floor-heating systems with significant time-lag [25e27]. More recently, a radiant heating system in a residential building successfully was controlled by ANN and incorporated Fuzzy logics. The ANN model predicted indoor air temperature, and this predicted value was utilized as input in the Fuzzy Logic. Compared to the PI control, this incorporated controller of ANN and FL reduced overshoots of air temperature and energy consumption [28]. 2. Problem statement Artificial Intelligence (AI) has been employed for optimal control of thermal conditions in buildings. Studies proved thermal comfort and energy efficiency by AI-based methods. However, each study tends to focus on the application of one specific theory such as Fuzzy, ANFIS, ANN and its comparison to the conventional control method. On the other hand, the performance of AI-based methods has not yet been compared fully. Therefore, a study comparing the performance of each AI-based method needs to be conducted for identifying their substantial potentials as a thermal control strategy. 3. Objective This study aimed to develop diverse AI-based thermal control logics and to compare their performances for identifying potentials as an advanced thermal control method in buildings. Towards that
aim, three AI-based control logics have been developed: i) Fuzzybased control; ii) ANFIS-based control; and iii) ANN-based control. Control logics produce variable output for the control system. Test results for indoor air temperature, thermal comfort profiles, and amount of heat supply and removal of each control method, using computer simulation, identified pros and cons of each control logic and are expected to provide solid foundation to suggest optimal thermal control methods in buildings. 4. Development of control logics 4.1. Fuzzy-based control Fuzzy logic was employed for control of interior air temperature, in which a Fuzzy model produced various amounts of heat supply and removal. A Fuzzy Logic toolbox in MATLAB was used for developing logic algorithm. A Fuzzy model in this study was developed with two input variables (E and dE) and one output variable (U). Membership functions of variables, given in Figs. 13, were organized based on the heuristic tuning process. Trapezoidal and triangular shapes are used for membership functions. Ranges of each variable are 2.0 to 2.0 ( C) for E, 2.0 to 2.0 ( C) for dE, and 2.0 to 2.0 for U. Output U, in system operation, indicates system operating ratio. When the calculated output from the Fuzzy model was under 1.0 or over 1.0, it was converged to 1.0 and 1.0, respectively. Accordingly, the range of output was between 1.0 and 1.0. Table 1 summarizes the Fuzzy rules using an IF-THEN structure which is the combination of two inputs and one output. 4.2. ANFIS-based control A Sugeno-type ANFIS (Adaptive Neuro-Fuzzy Inference System) model was developed using the Fuzzy Logic toolbox in MATLAB. Similarly to the previous Fuzzy-based logic, this ANFIS-based logic generates variable output for the control system. Input variable Table 1 Fuzzy If-Then Rules. Inputs (IF)
Outputs (THEN)
ENEW COLD COLD COMFORT COMFORT HOT HOT Fig. 2. Membership function plots of dE (2nd input).
and and and and and and
dENEW
UNEW
COLDER HOTTER COLDER HOTTER COLDER HOTTER
HEATING HEATING HEATING COOLING COOLING COOLING
J.W. Moon et al. / Applied Thermal Engineering 31 (2011) 2422e2429
c. Influenced by heating system operation, current temperature becomes TNEW ¼ 21.4 ( C) at the current cycle, i.e., slightly colder than TH (Set-temperature for heating system, 21.5 C). d. Using UOLD, TNEW and TH, UTRN could be calculated.
Table 2 ANFIS If-Then rules. Inputs (IF)
Outputs (THEN)
ENEW COLD COLD COMFORT COMFORT HOT HOT
and and and and and and
dENEW
UNEW
COLDER HOTTER COLDER HOTTER COLDER HOTTER
Output Output Output Output Output Output
membership membership membership membership membership membership
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function function function function function function
1 2 3 4 5 6
types were identical with the Fuzzy-based logic (Figs. 1 and 2) while different output membership functions and rules were employed as given in Table 2. Through the iterative training process during the system operation, the model continuously adjusted parameters of output membership functions, to produce optimal output (UNEW) in the given environment, thus the logic better could control the system and create more comfortable thermal conditions. The algorithm is graphically illustrated in Fig. 4. In the algorithm, the ANFIS model was trained with training-data sets which added new input sets: EOLD (ENEW of the previous cycle) and dEOLD (dENEW of the previous cycle), and output UTRN, replaced the oldest set. The example for obtaining the new training-data set is given below. a. Let EOLD ¼ 1.0 ( C) and dEOLD ¼ 0.1 ( C), which means the temperature was cold (20.5 C) and dropping at the previous cycle. b. For this condition, assume there had been UOLD ¼ 0.5 calculated by ANFIS model at the previous cycle, which means a heating system was working at half capacity.
UTRN ¼ UOLD þ UOLD (TH TNEW) ¼ 0.5 e 0.5 (21.5 e 21.4) ¼ 0.55 This 0.55 is the more desirable value for previous EOLD ¼ 1.0 ( C) and dEOLD ¼ 0.1 ( C) conditions. In this case, more heating (0.55 instead 0.5) should have been supplied between the previous and current cycles. e. Add EOLD ¼ 1.0 ( C) and dEOLD ¼ 0.1 ( C) as inputs, and UTRN ¼ 0.55 as output to the training-data sets. This is the sliding-window method, so the new training-data set was added to training-data sets, replacing the oldest. f. Train ANFIS model with new training-data sets. g. Calculate UNEW from the newly trained ANFIS model and use it for the heating system. h. Repeat this process each minute. Most parameters for the training model followed the recommended default values in the Fuzzy Logic toolbox, MATLAB: 30 epochs, more than default 10; 0.0 error tolerance; 0.01 initial stepsize; 0.9 step-size decrease rate; 1.1 step-size increase rate; back-
Fig. 4. Algorithm for ANFIS- or ANN-based controls.
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ANN model, instead of the ANFIS model, was used in the training step and the output calculating step. Thus, the sequence for obtaining training-data sets, training, and calculating were similar to the example in 4.2. nh ¼ 2 ni þ 1
(1)
nd ¼ (nh e (ni þ no)/2)2
(2)
5. Performance test
Fig. 5. ANN model for calculating the ratio of system operation (U).
