THERMAL COMFORT MONITORING IN ...

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Proceedings of SEBUA-12 ICHMT International Symposium on Sustainable Energy in Buildings and Urban Areas October 14-20, 2012, Kusadasi, Turkey SEBUA-12-S404

THERMAL COMFORT MONITORING IN COMMERCIAL BUILDINGS Jiří Vass, Jiří Rojíček§ and Jana Trojanová Honeywell Advanced Technology Laboratory, Prague, Czech Republic § Correspondence author. Fax: +420 234 625 900, Email: [email protected] *

ABSTRACT This paper presents a system for thermal comfort monitoring using the Predicted Mean Vote (PMV). The system is capable of identifying opportunities for energy savings and indicating violation of occupants' thermal comfort. The system consists of multiple modules, including PMV scheduler, PMV thresholding, and PMV visualization (for both online and historical data monitoring). The system has been applied to real data from commercial buildings and interesting PMV-based charts have been obtained. However, since PMV computation requires sensors that are rarely available (e.g. air velocity), alternative approaches for determining PMV are reviewed, including PMV sensors and inferential techniques (soft sensors). INTRODUCTION Thermal comfort in a room or zone is traditionally used for HVAC control only, i.e. the requested comfort level (as defined by a facility manager and/or occupants) is used as a setpoint for the HVAC system. However, the actual comfort level is usually not monitored, neither for detecting possible adjustments of the HVAC operation nor for analyzing and reporting the actual comfort level (i.e. occupants' satisfaction). Since the acceptable comfort level can be typically achieved by various combinations of multiple factors (temperature, humidity, air velocity, etc.) such monitoring can help to find better setpoints for HVAC control. By "better" are meant setpoints achievable with less energy, while the requested comfort level is still met. Comfort monitoring can also help to identify if and when the requested comfort is not met and correlate these situations with other data (time of day, weather, HVAC mode, etc.) for detailed root cause analysis. This paper is concerned with thermal comfort monitoring for commercial buildings. The predicted mean vote (PMV) is used as a comfort level indicator and visualized together with relevant sensor data to identify occupant comfort violation and savings opportunities. The proposed method has been applied to real building data and promising results have been obtained. It is important to mention that monitoring of thermal comfort for adjustment of HVAC operation should not be confused with adjustment of zone temperature setpoints – both can save energy, but the latter may compromise occupants’ comfort. The paper is organized as follows. Section 1 provides theoretical background for PMV computation. In Section 2, results of PMV literature review are presented. Section 3 describes the proposed method in detail. Section 4 summarizes the results of applying the method to real building data.

THEORETICAL BACKGROUND The Predicted Mean Vote (PMV) is a thermal comfort index (developed by Fanger [1972]) that predicts the mean vote of a large group of people on a 7-point thermal sensation scale [ISO 2005].

PMV became the most widely used empirical index for evaluation of indoor thermal conditions in moderate environments. PMV is based on heat balance equations for the human body and was derived using a steady-state model of thermal exchanges between the human body and the environment. Fanger believed that a person’s thermal sensation at a specified activity is only related to the thermal load on the body, where thermal load is defined as the difference between the internal heat production and the heat loss to the actual environment for a person hypothetically kept at comfort values of mean skin temperature and evaporative heat loss by regulatory sweating at the actual activity level [Ye et al. 2003]. The PMV takes into account temperature, relative humidity, indoor air velocity (vi), radiant temperature (Tmrt), as well as occupants' activity levels (metabolic rate) and their clothing levels. All six factors are taken into account in Fanger's Comfort Equation (FCE): PMV = ( 0.352 × exp( -0.042(M/ADu) ) + 0.032 ) ×

{

( M/ADu ) (1-η)

