Simulation Study of Thermal Sensation Based Control for Single Family Type Distributed Heating Systems Peizhang Chen 1, Fulin Wang 1,*, and Zheliang C

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1 Smulaton Study of hermal Sensaton Based Control for Sngle Famly ype Dstrbuted Heatng Systems Pezhang Chen 1, Fuln Wang 1,*, and Zhelang Chen 1 1 Department of Buldng Scence, snghua Unversty, Bejng, , Chna ABSRAC Sngle Famly type Dstrbuted Heatng (SFDH) has a tendency of ncrease n resdental buldngs because of ts low ntal and mantenance cost, sutablty to buldng occupancy and house sale stuatons, and less energy consumpton compared wth dstrct heatng. Further, the SFDH system can acheve better thermal envronment control by consderng the room occupants thermal sensaton to acheve personalzed comfort. However, the present room temperature control s based on set ponts gven by room occupants, whch mght lead to poor thermal comfort and energy waste as well. hs artcle proposes a new thermal sensaton based control for SFDH system, whch uses an on-lne learnng algorthm to fnd out an occupant s comfortable temperature range and to decde optmal temperature set pont. he SVM (support vector machne) model s used for the on-lne learnng of the comfortable temperature range. Smulaton s conducted to study the performance of the SVM model predcted control. he smulaton results show that the temperature set pont predcted by the model can acheve user satsfed thermal envronment quckly and relably. KEYWORDS Dstrbuted heatng system, Sensaton based control, hermal comfort modellng, Model predcted control INRODUCION Sngle Famly type Dstrbuted Heatng (SFDH) has a tendency of ncrease n resdental buldngs because t has lower ntal and mantenance cost, t s easer for occupants to start and stop heatng for savng heatng energy accordng to occupancy stuaton, and t s helpful for real estate developer to sale houses wthout consderng the lmtaton to sale of usng dstrct heatng system. A SFDH system should nclude thermostats, valves, pump and boler. In order to acheve better thermal comfort and hgher energy effcency, new automatc control system for SFDH system s needed. Researches on automatc control of heatng manly focus on dstrct heatng, such as control of the supply water temperature of secondary sde (Yu et al 001), and controllng the water valves wth methods of duty rato (Lu 010), heat meterng usng valve duty rato (Lu et al 008). Xu (010) used Back Propagaton Neural Network (BPNN) to predct the tme that the room temperature takes to match the set pont and use the tme to help the feedback control of valves. he * Correspondng author emal: flwang@tsnghua.edu.cn 380

2 method of BPNN s qute complcated. Easy and accurate control methods are needed. herefore some researchers studed on-lne learnng algorthm to acheve better control, such as NES temperature controller (Yang and Newman 01), PMV predcton wth BPNN (Peng 006). However, these temperature control methods are based on temperature set ponts gven by room occupants or buldng managers. Wang et al (014) revealed by nvestgaton that about 1/3 temperature settngs are mproper and lead to poor thermal comfort and energy waste, so they proposed a percepton-based thermal envronmental control method. Zhao et al (014) utlzed one-class classfer and Personal Dynamc hermal Comfort (PDC) model (Zhao et al 014) to fnd out comfort regon accordng to occupants thermal perceptons. But these models use large quantty of data for on-lne learnng, whch s nconvenent n practcal applcaton. hs paper descrbes a new method of thermal sensaton based control for SFDH system. When room occupants feel hot or cold, they can nput hot or cold sensatons through a human machne nterface (HMI). he control system uses an on-lne learnng algorthm to buld occupants thermal comfort model accordng to the room occupants thermal sensatons and use the model to fnd out comfortable temperature range and decde a proper temperature set pont to make room temperature comfortable. Smulatons are conducted to study the performance of the proposed model predcted control. At present stage, the study only consder one occupant stuaton. Mult-occupant stuaton wll be studed n the future. RESEARCH MEHODS Accordng to the thermal comfort theory (Fanger 198), the Predcted Mean Vote 0.036M (PMV) s calculated by PMV 0.303e 0.075L, where M s the metabolc rate, and L s the thermal load, defned as the dfference of heat producton and heat dsspaton. When the metabolc rate keeps stable, the ambent temperature wll affect the heat dsspaton. Hgher temperature leads to less heat dsspaton, and then greater L. So PMV wll be greater because t s proportonal to L, and the occupant wll feel hot. herefore, there s a postve correlaton between the temperature and the thermal sensaton,.e., hgher temperature leads to hot feelng and lower temperature leads to cold feelng. here s a temperature range where occupants do not feel hot or cold between the hot and cold temperature regon. Snce there s a postve correlaton between the temperature and the thermal sensaton, Support Vector Machne (SVM) are sutable for on-lne learnng of comfortable temperature regon. he prncples of SVM have been summarzed by Fan (003). he C-SVM method wth relaxaton factors can be used to tolerate the wrong classfcatons. he detals of the method are shown below. It s assumed that there are sample ponts of two classes, {x, y}, where x s the ambent state,.e. temperature and humdty [t, d], and y s the class. he class y 1 represents the hot vote, and the class y 1 represents the cold vote. SVM method s to calculate the classfcaton hyper plane, whch s descrbed by w x b 0. he optmal hyper plane s obtaned through the followng equaton. 381

