Advanced HVAC Control: Theory vs. Realty Karel Mařík* Jří Rojíček* Petr Stluka* Jří Vass* *Honeywell Prague Laboratory, Prague, Czech Republc e-mals: karel.mark, jr.rojcek, petr.stluka, jr.vass{@honeywell.com} Abstract: Intellgent control of HVAC equpment s a key step towards mprovng the energy effcency of commercal buldngs. Although advanced control technques have been developed and valdated under real condtons, numerous buldngs are stll beng poorly controlled due to wrong setponts, ncorrect PID settngs, no coordnaton of ndvdual PID loops, and other practcal problems. Ths paper ams to summarze the major contrbutors to neffcent HVAC control and outlne possble approaches towards better control strateges. Three areas are dscussed: performance montorng tools, rule-based control strateges and model-based predctve control (MPC). Performance montorng tools help control engneers to quantfy the performance of a partcular control strategy, compare multple control strateges among themselves, and defne a baselne for such comparsons. Rule-based control strateges utlze varous setpont resets, rules and other heurstcs to reduce HVAC energy consumpton; however, such methods yeld sub-optmal solutons only. Fnally, MPC s a powerful and ndustrally-proven technology for optmal control of complex systems, but ts use n buldng control seems to be so far lmted. The paper analyzes challenges and constrants when mplementng a control strategy n real projects, and covers topcs such as mssng sensors, legacy controllers and legslatve changes needed to motvate buldng owners towards more effcent faclty management. Keywords: Intellgent controllers; Identfcaton and control methods; Modelng 1. INTRODUCTION Due to ncreasng energy prces, optmzng energy usage n buldngs becomes more and more urgent. But surprsngly, ths task may be dffcult to acheve as the buldng market s dverse and complex wth conflctng nterests of nvolved stakeholders. Even worse, not every stakeholder s supportng energy effcency n buldngs and the needs dffer for dfferent types of buldngs (commercal vs. resdental sector). Ths creates a need for optmzaton tools easy to nstall wth mnmal retroft needs. A major energy consumer n commercal buldngs s the HVAC (Heatng, Ventlaton & Ar Condtonng) system that s composed of bolers, chllers, ar-handlng unts (AHU), varable ar volume (VAV) termnal unts and auxlary devces (pumps, fans, valves, etc.). HVAC system represents a specfc form of an energy converson and dstrbuton network where energy of prmary sources (electrcty, gas, coal, etc.) s converted nto other energy streams (water, ar) and dstrbuted through a network of ppes and heat exchangers nto buldng s nteror spaces. Intellgent control of such energy-ntensve system s an attractve way to acheve sgnfcant energy savngs and reduce CO 2 emssons. Fg.1 shows key elements of HVAC control systems, whch are mplemented n commercal buldngs. Each pece of equpment s controlled by a sngle untary controller, whch generates control sgnals for assocated actuator based on sensor data. Snce the untary controllers do not communcate among themselves, ther control actons are not coordnated, whch leads to neffcent control of the system as a whole and frequently to ncreased energy consumpton. Varous control neffcences can easly occur as a consequence of the mssng coordnaton. For nstance, smultaneous heatng and coolng, neffectve cooperaton of dampers and cols, or mproper schedulng (too early or too late start or stop). In addton, untary controllers often use setponts of a constant value only (e.g. supply ar temperature setpont of 15 C), or there s a constant setpont for each mode (occuped, unoccuped, summer, wnter, etc.). Hence supervsory controllers should be used to coordnate the untary controllers by adjustng ther setponts and specfyng the modes of operaton. Fg.1: Key elements of HVAC control systems. Copyrght by the Internatonal Federaton of Automatc Control (IFAC) 3108