- 0.35 [ 43 - 0.061 (M/ADu) (1-η) - pa ] - 0.42 [ (M/ADu) (1-η) - 50 ] - 0.0023 (M/ADu) (44 - pa) - 0.0014 (M/ADu) ( 34 - Ta ) - 3.4 × 10-8 fcl × [(Tcl + 273)4 - (Tmrt + 273)4] - fcl hc (Tcl - Ta) ] }, (Eq. 1) where M is the metabolic rate of human body [met], Ta is the air temperature [°C], Tmrt is the mean radiant temperature, pa is the vapour pressure of ambient air [Pa], ADu is the DuBois area [m2] (the surface area of the nude body), η is the external mechanical efficiency, fcl is the clothing area factor (ratio of the surface area of the clothed body to the surface area of the nude body), hc is the convection heat transfer coefficient [kcal/hr m2 °C], and Tcl is the clothing temperature [°C] (mean temperature of outer surface of clothed body). The temperature Tcl is computed iteratively from the following equation: Tcl = 35.7 - 0.032 ( M/ADu ) (1-η) - 0.18 Icl [ 3.4 × 10-8 fcl [ (Tcl + 273)4 - (Tmrt + 273)4 ] ] + fcl hc ( Tcl - Ta ), (Eq. 2) where Icl is the clothing insulation [clo] (thermal resistance of the clothing). Note that 1 clo = 0.155 (m2 K) / W and 1 met = 58.1 W/m2. The PMV predicts the mean response of a large group of people according to the ASHRAE thermal sensation scale [ASHRAE 2005]. When PMV is around zero (between -0.84 and 0.84), thermal comfort is maintained; +1, +2 and +3 indicate slightly warm, warm and hot conditions, respectively, while -1, -2 and -3 correspond to slightly cool, cool and cold, respectively. The PMV has been widely adopted by the HVAC engineering community and can be determined by various software tools [Marsh 2005; LumaSense 2012] that compute PMV according to the ISO 7730 standard [ISO 2005]. Also note that many other indicators of thermal comfort have been developed [ASHRAE 2005]: • • • • • • • • •

Predicted Percentage Dissatisfied (PPD), Operative Temperature (to), Effective Temperature (ET), New Effective Temperature (ET*), Standard Effective Temperature (SET), ASHRAE Standard Effective Temperature (SET*), Heat Stress Index (HSI), Wind Chill Index (WCI), Discomfort (DISC), Thermal Sensation (TSENS), Draft Rating (DR).

STATE-OF-THE-ART REVIEW In this section, we will review a number of PMV-related papers that can be classified into three main areas: a) PMV sensors, b) inferential techniques to overcome the absence of sensors needed for PMV computation, c) application of PMV to HVAC control and comparing control strategies. PMV Sensors. Ye et al. [2003] developed a novel instrument for measurement of PMV and SET* indices. The instrument consisted of three sensors (temperature sensor, relative humidity sensor, and equivalent temperature sensor) and processing unit (where preset values of metabolic rate and clothing insulation were also stored). The equivalent temperature sensor was represented by a 90-diameter heated black globe which was used to imitate the dry heat loss of the human body (i.e. to simulate a real person). Depending on the control method, the globe is measuring either the mean surface temperature of itself, or the calorific value of heater (located inside the globe). The coefficients of the comfort equation (Eq. 1) were obtained under controlled laboratory tests. The authors reported a reasonable agreement between the true PMV value and the value measured by the PMV sensor (under controlled laboratory tests). However, the authors emphasized the need to perform field trials of the instrument in order to evaluate its performance in practical conditions. With respect to the PMV sensor developed by Ye et al. [2003], it is worth mentioning the availability of advanced instruments for measuring thermal comfort, e.g. the INNOVA 1221 Thermal Comfort Data Logger [LumaSense 2007]. The instrument consists of three modules (comfort module, heat stress module, and dry heat loss module) and has sockets for the following transducer types: air temperature, surface temperature, operative temperature, humidity, air velocity, dry heat loss, radiant temperature asymmetry, etc. The transducers have been designed specifically for use in indoor climates and are compliant with the ISO 7730 and 7726 standards [LumaSense 2007]. For example, the velocity transducer reacts very quickly (< 0.2s) to changes in air velocities and measures omnidirectional air velocities with an excellent accuracy at the low velocity range (for vi < 1 m/s, the accuracy is ±(0.05 vi + 0.05) m/s for any flow direction greater than 15° from rear of transducer axis). The radiant temperature asymmetry transducer is insensitive to influence from air velocity, and is able to evaluate radiant asymmetry discomfort from hot and cold surfaces (due to its ability to measure plane radiant temperature in several directions). Although the 1221 thermal comfort data logger is too expensive to be installed permanently in a room, it can be used to provide referential measurements for development of inferential sensing methods. Inferential Sensors for PMV Estimation. Since PMV computation requires sensors that may not be commonly available (e.g. air velocity), several authors have explored the area of inferential sensing techniques to replace the missing sensors. Inferential sensors (also known as virtual sensors or soft sensors) are mathematical algorithms (multivariable models) that utilize existing datapoints to compute (approximate) a missing variable. Well-known approaches include Kalman filters, neural networks and fuzzy computing [Venkatasubramanian 2003]. A detailed review on soft sensors was presented by Kadlec et al. [2009]. Ahmed et al. [2009] developed a data mining technique to predict the thermal comfort using room temperature only. A classifier was trained using four rooms (with all necessary sensors available) and then applied to 70 rooms with air temperature only to predict the thermal comfort category (neutral, slightly cool, cool, slightly warm, warm) in order to identify rooms with low comfort. The classifier was based on the Naive Bayes algorithm (available in the Oracle Data Miner) assuming that the room attributes (e.g. comfort class) are independent. Osman [1999] patented a method that overcomes the need of expensive PMV sensors by obtaining occupants' feedback using internet voting and then applying fuzzy logic techniques to calculate a new thermostat setpoint.