propagation training method [14]; and 40 training-data sets, similar to those used in the ANN-based control logic describing in 4.3. 4.3. ANN-based control Using MATLAB and its Neural Network (NN) toolbox, an artificial neural network (ANN) model was developed to calculate a variable ratio of system operation. The model structure is shown in Fig. 5. Empirical solutions proven in previous studies were employed for model development. The model comprised an input layer, a hidden layer, and an output layer. The input layer consisted of two neurons: E and dE. The hidden layer employed five neurons based on Equation (1) [29,30]. Output of the output layer is U. Forty training-data sets, intuitively acquired in advance and updated with actual data using the sliding-window method during operation, were prepared for each model, based on Equation (2) suggesting a minimum number of sets [31]. A training goal (mean square error (MSE)) was set to 0.0 with a maximum 30 epoch, 0.75 learning rate, and 0.9 momentum based on research conducted by Yang et al. [32]. The LevenbergeMarquardt algorithm was used as a training algorithm considering training speed and accuracy [14]. Identical control algorithm with ANFIS-based variable control was applied for the temperature control given in Fig. 4. Only the
Developed control logics performance was tested through computer simulation. Two major means were incorporated for the simulation, namely International Building Physics Toolbox (IBPT) and MATLAB. IBPT was used for (1) modeling building components and related features (e.g., envelopes, ventilation rate, internal load, control systems, initial thermal conditions, and import of weather data), and (2) calculating interior air temperature conditions. Using calculated air temperature values, MATLAB was utilized for (1) developing control algorithms, (2) calculating U using Fuzzy and neural network toolboxes, (3) deciding operation of control devices based on calculated U value, and (4) training ANFIS and ANN models. The system operation decision was fed into the IBPT for system operation, and the interior air temperature condition (newly-created by such system working) was used in MATLAB iteratively [14,19,33]. Simulation tool validity was proved with acceptable differences in the amount of energy consumption and temperature profile, compared to experimental results in the previous study [18]. Based on the American Housing Survey [34], a USA-typical, twolevel, single-family home equipped with heating and cooling systems in cold-climate Detroit, Michigan, USA (Fig. 6) was used as a test model. Details of a test building are given in Table 3. Control systems were composed of convective heating (e.g., furnace) and cooling systems (e.g., DX coils) which had set-points 21.5 C for heating system and 24.5 C for cooling system based on the ASHRAE recommended temperature comfort range [35]. Each system was designed to work following the operating ratio that was calculated from the control logics in the MATLAB (previous Figs. 1e4). Following the operating ratio (from 0 to 1) fed into the IBPT, systems can change their amount of heat supply and removal in this study (from 0 to 100%), which could be generally applied in the real building systems by control of a damper gap, fan-rotating speed, inlet air temperature, etc. For example, if the calculated output from the logic in the MATLAB is 0.25 for heating in the
Fig. 6. A Typical U.S. Single-Family Home used in Simulation [19,20,34].
J.W. Moon et al. / Applied Thermal Engineering 31 (2011) 2422e2429
6. Results and analysis
Table 3 Descriptions of a test building. 184.8 m2 (92.4 m2 for each floor)
Area Envelope
Insulation SI ( K m2/W) (U.S. ( F ft2 hr/Btu))
Window Wall Ratio
Internal gain
Infiltration rate Systems applied [38]
Weather data Assumptions
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Walls R3.3 (R19.0 Roof R6.7 (R38.0 Floor R3.7 (R21.0 Windows R0.6 (R3.44 Doors R0.2 (R1.22 0.15 on average (0.24 for south, 0.08 for north, 0.14 for east, 0.13 for west) Hourly-weighted heat and moisture gains for a family of four people [36, 37] 0.35 ACH Convective heating: 21,394Watt heat supply Convective cooling: 10,198Watt heat removal TMY2 for Detroit, Michigan, USA Initial air temperature: 23 C Initial humidity: 45%
U.S.) U.S.) U.S.) U.S.) U.S.)
winter, the heating system in the IBPT supplies 5348.5Watt (21,394Watt X 0.25) of heat as a power to the space. As a result of system operation in the IBPT, the temperature conditions in the space can be maintained comfortably near the set-points. Logics were tested for six days for winter (January 27 to February 1, representing heating-peak days) and summer (July 3 to July 8, representing cooling-peak days). For data analysis, the first day was trimmed and the last five days were analyzed.
Each logic’s performance was analyzed in terms of profiles of indoor air temperature, thermal comfort, and amount of heat supply and removal. 6.1. Indoor air temperature conditions Indoor air temperature conditions by developed control logics are shown comparatively for a sampled period in winter (Fig. 7) and summer (Fig. 8). From the top, they are by Fuzzy-based control; Fuzzy-based control after tuning; ANFIS-based control; and ANNbased control. Fuzzy-based control conditioned temperature properly reaching to set-points in both seasons (21.5 C for winter and 24.5 C for summer), although fluctuations occurred around set-points (1st chart of each Figure). The proper conditioning using the Fuzzy model was due to the heuristic tuning process, by which outputs were properly calculated to heat up and cool down space. However, this tuned model does not always guarantee successful applicability to the space with different background conditions. ANFIS- and ANN-based control logics more successfully conditioned temperature following the set-point, which proves the adaptability of these methods (2nd and 3rd charts of each Figure). However, the reasons that air temperature conditioned below setpoint in the early period (0:00e7:00) in both seasons were 1) coldest weather exceeded heating-system capacity in winter and 2) cooler weather required no further cooling in summer. The average conditioned temperatures by each control method for the whole simulation period were 20.32 C, 21.41 C, 21.43 C, and 21.43 C for winter, and 25.28 C, 24.57 C, 24.40 C, and 24.34 C for summer, respectively.
Fig. 7. Profile of interior air temperature (January 28, winter).
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Fig. 8. Profile of interior air temperature (July 5, summer).
6.2. Thermal comfort profiles
6.3. Amount of heat supply and removal
Thermal comfort created by each logic was compared in terms of percentage of comfort period and deviation from set-point. Percentages of periods when indoor air temperature is within specified ranges (2023 C winter and 2326 C summer) are in Table 4. In winter, Fuzzy-, ANFIS-, and ANN-based methods controlled temperature conditions in a similar way (98.3%, 98.4% and 98.1% comfort period, respectively in winter). In summer, all three control methods satisfied temperature conditions well within specified comfort range. In the analysis on the standard deviations of air temperature from the set-point (21.5 C winter, 24.5 C summer) for three AI-based methods to identify how each logic maintains air temperature close to set-point (Table 5), two adaptive methods (ANFIS-based and ANN-based control methods) clearly reduced the amount of standard deviation (all values under 0.2 C for both seasons) compared to the Fuzzy-based control method (0.22 C winter and 0.24 C summer). From the analysis of temperature profiles, percentage of comfort period, and deviation from set-points, all the AI-based methods controlled temperature conditions properly, in particular, two adaptive models using ANFIS and ANN maintained temperature conditions more successfully near the set-points.
As a way of measuring energy consumption by climate control equipments controlled by different control logics, amounts of heat supply by a heater and heat removal by an air-conditioner were calculated and compared (Table 6). For the three control methods, the amount of heat supply and removal did not show significant difference. Fuzzy-based control method, however, presented the least amount of heat supply in winter (689.5 KWh) and removal in summer (332.4 KWh). It was due to the lower temperature conditioned (21.41 C) in winter and higher (24.57 C) in summer by the Fuzzy-based method compared to those of ANFIS- and ANNbased controls, which were 21.43 C for both methods in winter and 24.40 C and 24.34 C in summer, respectively.
Table 4 Percentage of periods (%) within specified comfort ranges (2023 C winter and 2326 C summer).