Specfc control strateges adopted n HVAC control systems nclude standard PID control that s embedded n untary controllers, as well as more advanced supervsory control concepts. State-of-the-art supervsory control methods are rule-based and followng prncples of statc optmzaton where the optmal setpont values are calculated ndependently at each nstant of tme. More advanced dynamc optmzaton methods, whch are subject of actve research nowadays (Oldewurtel et al., 2010; Zavala et al., 2010), are model-based and optmzng setpont trajectores over a confgurable tme horzon. Performance montorng tools are used as a complement to the control strategy. By systematc processng of data collected n the buldng management system (BMS) these tools allow to detect neffcences and faults, and facltate system mprovements. Performance montorng s an mportant step towards better HVAC control, whch s acheved by passvely observng the system and notfyng the user (techncan, applcaton engneer, faclty manager) about possble problems (hardware faults, bad control, etc.). Typcal examples are oscllatory control sgnals caused by mproper PI constants, permanent setpont offset, or a wrong porton of the outdoor ar, whch leads to unnecessary mechancal coolng. Varous dashboards for HVAC montorng are already commercally avalable; however, not much more than smple trendng s often used. Some equpment (e.g. bolers) s also equpped wth an on-board dagnostcs/montorng. More detals on montorng are gven n Secton 2. Rule-based control. Basc supervsory control strateges can be mplemented usng rules and heurstcs based on common sense and best practces. The rule-based approach s typcally used to compute better setponts and/or better schedulng (optmal start/stop, pre-coolng, etc.). For nstance, a rulebased control strategy for ar handlers can nvolve the supply ar temperature reset, nght purge, CO 2 -based DCV, and other modules summarzed n Table 1. Table 1. Examples of rule-based control strateges Rule-based strategy Intellgent schedulng Free coolng utlzaton Setpont resets Demand-controlled ventlaton (DCV) DCV usng real-tme prces Arflow/temperature balancng PMV-based defnton of zone setpont Examples Optmum start/stop Nght purge Duty cycle Temperature-based Enthalpy-based Temperature/humdty reset Compensaton usng outdoor temperature/humdty Supply duct statc pressure reset CO 2 -based Indoor Ar Qualty based Load sheddng Load shftng Supply fan speed reset Return fan speed reset See Secton 3 Although rule-based methods are relatvely smple, and thus capable of fndng a sub-optmal soluton only, qute sgnfcant energy savngs can be acheved f they are properly mplemented. Indeed, our experence wth real data shows that many buldngs are poorly controlled, thus even a smple enhancement of control strategy would reduce operatng costs. Rule-based systems can theoretcally encompass multple sub-systems (ar handlers, chllers), and thus acheve even hgher energy effcency. However, such systems are not wdely avalable because they could be too costly to setup and mantan. On the other hand, customzed solutons are beng mplemented on project-to-project bass, and there are attempts to make such solutons more generc and repeatable. Model predctve control (MPC). As ndcated n Fg.1 the supervsory control functonalty can be mplemented usng a model predctve controller capable of coordnatng untary controllers n multple HVAC sub-systems. Ths dynamc verson of supervsory control allows utlzng actve and passve thermal buldng storage and enables advanced applcatons, such as peak shavng or nght pre-coolng. Secton 4 dscusses practcal aspects of MPC control for HVAC systems n more detal. There are several common barrers and challenges for deployment of advanced solutons for HVAC systems. Legacy controllers. Control strateges for legacy controllers are often coded n programmng languages that do not allow easy modularzaton of the code, and therefore the strategy s often only as good as the knowledge of a local control engneer. As a result, there s a lmted knowhow sharng and lack of flexble lbrares. Of course, each project s unque due to specfc customer requrements and dfferent hardware n each buldng (VAV boxes vs. chlled / heated beams). Mssng sensors are often the reason why some control strateges smply cannot be employed. For example, CO 2 sensors are necessary for demand-controlled ventlaton (DCV), a well-known method to reduce the amount of ar to be condtoned (Gabel and Krafthefer, 1997; Brandemuehl and Braun, 1999; Emmerch and Persly, 2003). If the owner s not nterested n purchasng extra sensors (humdty sensors, zone temperature