3 mn w, b, 1 wwc s.t. y wxb 1, 0 (1) Where s the relaxaton factor, and C s the penalty parameter wth a postve value. Smaller C means hgher tolerance to the wrong classfcatons. he s the weght of a sample pont, whch s calculated by, where 0 1, represents the forgettng factor, and means tme wth the unt of day. We use Lagrangan Multpler Method and Sequental Mnmal Optmzaton (SMO) method (Platt 1998) to solve the optmzaton problem above. Now we obtan the classfcaton hyper plane w x b 0, and the regon between hyper planes wx b 1 and wx b 1 s the comfortable area n whch any temperature can be a set pont. he hyper plane wx b 1 s the boundary of hot area and wx b 1 s the boundary of cold area. For the SFDH system only heatng s consdered, so for the purpose of savng energy, the temperature near the lower boundary s selected as the set pont. he set pont s obtaned by solvng the followng equaton: * y wx b s.t. x x * f t 0.5 () Where x * s set pont and x s measured temperature. he f(t) means that the dfference between x * and x s the one-dmenson functon of temperature t. he reason why SVM method can acheve the acceptable classfcaton of hot and cold s that hot votes and cold votes are nearly lnear to temperature. However, the relaton s not strctly lnear, and occupants votes are not constant but slghtly change stochastcally, so wth the relaxaton factors, C-SVM method can also classfy hot votes and cold votes acceptably. he forgettng factor consders occupants comfort regon change by forgettng the hstorc votes. In order to check the effectveness of SVM model predcted control, the Smulnk model s bult to smulate the control performance, as shown n Fgure 1. he Smulnk model conssts of four modules: Room, Vote, Water system, and Controller. he module Room s used to smulate the room thermal dynamcs, whose volume s 90m 3, and the outdoor ar temperature perodcally changes from 3 o C to 7 o C. he module Water system s used to smulate the thermal dynamcs of radator, where the supply water temperature s 60 o C. he module Controller uses a PID (Proportonal, Integral and Dfferental) control logc to decde the duty rato of the radator s valve. he module Vote s to generate the temperature set pont usng the former mentoned SVM model. For generatng hot/cold vote, a human comfort model s bult accordng to the changng room temperature. he generated set pont s sent to Controller module as the nput for PID control. he human comfort model used for generatng hot/cold votes s 38

4 shown n Fgure. he comfortable temperature range s assumed to be 0~4 (ASHRAE 199), and the hot and cold votes are generated under a probablty gven by the normal dstrbuton densty functon, as shown n Equaton 5, where represents the present temperature, L and U represents the lower and upper boundary temperature of the comfortable temperature ranger respectvely. he tol represents the tolerance temperature. Votes are generated randomly every 1, 30 mn. Correspondng to every room temperature, there s a probablty of generatng a hot/cold vote. In the comfortable temperature range, the probablty of generatng a hot/cold vote s small. he farther s the room temperature from the comfortable temperature range, the larger s the probablty of generatng a hot/cold vote. Fgure 1. Smulaton model dagram bult n Smulnk LU cold hot probablty Fgure. Schematc dagram of the comfort model and vote probablty dstrbuton L 1 e, U P 1 e, 0,other L U (3) 383