Table 5 Standard deviation ( C) from set-points by control methods. Control Methods
Winter
Summer
Fuzzy-based control ANFIS-based control ANN-based control
0.22 0.13 0.13
0.24 0.17 0.19
Table 6 Amount of heat supply in winter and removal in summer (KWh).
Control Methods
Winter
Summer
Control methods
Winter
Summer
Fuzzy-based control ANFIS-based control ANN-based control
98.3 98.4 98.1
100.0 100.0 100.0
Fuzzy-based control ANFIS-based control ANN-based control
689.5 690.3 692.5
332.4 340.7 343.3
J.W. Moon et al. / Applied Thermal Engineering 31 (2011) 2422e2429
7. Conclusions In this study, three AI-based thermal control methods were developed and their performances were compared for identifying potentials as an advanced thermal control method in buildings. A computer-simulation method incorporating MATLAB and IBPT was employed for testing performance of logics. Test results were analyzed in terms of indoor air temperature conditions, thermal comfort profiles, and amount of heat supply and removal. Findings from this study are: Fuzzy-based control conditioned temperature properly following set-points due to the tuning process. However, that does not mean model adaptability. In other words, this tuned model may not work properly in a building with new parameters such as change of control system capacity or change of building configurations. Adaptive ANFIS- and ANN-based control logics more successfully conditioned temperature following the set-point and their standard deviations from set-points thus were reduced significantly. These improvements proved the necessity of the iterative training process during the logic and system operation. Tuned model or adaptive models did not present a significant energy saving effect. In conclusion, adaptive AI-based control methods using ANFIS and ANN models have presented substantial potential for enhancing thermal comfort with improved stability. This study’s findings could provide solid foundation for identifying the optimal building thermal control method in future research.
Acknowledgements This work was supported by the Grant of the Korean Ministry of Education, Science and Technology (The Regional Core Research Program/Biohousing Research Institute) and by the Biohousing Research Center.
References [1] A.I. Dounis, C. Caraiscos, Advanced control systems engineering for energy and comfort management in a building environmenteA review, Renewable and Sustainable Energy Reviews 13 (2009) 1246e1261. [2] M. Krarti, An overview of artificial intelligence-based methods for building energy systems, Journal of Solar Energy Engineering 25 (2003) 331e342. [3] S.D. Kaehler, Fuzzy Logic e An Introduction, Encoder e The Newsletter of the Seattle Robotics Society (2006-09-19).http://www.seattlerobotics.org/ encoder/mar98/fuz/fl_part1.html. [4] G. Goebel, An Introduction to Fuzzy Control Systems (2006-09-19).http:// www.faqs.org/docs/fuzzy/. [5] E. Kaymaz, Adaptive environmental control for optimal energy consumption in hospitals, Computer Based Medical Systems, Proceedings of the Eighth IEEE Symposium (1995) 165e172. [6] G. Fraisse, J. Virgone, J.J. Roux, Thermal control of a discontinuously occupied building using a classical and a fuzzy logic approach, Energy and Buildings 26 (1997) 303e316. [7] A.I. Dounis, E.D. Manolakis, Design of a fuzzy system for living space thermal comfort regulation, Applied Energy 69 (2001) 119e144. [8] A.I. Dounis, M.J. Santamouris, C.C. Lefas, A. Argiriou, Design of a fuzzy set environment comfort system, Energy and Buildings 22 (2005) 81e87. [9] M.M. Gouda, S. Danaher, C.P Underwood, Thermal comfort based fuzzy logic controller, Building Services Engineering Research and Technology 22 (2001) 237e253.
2429
[10] F. Calvino, M.L. Gennusa, G. Rizzo, G. Scaccianoce, The control of indoor thermal comfort conditions: introducing a fuzzy adaptive controller,, Energy and Buildings 36 (2004) 97e102. [11] D. Kolokotsa, K. Niachou, V. Geros, K. Kalaitzakis, G.S. Stavrakais, M. Santamouris, Implementation of an integrated indoor environment and energy management system, Energy and Buildings 37 (2005) 93e99. [12] D. Kolokotsa, Comparison of the performance of fuzzy controllers for the management of the indoor environment, Building and Environment 38 (2003) 1439e1450. [13] R. Karunakaran, S. Iniyan, R. Goic, Energy efficient fuzzy based combined variable refrigenrat volume and variable air volume air conditioning system for buildings, Applied Energy 87 (2010) 1158e1175. [14] MathWorks, MATLAB 14, http://www.mathworks.com, 2005-12-10e17. [15] H.M. Ertunc, M. Hosoz, Comparative analysis of an evaporative condenser using artificial neural network and adaptive neuro-fuzzy inference system, International Journal of Refrigeration 31 (2008) 1426e1436. [16] S. Soyguder, H. Alli, An expert system for the humidity and temperature control in HVAC systems using ANFIS and optimization with Fuzzy Modeling Approach, Energy and Buildings 41 (2009) 814e822. [17] S. Soyguder, H. Alli, Predicting of fan speed for energy saving HVAC system based on adaptive network based fuzzy inference system, Expert Systems with Applications 36 (2009) 8631e8638. [18] J.W. Moon, J.J. Kim, ANN-based thermal control models for residential buildings, Building and Environment 45 (2010) 1612e1625. [19] J.W. Moon, S.K. Jung, J.J. Kim, Application of ANN (Artificial-Neural-Network) in Residential Thermal Control, 11th International Building Performance Simulation Association Conference, Building Simulation 2009. University of Strathclyde, Glasgow, July 27-30, 2009, 64e71. [20] M.S. Yeo, K.W. Kim, Application of artificial neural network to predict the optimal start time for heating system in building, Energy Conversion and Management 44 (2003) 2791e2809. [21] I.H. Yang, K.W. Kim, Development of artificial neural network model for the prediction of descending time of room air temperature, International Journal of Air-Conditioning and Refrigeration 12 (2000) 1038e1048. [22] A.E. Ben-Nakhi, M.A. Mahmoud, Energy conservation in buildings through efficient A/C control using neural networks, Applied Energy 73 (2002) 5e23. [23] A.A. Argiriou, I. Bellas-Velidis, M. Kummert, P. Andre, A neural network controller for hydronic heating, systems of solar buildings,, Neural Networks 17 (2004) 427e440. [24] A.A. Argiriou, I. Bellas-Velidis, C.A. Balaras, Development of a neural network heating controller for solar buildings, Neural Networks 13 (2000) 811e820. [25] N. Morel, M. Bauer, M. El-Khoury, J. Krauss, Neurobat, A predictive and adaptive heating control system using artificial neural networks, International Journal of Solar Energy 21 (2001) 161e201. [26] J.Y. Lee, M.S. Yeo, K.W. Kim, Predictive control of the radiant floor heating system in apartment buildings, Journal of Asian Architecture and Building Engineering 1 (2002) 105e112. [27] J.Y. Lee, I.H. Yang, S.Y. Song, H.S. Kim, K.W. Kim, A Study of the Predictive Control of the Ondol System in Apartments. International Building Performance Simulation Association, Kyoto, September 13e15, 1999, 215e222. [28] G.G. Gouda, S. Danaher, C.P. Underwood, Quasi-adaptive fuzzy heating control of solar buildings, Building and Environment 41 (2006) 1881e1891. [29] J. Yang, H. Rivard, R. Zmeureanu, On-line building energy prediction using adaptive artificial neural networks, Energy and Buildings 37 (2005) 1250e1259. [30] D. Datta, S.A. Tassou, D. Marriott, Application of Neural Networks for the Prediction of the Energy Consumption in a Supermarket. Clima 2000, Brussels, Belgium, 1997, 98e107. [31] S.A. Kalogirou, M. Bojic, Artificial neural networks for the prediction of the energy consumption of a passive solar building, Energy 25 (2000) 479e491. [32] I.H. Yang, M.S. Yeo, K.W. Kim, Application of artificial neural network to predict the optimal time for heating system in building, Energy Conversion and Management 44 (2003) 2791e2809. [33] IBPT, International Building Physics Toolbox in Simulink, http://www.ibpt. org/, 2007-11-25. [34] U.S. Census Bureau, American Housing Survey for the United States 2005, http://www.census.gov/hhes/www/housing/ahs/ahs.html; 2008-05-10e3:00. [35] ASHRAE, Thermal Environmental Conditions for Human Occupancy, (ANSI/ ASHRAE Standard 55e1992), Atlanta, GA. American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc, 1992. [36] Ashrae, Ashrae Standard 90.2e2004, Energy-Efficient Design of Low-Rise Residential Buildings. American Society of Heating, Refrigerating and AirConditioning Engineers, Inc., 2004. [37] H. McArthur, D. Spalding, Engineering Materials Science: Properties, Uses, Degradation and Remediation. Horwood Publishing, 2004. [38] Alpine, Sizing Estimator for Heating & Cooling Equipment, http://www. alpinehomeair.com/view.cfm?objID¼4A176770-C3EB-4E97-AAEDC34B81D7AFED; 2009-11-25-11:19.