sensors, etc.), there s not much to be optmzed. Another typcal example are flow sensors (to measure ar or water flow rate); when they are mssng t s dffcult to setup models based on enthalpy balances. Generally, more advanced supervsory control strateges requre more nformaton and they suffer from the lack of measurement devces. Ineffcent hardware. In fact, t s not always easy to convnce buldng owners to nvest nto VFD (varablefrequency drve) fans rather than the tradtonal constantspeed drves. The same apples also to VFD pumps, modulatng dampers, heat recovery wheels, etc. For these reasons, advanced solutons are often mplemented wthn energy-retroft projects rather than new buldng constructon, where a typcal goal s to mnmze the ntal cost. A good example s offce rentng, where electrcty/gas blls are pad by the rentng party rather than the buldng 3109
owner (who s thus less motvated to purchase energyeffcent hardware and addtonal sensors). Cost-to-beneft rato. Total cost assocated wth mplementaton of an advanced montorng or control soluton s gven by several elements ncludng setup and confguraton cost, mantenance cost, cost of addtonal sensors, and cost of potental hardware retrofts. Buldng owners usually requre an attractve return of nvestment (ROI) wth the prmary benefts represented by the reduced operatng costs. Other potental benefts, such as mproved comfort or safety, cannot be so easly quantfed. Regulaton. Operaton of buldngs s frequently nfluenced by regulatons and standards n ndvdual countres and regons. An nterestng specal case s humd clmate regons, where specfc requrements are gven on enthalpy controllers. For soluton vendors, whch are actve n the HVAC ndustry, ths factor complcates possble ntegraton of localzed and fragmented solutons nto one globally supported control strategy. 2. PERFORMANCE MONITORING As shown n Fg.1, the montorng system may consst of multple modules focused on analyzng major crtera, such as energy consumpton, equpment health (performance degradaton), comfort, and qualty of control (Vasyutynskyy et al., 2005). Here we wll focus on montorng of thermal comfort only; detals on AHU fault dagnostcs can be found n other papers (Trojanova et al., 2009; Vass et al., 2010). temperature setpont both can save energy, but the latter may compromse occupants comfort. Snce PMV computaton requres sensors that are not commonly avalable (e.g. ar velocty), Ahmed et al. (2009) developed a data mnng technque to estmate the thermal comfort usng room temperature only. A classfcaton model was created usng 4 rooms (wth all necessary sensors avalable) and then appled to 70 rooms wth ar temperature only to predct the thermal comfort category (e.g. hot, warm, neutral, etc.) n order to dentfy rooms wth low comfort. Osman (1999) patented a method that overcomes the need of expensve PMV sensors by obtanng occupants' feedback usng nternet votng and then applyng fuzzy logc technques to calculate a new thermostat setpont. Fg.2 shows a long-term comfort vsualzaton allowng to observe the actual vs. expected comfort level over a perod of tme. Ths tool provdes an ntutve pcture showng whether the actual comfort s satsfed (or compromsed) and can ndcate whether there are any savngs opportuntes: e.g. durng coolng season the actual comfort ndex may be acceptable, but lower than necessary to meet expected occupant s comfort (t's unnecessarly cold). Increasng the PMV would not volate the comfort, but wll lead to energy savngs (by usng less coolng energy). 2.1 Thermal comfort montorng Thermal comfort n a room or zone s tradtonally used for HVAC control only,.e. the requested comfort level (as defned by a faclty manager and/or occupants) s used as a setpont for the HVAC system. However, the actual comfort level s usually not montored, nether for detectng possble adjustments of the HVAC operaton nor for analyzng and reportng the actual comfort level (.e. occupants' satsfacton). Snce the acceptable comfort level can typcally be acheved by varous combnatons of all factors nfluencng the comfort (temperature, humdty, ar speed, etc.) such montorng can help to fnd better setponts for HVAC control. By "better" are meant setponts achevable wth less energy, whle the requested comfort level s stll met. Comfort montorng can also help to dentfy f and