5 RESULS he smulaton perod s 4h wth the ntal room temperature of 15 o C. he smulaton result s shown n Fgure 3. he model parameters are that the penalty parameter C=100, and the forgettng factor From Fgure 3, t can be seen that all set ponts are correctly decded correspondng to the hot and cold votes,.e., hot votes trggered lower temperature settngs, whle cold votes trggered hgher temperature settngs. he zoomed n smulaton result from 8:00 to 1:00 are shown n Fgure 4. he temperature settngs tend to be stable accompanyng to the model learnng, and fnally get close to the mddle of the comfortable range. hs proves that the model has learned out the prevously assumed comfortable temperature range. he performance of the SVM predcted control are quanttvely evaluated usng three ndexes, comfort range enterng tme t steady, less vote tme t less, and satsfactory tme rato. he comfort range enterng tme t steady s the hours that the room temperature takes to enter the comfortable temperature range. he less vote tme t less s the hours that t takes untl the frequency of vote descends to 10% of that at the begnnng. he satsfactory tme rato, as shown n Equaton 6, reflects the rato of the occupant s comfortable tme to one control perod, whch s 4 hours here. he evaluaton results are shown n able 1. he SVM predcted control takes 0.87 hour to make room temperature enter comfortable range [0, 4]. It takes.936 hours to make the hot/cold vote frequency decrease to 1/10 of that at the begnnng. For the 4 hours control perod, the satsfactory rato s hese parameters show that the proposed SVM predcted control can fast and relably control room temperature to be n the comfortable range. Further the comfortable temperature range s learnt out accordng to ndvdual hot/cold vote, so the proposed method can be easly acheve personalzed comfort and prevent energy waste caused by mproper temperature settngs. 4 t less (4) 4 384

6 4 a hot cold set 3 / smulaton tme/h Fgure 3. Smulaton result through SVM 3.5 a hot cold set / smulaton tme/h Fgure 4. Detals of smulaton result from 8:00 to 1:00 able 1. Evaluatons of SVM predcted control Model t steady (h) t less (h) Satsfactory rate SVM CONCLUSIONS In ths artcle, a new method of thermal sensaton based control for SFDH system s proposed. hrough an HMI, an occupant can nput the thermal sensaton of hot or cold and the control system learns out the occupant comfortable temperature range and adjusts the room temperature to be n the comfortable range. he SVM model s used for the on-lne learnng of the comfort temperature range. Smulaton s conducted to check the performance of SVM predcted control. he smulaton results show that the temperature set pont predcted by the SVM model can acheve satsfed thermal envronment quckly and relably. It takes less than 1 hour to fnd out the occupant 385

7 comfortable temperature and less than 3 hours to let an occupant acheve thermal comfort. REFERENCES ASHRAE ANSI/ASHRAE Standard , hermal Envronmental Condtons for Human Occupancy, Atlanta: Amercan Socety of Heatng, Refrgeratng, and Ar-Condtonng Engneers, Inc. Fan, X Research and applcaton of support vector machne (SVM) algorthm, Ph.D. hess, Zhejang Unversty (Chna). Fanger, P. O hermal comfort: analyss and applcatons n envronmental engneerng, Malabar, Fla.: Robert E.Kreger Publshng Company. Lu, L Intellgent on-off regulaton technology of heatng system termnals, HVAC and Energy Effcency Management, Vol.8, pp Lu, L., Jang, Y., and L. Fu Introducton of termnals on-off regulaton and heat meterng technology based on termnals on-off rato, Natonal HVAC 008 Academc Anthology, pp.8-1. Bejng. Peng, S Optmzaton combnaton of HVAC thermal parameters based on artfcal Neural Network, Master hess, Hunan Unversty (Chna). Platt, J. C Sequental mnmal optmzaton: a fast algorthm for tranng support vector machnes, MSR-R-98-14, Redmond: Mcrosoft Research. Wang, F., et al. 014 Prelmnary study on percepton-based ndoor thermal envronment control, Proceedngs of the 13th Internatonal Conference on Indoor Ar Qualty and Clmate, July 7-14, 014, Hong Kong. Xu, C Research on household heat meterng system temperature control algorthm, Master hess, Shenyang Insttute of Aeronautcal Engneerng (Chna). Yang, R. and Newman, M. W. 01. Lvng wth an ntellgent thermostat: advanced control for heatng and coolng systems, 01 ACM Conference on Ubqutous Computng, pp Pttsburgh. Yu, B., L, B., and Zhao, H Automatc control strategy of heatng system, Journal of Southwest Jaotong Unversty, Vol.36 (4), pp Zhao, Q., Zhao, Y., Wang, F.et al Prelmnary study of learnng ndvdual thermal complant behavor usng one-class classfer for ndoor envronment control, Buldng and Envronment, Vol.7, pp Zhao, Q., Zhao, Y., Wang, F.et al A data-drven method to descrbe the personalzed dynamc thermal comfort n ordnary offce envronment from model to applcaton, Buldng and Envronment, Vol.7, pp