Contents lists available at ScienceDirect
Applied Thermal Engineering journal homepage: www.elsevier.com/locate/apthermeng
Comparative study of artificial intelligence-based building thermal control methods e Application of fuzzy, adaptive neuro-fuzzy inference system, and artificial neural network Jin Woo Moon a, Sung Kwon Jung b, Youngchul Kim b, Seung-Hoon Han a, * a b
School of Architecture, Chonnam National University, 300 Yongbongdong, Bukgu, Gwangju 500-757, South Korea 2000 Bonisteel Blvd, TCAUP, University of Michigan, Ann Arbor, MI 48105, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 21 October 2010 Accepted 7 April 2011 Available online 20 April 2011
This study’s aim is to develop diverse Artificial Intelligence-based (AI-based) thermal control logics and to compare their performances for identifying potentials as an advanced thermal control method in buildings. Towards that aim, three AI-based control logics have been developed: i) Fuzzy-based control; ii) ANFIS-based (Adaptive Neuro-Fuzzy Inference System-based) control; and iii) ANN-based (Artificial Neural Network-based) control. The last-mentioned two were adaptive methods employing iterative self-tuning process during system operation. Each method’s performance was tested in a typical twostory residential building in USA, via computer simulation incorporating IBPT (International Building Physics Toolbox) and MATLAB. In analysis of test results for indoor air temperature, thermal comfort profiles, and amount of heat supply and removal, two adaptive control methods e ANFIS-based and ANN-based e significantly stabilized thermal conditions by the increased comfort period and the decreased deviations from the set-point compared to the Fuzzy-based non-adaptive method. No control method showed significant energy saving effects over the other. In conclusion, adaptive AI-based control methods have potential to maintain interior air temperature more comfortably. Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: Artificial intelligence Fuzzy control Neuro-fuzzy systems Artificial neural network Building thermal control
1. Introduction Artificial Intelligence (AI) has been applied to advanced building environmental controls such as thermal-, lighting-, and air-quality controls. With the substantial advantage of applicability in nonlinear systems or systems with unclear dynamics, AI-based control methods incorporating Fuzzy Logic (FL), Adaptive NeuroFuzzy Inference System (ANFIS), and Artificial Neural Network (ANN) successfully have created more comfortable environments in buildings [1,2]. In thermal quality, advanced logics using AI models are being investigated for optimal control of thermal conditions with improved energy efficiency. 1.1. Fuzzy logic (FL) Fuzzy Logic (FL), developed by Zadeh, deals with the degree of truth or falsity of phenomena. FL comprises three basic processes: 1) Fuzzification (input stage) for obtaining input values and translating them to Fuzzy type via transfer function/s such as triangular, * Corresponding author. Tel.: þ82 62 530 0639; fax: þ82 62 530 1915. E-mail address: [email protected] (S.-H. Han). 1359-4311/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.applthermaleng.2011.04.006
trapezoidal, Gaussian distribution curve, sigmoidal functions, etcetera; 2) Inferencing (processing stage) for generating inferred results based on previously fuzzified values and linguistic IF/THEN statements; and 3) Defuzzification (output stage) for totaling the result by linguistic rules, then converting it to specific output values. Benefiting from the non-requirement of precise and noise-free inputs resulting in the implementation of cheaper sensors, low resolution A/D converters and cheap microcontroller chips, Fuzzy-based control methods are being applied to building thermal controls [3,4]. The FL method, employing two inputs (E: difference between desired temperature and current temperature and dE: difference between current error and previous error) for creating one output (heating/cooling power), was comparatively tested with on/off control, PI, and PID controller for HVAC in a hospital building, using computer simulation. The FL controller showed less overshoot, oscillation, and power consumption than did conventional on/off, PI and PID methods. In addition, Fuzzy controllers provided better performance when system parameters were changed [5]. In a similar study comparatively testing an FL model with a classical closed-loop control method in an office building, the FL-based method maintained interior temperature better following the settemperature both in occupied- or non-occupied periods [6].