when the requested comfort s not met and correlate these stuatons wth other data (tme of day, weather, AHU mode, etc.) for detaled root cause analyss. Comfort level s commonly defned by room temperature only; for takng nto account other comfort-related factors, several authors have proposed to adopt the predcted mean vote (PMV) as a thermal sensaton ndex (Ahmed et al., 2009; Osman, 1999). The PMV s a real number, whose value near zero corresponds to "neutral", whle -3 and +3 correspond to "very cold" and "very hot", respectvely (ASHRAE, 2005). However, usng thermal comfort montorng for adjustng HVAC operaton should not be confused wth adjustng zone Fg.2: Vsualzaton of room thermal comfort. 3. RULE-BASED CONTROL Rule-based control s currently vewed as the state-of-the-art n supervsory control. The beneft of the rule-based approach s applcablty to most systems, clear physcal meanng of rules, and flexblty to be adjusted for a partcular equpment sub-type. In addton, f some rule cannot be employed due to mssng sensors, a smpler rule (wth lower sensor requrements) can be desgned. Although rule-based methods are wdely used, the complexty of control sequences and correspondng heurstcs vares to a great extent. In our opnon, more advanced rulebased strateges are beng mplemented n Europe where emphass on energy effcency has a longer tradton than n other world regons, mostly because of tradtonally hgh energy prces. Table 1 provdes an llustratve lst of possble rule-based strateges for AHUs. From the complexty vewpont, the reset of supply ar temperature setpont (We et al., 2002) s a good example of strategy that can range from smple (e.g. setpont adjustment usng a lnear functon 3110
of zone temperature) to hghly advanced (setpont correcton reflectng buldng dynamcs, nternal heat gan, outdoor temperature forecast, etc.). Comparson of non-predctve and predctve rule-based strateges has been done by Gwerder et al. (2010). Yu et al. (2007) nvestgated the benefts of fuzzy rules over tradtonal rules obtaned from system desgners. Also note that Douns and Carascos (2009) compared a number of conventonal and advanced control systems (on/off, PID, fuzzy PID, adaptve fuzzy, neural networkbased, agent-based, predctve, robust, etc.) n terms of ther support of DCV control, comfort control (PMV), ablty to ncorporate user preferences, learnng/adaptaton abltes, energy consumpton, and other crtera. Methods focused on mnmzng the supply ar flow represent an mportant group of rule-based control strateges that offer a great potental for energy savngs. For example, Federspel (2004) patented a control strategy for supply fans n VAV systems that reduces the statc pressure at part-load condtons by calculatng the statc pressure setpont as a functon of the arflow rate. Specfcally, VFD fans are modulated so that the statc pressure s reduced below a desgn statc pressure when the arflow rate s reduced below a desgn arflow rate. Cho and Lu (2009) developed a mnmum arflow reset strategy (for sngle-duct pressure ndependent VAV boxes) that calculates the mnmum arflow to satsfy both the heatng load requrements and ventlaton requrements. However, the method requres expermental measurement of room heatng load (W) as a functon of outdoor ar temperature, whch seems as a tmeconsumng task (f such method would be appled to all rooms n a buldng). Fg. 5 shows a hgh-level scheme of a specfc control strategy of ntermedate complexty. It s based on PMV (as dscussed n Secton 2.2), a thermal comfort ndex known for decades, but not yet wdely used for AHU or VAV control. Fg.5: Example of a rule-based control strategy for AHU. The strategy operates as follow. In Step 1, PMV setpont s defned usng a PMV scheduler that takes nto account the geographcal locaton (of a gven buldng), current season (summer, wnter), room type (e.g. offce, gym, ballroom), etc. In Step 2, the PMV space s computed for a gven range of zone ar propertes and occupant-related parameters (actvty level, clothng nsulaton). Then, zone ar propertes target area s determned as a subset of the PMV space that conssts of ponts satsfyng the gven PMV setpont. The zone target area can be a 2D plane (n temperature / humdty plane, or temperature / speed plane) or 3D subspace (n temperature / humdty / speed space). In Step 3, supply ar propertes target area s computed by transformng the zone ar propertes target area usng varous reset methods, ncludng supply ar temperature reset, supply ar humdty reset, and varous correctons based on nternal heat gan (dependng on current occupancy), buldng dynamcs, current weather and/or weather forecast. Step 4 s to defne a mnmal porton of the outdoor ar (based on regulatons, demand-controlled ventlaton, etc.). Step 5 s to search for the cheapest feasble path n the psychrometrc chart,.e. to fnd the best "startng pont" (optmal porton of outdoor ar) and the best "end pont" (supply ar temperature/humdty setpont). 