J.W. Moon et al. / Applied Thermal Engineering 31 (2011) 2422e2429
Nomenclature T TNEW TH TC E ENEW EOLD dE dENEW dEOLD U UNEW UOLD UTRN ni nh no nd
air temperature ( C) air temperature of current cycle ( C) set-point temperature for heating system ( C) set-point temperature for cooling system ( C) difference between air temperature from set-point temperature ( C) E of current cycle ( C) E of previous cycle ( C) change of E from previous cycle ( C) dE of current cycle ( C) dE of previous cycle ( C) output (ratio, unitless) U of current cycle (ratio, unitless) U of previous cycle (ratio, unitless) U for new training-data set (ratio, unitless) number of input neurons number of hidden neurons number of output neurons number of data sets
In the other study, employing internal PMV (Predicted Mean Vote), which describes the overall thermal comfort considering dry-bulb temperature, relative humidity, air velocity, mean radiant temperature, individual activity and clothing level, and external temperature as inputs for calculating operating signals for air heating, air cooling, and window opening angle, as outputs, the FL control-method targeted indoor PMV was investigated. In the computer simulation conducted for winter and summer seasons, PMV control with a Fuzzy model comfortably conditioned not only PMV, but also air temperature and humidity [7,8]. A similar Fuzzybased method was compared with a PID method. For the PMV control, the Fuzzy controller more quickly reached desired thermal conditions, was more energy efficient e saving some 20% of energy, and was more flexible in change of system parameters, than was the tuned PID controller [9]. In addition, a Fuzzy PID system using E (difference between desired PMV and current PMV) and dE (difference between current error and previous error) as inputs for generating a fan-rotating speed as output, proved its potentials using computer simulation and experiment [10]. Recently, Fuzzy logic began to be applied to controlling wholebuilding environment including thermal-, air-, and lighting- quality. An FL-based IEEMS (Indoor Environment Energy Management System) used PMV, outdoor temperature, indoor CO2 concentration, change ratio of CO2 concentration, and illuminance as inputs for generating heating/cooling operation, window opening, and shading-device operation as outputs. In the experiment and computer simulation for the existing building, CO2, PMV and illuminance levels were conditioned successfully and more than 30% energy was saved, compared to conventional on/off controls [11,12]. In another study, a Fuzzy Logic Controller (FLC) was implemented in the variable refrigerant volume (VRV) e variable air volume (VAV) A/C system for optimal thermal and air quality control, in which the FLC generated compressor speed (rpm), fan speed (rpm), and damper opening (%) as outputs. This system provided improved thermal comfort, air quality, and energy efficiency, compared to the conventional constant air volume (CAV) A/C system [13].
1.2. Adaptive neuro-fuzzy inference system (ANFIS) Inherently, Fuzzy logic has difficulty in developing optimal Fuzzy rules and membership functions, so, in many cases, they have
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been organized intuitively. To overcome this difficulty, a new approach incorporating FL and Neural Network has been introduced for developing a globally-applicable control model. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is one of the Neuro-Fuzzy approaches. Neuro-adaptive learning techniques are implemented for tuning membership function parameters, to permit the Fuzzy inference system to track given input/output data. The tuning process of membership function parameters are similar to those conducted in the ANN, which employs training-data sets comprising input/output data and training algorithm such as backpropagation. Based on this tuning process, ANFIS obtains the best parameters to produce output responding optimally to the given environment. In addition, by the iterative retuning process for adaptation, a global solution is obtainable [1,2,14]. A comparative study using ANN and ANFIS models was conducted for performance analysis of the evaporative condenser. In this study, ANN-based and ANFIS-based models were developed having the same inputs ([i] dry-bulb and [ii] wet-bulb temperatures of the air stream entering the condenser, mass flow rates of [iii] air, [iv] water and [v] refrigerant, and [vi] pressure and [vii] temperature of the refrigerant) for calculating the same output ([i] condenser heat rejection rate, [ii] refrigerant temperature leaving the condenser, [iii] dry-bulb, and [iv] wet-bulb temperatures of the air stream leaving the condenser). Statistical testing proved a slight superiority of the ANFIS model although both methods were successful in predicting condenser performance [15]. In another study, an ANFIS model was developed for controlling damper gap rates in the HVAC system based on indoor temperature and humidity conditions. Using error and change of error of temperature and humidity from required levels, the Neuro-Fuzzy model predicted ratios of two dampers e one for direct diffusion of supply air to the interior space, the other for humidification of supply air through the humidifier before diffusion. Using predicted ratios, this model proved faster, simpler and more efficient in controlling temperature and humidity conditions [16]. A similar study was conducted for implementing ANFIS for calculating damper ratio and fan speed in the HVAC system [17]. 1.3. Artificial neural network (ANN) Artificial Neural Network (ANN), analogous to human neural structure and its learning process, increasingly has been applied to advanced thermal control of buildings. ANN utilizes connectivity and transfer functions between input-, hidden-, and outputneurons for calculating optimal output. Different from mathematical models such as regression models or proportional-integralderivative (PID) controllers, ANN models have adaptability via a self-tuning process, so can decide accurately without external expert intervention for retuning model parameters [18,19]. Studies proved that, as a thermal control method, the ANNbased predictive control strategy has advantages over mathematical strategies in terms of accurate thermal control with reduced overheating and overcooling, and improved energy efficiency [20e28]. ANN-based algorithms were developed for determining optimal start- and stop-times of heating systems using predicted values from ANN in their algorithms. In one study, ANN models predicted optimal turn-on time of the heating system for restoring interior air temperature to a comfortable level and, in another, the length of time to drop the interior air temperature to the lower limit of comfort range. Using these predicted values, suggested control methods demonstrated advanced thermal comfort and energy efficiency [20,21]. A similar ANN model was implemented for predicting the end-of-setback moment of A/C systems [22]. ANN also successfully has been applied to hydronic heating systems of solar buildings with significant energy savings, in which
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Fig. 3. Membership function plots of U (output).
Fig. 1. Membership function plots of E (1st Input).
ANN models predicted outdoor temperature, solar radiation, indoor temperature, and supply temperature [23,24]. In particular, ANNbased predictive control methods effectively controlled residential water-heating systems and radiant floor-heating systems with significant time-lag [25e27]. More recently, a radiant heating system in a residential building successfully was controlled by ANN and incorporated Fuzzy logics. The ANN model predicted indoor air temperature, and this predicted value was utilized as input in the Fuzzy Logic. Compared to the PI control, this incorporated controller of ANN and FL reduced overshoots of air temperature and energy consumption [28]. 2. Problem statement Artificial Intelligence (AI) has been employed for optimal control of thermal conditions in buildings. Studies proved thermal comfort and energy efficiency by AI-based methods. However, each study tends to focus on the application of one specific theory such as Fuzzy, ANFIS, ANN and its comparison to the conventional control method. On the other hand, the performance of AI-based methods has not yet been compared fully. Therefore, a study comparing the performance of each AI-based method needs to be conducted for identifying their substantial potentials as a thermal control strategy. 3. Objective This study aimed to develop diverse AI-based thermal control logics and to compare their performances for identifying potentials as an advanced thermal control method in buildings. Towards that
aim, three AI-based control logics have been developed: i) Fuzzybased control; ii) ANFIS-based control; and iii) ANN-based control. Control logics produce variable output for the control system. Test results for indoor air temperature, thermal comfort profiles, and amount of heat supply and removal of each control method, using computer simulation, identified pros and cons of each control logic and are expected to provide solid foundation to suggest optimal thermal control methods in buildings. 4. Development of control logics 4.1. Fuzzy-based control Fuzzy logic was employed for control of interior air temperature, in which a Fuzzy model produced various amounts of heat supply and removal. A Fuzzy Logic toolbox in MATLAB was used for developing logic algorithm. A Fuzzy model in this study was developed with two input variables (E and dE) and one output variable (U). Membership functions of variables, given in Figs. 13, were organized based on the heuristic tuning process. Trapezoidal and triangular shapes are used for membership functions. Ranges of each variable are 2.0 to 2.0 ( C) for E, 2.0 to 2.0 ( C) for dE, and 2.0 to 2.0 for U. Output U, in system operation, indicates system operating ratio. When the calculated output from the Fuzzy model was under 1.0 or over 1.0, it was converged to 1.0 and 1.0, respectively. Accordingly, the range of output was between 1.0 and 1.0. Table 1 summarizes the Fuzzy rules using an IF-THEN structure which is the combination of two inputs and one output. 4.2. ANFIS-based control A Sugeno-type ANFIS (Adaptive Neuro-Fuzzy Inference System) model was developed using the Fuzzy Logic toolbox in MATLAB. Similarly to the previous Fuzzy-based logic, this ANFIS-based logic generates variable output for the control system. Input variable Table 1 Fuzzy If-Then Rules. Inputs (IF)
Outputs (THEN)
ENEW COLD COLD COMFORT COMFORT HOT HOT Fig. 2. Membership function plots of dE (2nd input).
and and and and and and
dENEW
UNEW
COLDER HOTTER COLDER HOTTER COLDER HOTTER
HEATING HEATING HEATING COOLING COOLING COOLING
J.W. Moon et al. / Applied Thermal Engineering 31 (2011) 2422e2429
c. Influenced by heating system operation, current temperature becomes TNEW ¼ 21.4 ( C) at the current cycle, i.e., slightly colder than TH (Set-temperature for heating system, 21.5 C). d. Using UOLD, TNEW and TH, UTRN could be calculated.
Table 2 ANFIS If-Then rules. Inputs (IF)
Outputs (THEN)
ENEW COLD COLD COMFORT COMFORT HOT HOT
and and and and and and
dENEW
UNEW
COLDER HOTTER COLDER HOTTER COLDER HOTTER
Output Output Output Output Output Output
membership membership membership membership membership membership
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function function function function function function
1 2 3 4 5 6
types were identical with the Fuzzy-based logic (Figs. 1 and 2) while different output membership functions and rules were employed as given in Table 2. Through the iterative training process during the system operation, the model continuously adjusted parameters of output membership functions, to produce optimal output (UNEW) in the given environment, thus the logic better could control the system and create more comfortable thermal conditions. The algorithm is graphically illustrated in Fig. 4. In the algorithm, the ANFIS model was trained with training-data sets which added new input sets: EOLD (ENEW of the previous cycle) and dEOLD (dENEW of the previous cycle), and output UTRN, replaced the oldest set. The example for obtaining the new training-data set is given below. a. Let EOLD ¼ 1.0 ( C) and dEOLD ¼ 0.1 ( C), which means the temperature was cold (20.5 C) and dropping at the previous cycle. b. For this condition, assume there had been UOLD ¼ 0.5 calculated by ANFIS model at the previous cycle, which means a heating system was working at half capacity.
UTRN ¼ UOLD þ UOLD (TH TNEW) ¼ 0.5 e 0.5 (21.5 e 21.4) ¼ 0.55 This 0.55 is the more desirable value for previous EOLD ¼ 1.0 ( C) and dEOLD ¼ 0.1 ( C) conditions. In this case, more heating (0.55 instead 0.5) should have been supplied between the previous and current cycles. e. Add EOLD ¼ 1.0 ( C) and dEOLD ¼ 0.1 ( C) as inputs, and UTRN ¼ 0.55 as output to the training-data sets. This is the sliding-window method, so the new training-data set was added to training-data sets, replacing the oldest. f. Train ANFIS model with new training-data sets. g. Calculate UNEW from the newly trained ANFIS model and use it for the heating system. h. Repeat this process each minute. Most parameters for the training model followed the recommended default values in the Fuzzy Logic toolbox, MATLAB: 30 epochs, more than default 10; 0.0 error tolerance; 0.01 initial stepsize; 0.9 step-size decrease rate; 1.1 step-size increase rate; back-
Fig. 4. Algorithm for ANFIS- or ANN-based controls.
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ANN model, instead of the ANFIS model, was used in the training step and the output calculating step. Thus, the sequence for obtaining training-data sets, training, and calculating were similar to the example in 4.2. nh ¼ 2 ni þ 1
(1)
nd ¼ (nh e (ni þ no)/2)2
(2)
5. Performance test
Fig. 5. ANN model for calculating the ratio of system operation (U).
propagation training method [14]; and 40 training-data sets, similar to those used in the ANN-based control logic describing in 4.3. 4.3. ANN-based control Using MATLAB and its Neural Network (NN) toolbox, an artificial neural network (ANN) model was developed to calculate a variable ratio of system operation. The model structure is shown in Fig. 5. Empirical solutions proven in previous studies were employed for model development. The model comprised an input layer, a hidden layer, and an output layer. The input layer consisted of two neurons: E and dE. The hidden layer employed five neurons based on Equation (1) [29,30]. Output of the output layer is U. Forty training-data sets, intuitively acquired in advance and updated with actual data using the sliding-window method during operation, were prepared for each model, based on Equation (2) suggesting a minimum number of sets [31]. A training goal (mean square error (MSE)) was set to 0.0 with a maximum 30 epoch, 0.75 learning rate, and 0.9 momentum based on research conducted by Yang et al. [32]. The LevenbergeMarquardt algorithm was used as a training algorithm considering training speed and accuracy [14]. Identical control algorithm with ANFIS-based variable control was applied for the temperature control given in Fig. 4. Only the
Developed control logics performance was tested through computer simulation. Two major means were incorporated for the simulation, namely International Building Physics Toolbox (IBPT) and MATLAB. IBPT was used for (1) modeling building components and related features (e.g., envelopes, ventilation rate, internal load, control systems, initial thermal conditions, and import of weather data), and (2) calculating interior air temperature conditions. Using calculated air temperature values, MATLAB was utilized for (1) developing control algorithms, (2) calculating U using Fuzzy and neural network toolboxes, (3) deciding operation of control devices based on calculated U value, and (4) training ANFIS and ANN models. The system operation decision was fed into the IBPT for system operation, and the interior air temperature condition (newly-created by such system working) was used in MATLAB iteratively [14,19,33]. Simulation tool validity was proved with acceptable differences in the amount of energy consumption and temperature profile, compared to experimental results in the previous study [18]. Based on the American Housing Survey [34], a USA-typical, twolevel, single-family home equipped with heating and cooling systems in cold-climate Detroit, Michigan, USA (Fig. 6) was used as a test model. Details of a test building are given in Table 3. Control systems were composed of convective heating (e.g., furnace) and cooling systems (e.g., DX coils) which had set-points 21.5 C for heating system and 24.5 C for cooling system based on the ASHRAE recommended temperature comfort range [35]. Each system was designed to work following the operating ratio that was calculated from the control logics in the MATLAB (previous Figs. 1e4). Following the operating ratio (from 0 to 1) fed into the IBPT, systems can change their amount of heat supply and removal in this study (from 0 to 100%), which could be generally applied in the real building systems by control of a damper gap, fan-rotating speed, inlet air temperature, etc. For example, if the calculated output from the logic in the MATLAB is 0.25 for heating in the
Fig. 6. A Typical U.S. Single-Family Home used in Simulation [19,20,34].