4. MODEL PREDICTIVE CONTROL The prmary goal of any HVAC control system s to mantan predefned comfort levels n zones, whle mnmzng the overall operatng costs, whch are usually reduced to the costs of prmary energy sources. An MPC-based soluton addresses ths goal by modellng the relatons between optmzed varables, zone comfort, and energy costs. Formally, the cost mnmzaton problem can be descrbed by the followng equaton: mn f ( x0, u, d ) u subject to x u l l 1..H x g x, u, d ) x u u h ( 0 Here, x 0 s a vector of the current system state varables (e.g. zone comfort varables), x are future system states, whch are predcted by a functon g and satsfyng defned comfort constrants, u s a vector of optmzed acton varables, usually future HVAC setponts, and d s a vector of dsturbance varables, usually weather forecast. Mathematcal solver mnmzes the cost functon f over the defned optmzaton horzon H whle keepng future comfort n a defned range between x l and x h and setponts satsfyng the box constrants u l and u h. The models f and g can be mplemented as statstcal blackbox models, can be derved from physcal equatons, or can ft somewhere between these two approaches. The choce of a sutable optmzaton method depends on a partcular mplementaton of the models f and g, and on the types of acton varables u. MPC control brngs a number of potental advantages compared to a tradtonal rule-based method. Generally, MPC better handles constrants, complex dynamcs, and tme delays that exst n the HVAC systems. MPC can also better cope wth nteractons between multple varables and mnmze the effect of dsturbances. The predctve nature of MPC enables to mprove the control by ncludng nformaton about future dsturbances (Frere, 2008; Oldewurtel, 2010; Zavala, 2010), such as ambent condtons or buldng occupancy. More mportant than typcal mprovements n h 3111
terms of qualty of control are potental energy savngs that can be acheved by an MPC controller manpulatng wth selected man setponts. Potental applcatons of MPC n buldngs are not lmted to only classcal HVAC systems. Natural extensons can nclude lghtng and blnd postonng systems (Oldewurtel et al., 2010), as well as a broad portfolo of generaton and storage technologes (Zavala et al., 2010) ncludng wnd and solar power, cogeneraton, batteres, ce storage, or fuel cells. Optmal control of such complex system have to take nto account future heatng, coolng, and electrcty demand of gven buldng, local electrcty producton from renewable sources, as well as varable prces of electrcty and other prmary energy sources. Although MPC has a long tradton n the process ndustres, such as ol refneres and chemcal plants (Camacho and Bordons, 2004), and t also has an undsputed potental for buldngs, MPC applcatons have not yet been wdely deployed due to specfc aspects of the buldng envronment. Soluton setup. MPC controller setup for a refnng unt mples not neglgble effort assocated wth step testng and model dentfcaton, whch both need to be conducted by a qualfed person. However, the same requrements on tme and people s qualfcaton cannot be fulflled n HVAC projects as they are mplemented today. To make the soluton setup and confguraton easer, two dfferent strateges can be employed. The frst one s to use smple statstcal or other black-box models wth only mnmum nformaton about specfc HVAC arrangement. Ths can be a vable opton especally for smaller nstallatons. Another opportunty n the future wll come wth standardzed Buldng Informaton Models (BIM), whch wll contan complete nformaton about buldng systems and equpment, and thus make the soluton setup procedures easer and faster. Mantenance costs. Modern