J.W. Moon et al. / Applied Thermal Engineering 31 (2011) 2422e2429
6. Results and analysis
Table 3 Descriptions of a test building. 184.8 m2 (92.4 m2 for each floor)
Area Envelope
Insulation SI ( K m2/W) (U.S. ( F ft2 hr/Btu))
Window Wall Ratio
Internal gain
Infiltration rate Systems applied [38]
Weather data Assumptions
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Walls R3.3 (R19.0 Roof R6.7 (R38.0 Floor R3.7 (R21.0 Windows R0.6 (R3.44 Doors R0.2 (R1.22 0.15 on average (0.24 for south, 0.08 for north, 0.14 for east, 0.13 for west) Hourly-weighted heat and moisture gains for a family of four people [36, 37] 0.35 ACH Convective heating: 21,394Watt heat supply Convective cooling: 10,198Watt heat removal TMY2 for Detroit, Michigan, USA Initial air temperature: 23 C Initial humidity: 45%
U.S.) U.S.) U.S.) U.S.) U.S.)
winter, the heating system in the IBPT supplies 5348.5Watt (21,394Watt X 0.25) of heat as a power to the space. As a result of system operation in the IBPT, the temperature conditions in the space can be maintained comfortably near the set-points. Logics were tested for six days for winter (January 27 to February 1, representing heating-peak days) and summer (July 3 to July 8, representing cooling-peak days). For data analysis, the first day was trimmed and the last five days were analyzed.
Each logic’s performance was analyzed in terms of profiles of indoor air temperature, thermal comfort, and amount of heat supply and removal. 6.1. Indoor air temperature conditions Indoor air temperature conditions by developed control logics are shown comparatively for a sampled period in winter (Fig. 7) and summer (Fig. 8). From the top, they are by Fuzzy-based control; Fuzzy-based control after tuning; ANFIS-based control; and ANNbased control. Fuzzy-based control conditioned temperature properly reaching to set-points in both seasons (21.5 C for winter and 24.5 C for summer), although fluctuations occurred around set-points (1st chart of each Figure). The proper conditioning using the Fuzzy model was due to the heuristic tuning process, by which outputs were properly calculated to heat up and cool down space. However, this tuned model does not always guarantee successful applicability to the space with different background conditions. ANFIS- and ANN-based control logics more successfully conditioned temperature following the set-point, which proves the adaptability of these methods (2nd and 3rd charts of each Figure). However, the reasons that air temperature conditioned below setpoint in the early period (0:00e7:00) in both seasons were 1) coldest weather exceeded heating-system capacity in winter and 2) cooler weather required no further cooling in summer. The average conditioned temperatures by each control method for the whole simulation period were 20.32 C, 21.41 C, 21.43 C, and 21.43 C for winter, and 25.28 C, 24.57 C, 24.40 C, and 24.34 C for summer, respectively.
Fig. 7. Profile of interior air temperature (January 28, winter).
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Fig. 8. Profile of interior air temperature (July 5, summer).
6.2. Thermal comfort profiles
6.3. Amount of heat supply and removal
Thermal comfort created by each logic was compared in terms of percentage of comfort period and deviation from set-point. Percentages of periods when indoor air temperature is within specified ranges (2023 C winter and 2326 C summer) are in Table 4. In winter, Fuzzy-, ANFIS-, and ANN-based methods controlled temperature conditions in a similar way (98.3%, 98.4% and 98.1% comfort period, respectively in winter). In summer, all three control methods satisfied temperature conditions well within specified comfort range. In the analysis on the standard deviations of air temperature from the set-point (21.5 C winter, 24.5 C summer) for three AI-based methods to identify how each logic maintains air temperature close to set-point (Table 5), two adaptive methods (ANFIS-based and ANN-based control methods) clearly reduced the amount of standard deviation (all values under 0.2 C for both seasons) compared to the Fuzzy-based control method (0.22 C winter and 0.24 C summer). From the analysis of temperature profiles, percentage of comfort period, and deviation from set-points, all the AI-based methods controlled temperature conditions properly, in particular, two adaptive models using ANFIS and ANN maintained temperature conditions more successfully near the set-points.
As a way of measuring energy consumption by climate control equipments controlled by different control logics, amounts of heat supply by a heater and heat removal by an air-conditioner were calculated and compared (Table 6). For the three control methods, the amount of heat supply and removal did not show significant difference. Fuzzy-based control method, however, presented the least amount of heat supply in winter (689.5 KWh) and removal in summer (332.4 KWh). It was due to the lower temperature conditioned (21.41 C) in winter and higher (24.57 C) in summer by the Fuzzy-based method compared to those of ANFIS- and ANNbased controls, which were 21.43 C for both methods in winter and 24.40 C and 24.34 C in summer, respectively.
Table 4 Percentage of periods (%) within specified comfort ranges (2023 C winter and 2326 C summer).
Table 5 Standard deviation ( C) from set-points by control methods. Control Methods
Winter
Summer
Fuzzy-based control ANFIS-based control ANN-based control
0.22 0.13 0.13
0.24 0.17 0.19
Table 6 Amount of heat supply in winter and removal in summer (KWh).
Control Methods
Winter
Summer
Control methods
Winter
Summer
Fuzzy-based control ANFIS-based control ANN-based control
98.3 98.4 98.1
100.0 100.0 100.0
Fuzzy-based control ANFIS-based control ANN-based control
689.5 690.3 692.5
332.4 340.7 343.3
J.W. Moon et al. / Applied Thermal Engineering 31 (2011) 2422e2429
7. Conclusions In this study, three AI-based thermal control methods were developed and their performances were compared for identifying potentials as an advanced thermal control method in buildings. A computer-simulation method incorporating MATLAB and IBPT was employed for testing performance of logics. Test results were analyzed in terms of indoor air temperature conditions, thermal comfort profiles, and amount of heat supply and removal. Findings from this study are: Fuzzy-based control conditioned temperature properly following set-points due to the tuning process. However, that does not mean model adaptability. In other words, this tuned model may not work properly in a building with new parameters such as change of control system capacity or change of building configurations. Adaptive ANFIS- and ANN-based control logics more successfully conditioned temperature following the set-point and their standard deviations from set-points thus were reduced significantly. These improvements proved the necessity of the iterative training process during the logic and system operation. Tuned model or adaptive models did not present a significant energy saving effect. In conclusion, adaptive AI-based control methods using ANFIS and ANN models have presented substantial potential for enhancing thermal comfort with improved stability. This study’s findings could provide solid foundation for identifying the optimal building thermal control method in future research.
Acknowledgements This work was supported by the Grant of the Korean Ministry of Education, Science and Technology (The Regional Core Research Program/Biohousing Research Institute) and by the Biohousing Research Center.