buldngs are usually dynamc envronments, whch are subject to contnuous and relatvely frequent changes, such as occupancy changes, HVAC equpment replacements, system retrofts, control system adjustments, or equpment faults. All ths can negatvely nfluence the valdty of the prevously dentfed model and requre ts updatng. Bnary varables. HVAC system operaton s characterzed by numerous bnary varables. Coolng plants, heatng plants and ar handlers operate n dscrete modes. Also the overall system operaton s not contnuous lke n the process ndustres but followng daly cycles that mply on / off swtchng of equpment. Presence of bnary varables leads to a hybrd MPC problem formulated as MILP or MINLP task, whch s consderably more dffcult to solve than wth contnuous varables only. System complexty. In case of large buldngs or buldng complexes and campuses, the optmzed HVAC system can contan multple plants and hundreds of zones to be consdered. In such cases t s necessary to apply MPC only to sub-systems of reasonable sze but even such a decomposton strategy may lead to a large mplementaton project. Modellng. A number of dfferent modellng concepts are used nowadays to support dfferent aspects of buldng operaton. Frst prncple buldng models based on physcs and thermodynamcs are useful durng computer-aded desgn. Then statstcal regresson models have been employed prmarly to develop buldng energy consumpton baselnes, whch are used to detect anomalous energy consumpton, and can also support comparson of energy performance wthn a group of smlar buldngs. All these models are used typcally off-lne by archtects, analysts, or performance contract engneers. Models sutable for control applcatons must capture system dynamcs and need to be dentfed by applcaton engneers who, however, are currently lackng such sklls. 5. CONCLUSIONS Although the advanced solutons for HVAC systems are not yet wdely deployed due to the reasons summarzed n the ntal part of the paper, the followng trends and antcpated advances n the feld should make t possble n the foreseeable future. New sensng technologes. Gven the tradtonal push on low nstallaton costs there has already been made a sgnfcant progress wth wreless sensng technologes that have been wdely adopted n many dfferent felds, ncludng buldng automaton. One of the desres n ths area s to develop nonnvasve, easy-to-nstall, and low-cost sensors that can communcate wrelessly wth Buldng Management Systems, and thus, they can enable a varety of advanced applcatons that generally requre data and nformaton currently not avalable. Another new source of data could be provded by nferental sensors, whch wll be developed for parameters that are dffcult to measure, such as buldng occupancy. Buldng Informaton Models (BIM). Buldng nformaton modellng s the process of capturng and managng buldng data durng ts lfe cycle. It has attracted a consderable nterest after the study done by the U.S. Natonal Insttute of Standards and Technology (NIST) that qualfed sgnfcant fnancal losses assocated wth neffcent nteroperablty and nformaton sharng. Although BIM s now beng adopted and promoted prmarly by large scale buldng developers and cvl engneerng organzatons, BIM models brng a lot of value also to buldng automaton companes (Schen, 2007). BIM can help to get the rght nformaton on e.g. specfc sensor placement, damper settngs, or connecton between ndvdual ar handlers and zones. In the long-term horzon, advanced HVAC applcatons wll be able to collect all nformaton needed for ther setup automatcally wthout human nterventon. System ntegraton, nteroperablty and standardsaton. Industral trends are headng towards open systems, whch can share nformaton va standardsed communcaton protocols. Ths wll help to elmnate the problem of legacy control hardware and software, whch causes dffcultes wth overall system mantenance. Integraton and mproved nteroperablty between varous buldng subsystems (e.g. for securty or safety) wll also enable access to nformaton 3112
sources that cannot be reached today. For nstance, securty systems and access logs may hold very useful nformaton about occupancy patterns and typcal human behavour, whch could be leveraged for mproved HVAC control. Smart grd. Smart grd s an mportant drver for operatonal changes of electrcty consumers across ndustral, commercal and resdental segments. Consumer Energy Management Systems (EMS) prmarly n homes and buldngs are expected to be applcatons playng mportant roles wthn the ecosystem of next generaton electrcty networks. The need to better montor and manage energy consumpton wll create pressure on emergence of new control solutons for nternal buldng systems. Demand response, dynamc load management, or optmal dspatchng of generaton, consumpton and storage devces are examples of complex control and optmzaton tasks that wll requre effcent solutons, and thus wll drve further progress n the feld. REFERENCES Ahmed, A., Plönngs, J., Gao, Y. and Menzel, K. (2009). Analyse buldng performance data for energy-effcent buldng operaton. Proc. 26th Internatonal Conference on Managng IT n Constructon, Istanbul, Turkey, 2009. ASHRAE (2005). Handbook of Fundamentals. Amercan Socety of Heatng, Refrgeratng and Ar Condtonng Engneers, Atlanta, GA, 2005, ISBN 1-931862-71-0. Brandemuehl, M.J. and Braun J.E. (1999). The Impact of Demand-Controlled and Economzer Ventlaton Strateges on Energy Use n Buldngs. ASHRAE Transactons 105 (2). Camacho, E. F. And Bordons, C. A. (2004). Model Predctve Control, Sprnger-Verlag London. Cho, Y.-H. and Lu M. (2009). Mnmum arflow reset of sngle duct VAV termnal boxes. Buldng and Envronment 44, pp. 1876 1885. Douns, A.I. and Carascos, C. (2009). Advanced control systems engneerng for energy and comfort management n a buldng envronment A revew. Renewable and Sustanable Energy Revews 13, pp. 1246 1261. Emmerch, S.J. and Persly, A.K. (2003). State-of-the-art revew of CO2 demand controlled ventlaton technology and applcaton. NIST Report 6729, Natonal Insttute of Standards and Technology, pp. 1 43. Federspel, C. C. (2004). Method and apparatus for controllng VAV supply fans n HVAC systems, Unted States Patent no. 6,719,625 B2. Frere, R., Olvera, G. and Mendes, N. (2008). Predctve controllers for thermal comfort optmzaton and energy savngs. Energy and Buldngs 40, pp. 1353 1365. Gabel, S. and Krafthefer, B. (1997). Automated CO2 and VOC-Based Control of Ventlaton Systems Under Real- Tme Prcng. Techncal Report WO2830-18. Gwerder, M., Gyalstras, D., Oldewurtel, F., Lehmann, B., Wrth, K., Stauch, V., Tödtl, J. (2010). Potental Assessment of Rule-Based Control for Integrated Room Automaton, n: Proc. 10 th REHVA World Congress, Antalya, Turkey, May 2010. Oldewurtel, F., Parso, A., Jones, C. N., Morar, M., Gyalstras, D., Gwerder, M., Stauch, V., Lehmann, B., Wrth, K. (2010). Energy Effcent Buldng Clmate Control usng Stochastc Model Predctve Control and Weather Predctons, n: Proc. 2010 Amercan Control Conference, Baltmore, MD, USA, June 2010. Osman, A. (1999). Method and apparatus for determnng a thermal setpont n a HVAC system. Unted States Patent no. 6145751. Schen, J. (2007). An nformaton model for buldng automaton systems. Automaton n Constructon 16 (2007), pp. 125 139. Trojanova, J., Vass, J., Macek, K., Rojcek, J. and Stluka, P. (2009). Fault Dagnoss of Ar Handlng Unts. n: Proc. SafeProcess 7th IFAC Internatonal Symposum on Fault Detecton, Supervson and Safety of Techncal Processes, Barcelona, Span, 2009. Vass, J., Trojanova, J., Fsera, R. and Rojcek, J. (2010). Embedded Controllers for Increasng HVAC Energy Effcency by Automated Fault Dagnostcs. n: Proc. GREEMBED 2010, Stockholm, Sweden, Aprl 2010. Vasyutynskyy, V., Plönngs, J., Kabtzsch, K. (2005). Passve Montorng of Control Loops n Buldng Automaton. n: Proc. FeT2005 6 th IFAC Internatonal Conference on Feldbus Systems and ther Applcatons, Puebla, Mexco, November 2005, pp. 263-269. We, G., Turner, W.D., Clardge, D.E. and Lu, M. (2002). Sngle-Duct Constant Ar Volume System Supply Ar Temperature Reset: Usng Return Ar Temperature or Outsde Ar Temperature?, Proc. 2nd Internatonal Conference for Enhanced Buldng Operatons, Texas, USA, October 2002. Yu, Z., Zhou, Y., Dexter, A. (2007). Herarchcal Fuzzy Rule-Based Control of Renewable Energy Buldng Systems. In: Proc. 2007 CISBAT Conference, Lausanne, Swtzerland, September 2010. Zavala, V. M., Antescu, M., Constantnescu, E., Leyffer, S., Wang, J., Conzelmann, G. (2010). Proactve Energy Management for Next-Generaton Buldng Systems, n: Proc. IBPSA-USA SmBuld 2010, New York, NJ, USA, August 2010. 3113