References [1] A.I. Dounis, C. Caraiscos, Advanced control systems engineering for energy and comfort management in a building environmenteA review, Renewable and Sustainable Energy Reviews 13 (2009) 1246e1261. [2] M. Krarti, An overview of artificial intelligence-based methods for building energy systems, Journal of Solar Energy Engineering 25 (2003) 331e342. [3] S.D. Kaehler, Fuzzy Logic e An Introduction, Encoder e The Newsletter of the Seattle Robotics Society (2006-09-19).http://www.seattlerobotics.org/ encoder/mar98/fuz/fl_part1.html. [4] G. Goebel, An Introduction to Fuzzy Control Systems (2006-09-19).http:// www.faqs.org/docs/fuzzy/. [5] E. Kaymaz, Adaptive environmental control for optimal energy consumption in hospitals, Computer Based Medical Systems, Proceedings of the Eighth IEEE Symposium (1995) 165e172. [6] G. Fraisse, J. Virgone, J.J. Roux, Thermal control of a discontinuously occupied building using a classical and a fuzzy logic approach, Energy and Buildings 26 (1997) 303e316. [7] A.I. Dounis, E.D. Manolakis, Design of a fuzzy system for living space thermal comfort regulation, Applied Energy 69 (2001) 119e144. [8] A.I. Dounis, M.J. Santamouris, C.C. Lefas, A. Argiriou, Design of a fuzzy set environment comfort system, Energy and Buildings 22 (2005) 81e87. [9] M.M. Gouda, S. Danaher, C.P Underwood, Thermal comfort based fuzzy logic controller, Building Services Engineering Research and Technology 22 (2001) 237e253.
2429
[10] F. Calvino, M.L. Gennusa, G. Rizzo, G. Scaccianoce, The control of indoor thermal comfort conditions: introducing a fuzzy adaptive controller,, Energy and Buildings 36 (2004) 97e102. [11] D. Kolokotsa, K. Niachou, V. Geros, K. Kalaitzakis, G.S. Stavrakais, M. Santamouris, Implementation of an integrated indoor environment and energy management system, Energy and Buildings 37 (2005) 93e99. [12] D. Kolokotsa, Comparison of the performance of fuzzy controllers for the management of the indoor environment, Building and Environment 38 (2003) 1439e1450. [13] R. Karunakaran, S. Iniyan, R. Goic, Energy efficient fuzzy based combined variable refrigenrat volume and variable air volume air conditioning system for buildings, Applied Energy 87 (2010) 1158e1175. [14] MathWorks, MATLAB 14, http://www.mathworks.com, 2005-12-10e17. [15] H.M. Ertunc, M. Hosoz, Comparative analysis of an evaporative condenser using artificial neural network and adaptive neuro-fuzzy inference system, International Journal of Refrigeration 31 (2008) 1426e1436. [16] S. Soyguder, H. Alli, An expert system for the humidity and temperature control in HVAC systems using ANFIS and optimization with Fuzzy Modeling Approach, Energy and Buildings 41 (2009) 814e822. [17] S. Soyguder, H. Alli, Predicting of fan speed for energy saving HVAC system based on adaptive network based fuzzy inference system, Expert Systems with Applications 36 (2009) 8631e8638. [18] J.W. Moon, J.J. Kim, ANN-based thermal control models for residential buildings, Building and Environment 45 (2010) 1612e1625. [19] J.W. Moon, S.K. Jung, J.J. Kim, Application of ANN (Artificial-Neural-Network) in Residential Thermal Control, 11th International Building Performance Simulation Association Conference, Building Simulation 2009. University of Strathclyde, Glasgow, July 27-30, 2009, 64e71. [20] M.S. Yeo, K.W. Kim, Application of artificial neural network to predict the optimal start time for heating system in building, Energy Conversion and Management 44 (2003) 2791e2809. [21] I.H. Yang, K.W. Kim, Development of artificial neural network model for the prediction of descending time of room air temperature, International Journal of Air-Conditioning and Refrigeration 12 (2000) 1038e1048. [22] A.E. Ben-Nakhi, M.A. Mahmoud, Energy conservation in buildings through efficient A/C control using neural networks, Applied Energy 73 (2002) 5e23. [23] A.A. Argiriou, I. Bellas-Velidis, M. Kummert, P. Andre, A neural network controller for hydronic heating, systems of solar buildings,, Neural Networks 17 (2004) 427e440. [24] A.A. Argiriou, I. Bellas-Velidis, C.A. Balaras, Development of a neural network heating controller for solar buildings, Neural Networks 13 (2000) 811e820. [25] N. Morel, M. Bauer, M. El-Khoury, J. Krauss, Neurobat, A predictive and adaptive heating control system using artificial neural networks, International Journal of Solar Energy 21 (2001) 161e201. [26] J.Y. Lee, M.S. Yeo, K.W. Kim, Predictive control of the radiant floor heating system in apartment buildings, Journal of Asian Architecture and Building Engineering 1 (2002) 105e112. [27] J.Y. Lee, I.H. Yang, S.Y. Song, H.S. Kim, K.W. Kim, A Study of the Predictive Control of the Ondol System in Apartments. International Building Performance Simulation Association, Kyoto, September 13e15, 1999, 215e222. [28] G.G. Gouda, S. Danaher, C.P. Underwood, Quasi-adaptive fuzzy heating control of solar buildings, Building and Environment 41 (2006) 1881e1891. [29] J. Yang, H. Rivard, R. Zmeureanu, On-line building energy prediction using adaptive artificial neural networks, Energy and Buildings 37 (2005) 1250e1259. [30] D. Datta, S.A. Tassou, D. Marriott, Application of Neural Networks for the Prediction of the Energy Consumption in a Supermarket. Clima 2000, Brussels, Belgium, 1997, 98e107. [31] S.A. Kalogirou, M. Bojic, Artificial neural networks for the prediction of the energy consumption of a passive solar building, Energy 25 (2000) 479e491. [32] I.H. Yang, M.S. Yeo, K.W. Kim, Application of artificial neural network to predict the optimal time for heating system in building, Energy Conversion and Management 44 (2003) 2791e2809. [33] IBPT, International Building Physics Toolbox in Simulink, http://www.ibpt. org/, 2007-11-25. [34] U.S. Census Bureau, American Housing Survey for the United States 2005, http://www.census.gov/hhes/www/housing/ahs/ahs.html; 2008-05-10e3:00. [35] ASHRAE, Thermal Environmental Conditions for Human Occupancy, (ANSI/ ASHRAE Standard 55e1992), Atlanta, GA. American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc, 1992. [36] Ashrae, Ashrae Standard 90.2e2004, Energy-Efficient Design of Low-Rise Residential Buildings. American Society of Heating, Refrigerating and AirConditioning Engineers, Inc., 2004. [37] H. McArthur, D. Spalding, Engineering Materials Science: Properties, Uses, Degradation and Remediation. Horwood Publishing, 2004. [38] Alpine, Sizing Estimator for Heating & Cooling Equipment, http://www. alpinehomeair.com/view.cfm?objID¼4A176770-C3EB-4E97-AAEDC34B81D7AFED; 2009-11-25-11:19.