PMV in HVAC Control. Tham [1993] used PMV for exploring a number of energy conservation measures (ECMs) and quantifying their impact on thermal environment. The exploration was done using the DOE-2 energy simulation program applied to a commercial office building in Singapore. Parametric simulation runs were conducted by holding all parameters at their reference values and varying the value of a chosen parameter (e.g. cooling setpoint, chiller COP, etc.) over its selected range. A new specialized graph was designed (denoted as comfort-energy grid) to visualize the relationship between energy consumption and thermal comfort (PMV) obtained for various values of metabolic rate (M), clothing level (Icl) and relative air motion (vi). The ECMs were ranked according to percentage of saved energy (without sacrificing thermal comfort) with the following results: utilization of daylighting (25% savings), use of high efficiency lighting systems (13%), higher cooling setpoint (12%), and so on (the remaining ECMs yielded savings of 5% or less). Interestingly, Tham reported that minimum airflow ratio had insignificant effects on energy consumption (0.5% savings). Increasing the cooling setpoint was found to be an attractive ECM (each 1 °C rise yields app. 6% savings in cooling energy) provided that higher air velocities can be achieved (e.g. through supply air grille, supply air velocity design, or other mechanical means). However, the air velocity must not exceed constraints of the office environment (typically 0.4 m/s) to avoid excessive drafts. A well-known limitation of conventional HVAC control systems is the fact that temperature and humidity are controlled separately (i.e. via two independent control loops) [Ye at al. 2003; Marik et al. 2011]. For this reason, a number of authors explored the potential of PMV-based control, or more generally, a comfort index-based control. For example, Yang and Su [1996] developed an intelligent controller to maintain neutral PMV values and reported energy savings by automatically adjusting the air velocity. Dounis and Caraiscos [2009] used PMV as one of the criteria to compare various control control systems (on/off, PID, fuzzy PID, adaptive fuzzy, neural network-based, agent-based, predictive, robust, etc.). Calvino et al. [2010] presented a methodology for comparing control strategies based on two cost functions that evaluate the thermal comfort performance and energy performance, respectively. Thermal comfort performance was quantified using two comfort indices: T

T

I1C = ∫ ( PMVact − PMVspt ) 2 dt = ∫ ( PMVact ) 2 dt , 0 T

I 2C = ∫ 0

(Eq. 3)

0 T

PPDact dt = 1 / 0.05 ⋅ ∫ PPDact dt , PPDspt 0

(Eq. 4)

where T is the observation time, PMVact is the actual PMV value, PMVspt is the PMV setpoint (equal to 0 in this case), PPDact is the actual PPD value, and PPDspt is the PPD setpoint (equal to 0.05, i.e. 5% of dissatisfied corresponding to PMVspt of zero). Due to difficulties in measuring the PMV index, the authors have expressed the PMV as a function of indoor air temperature only: PMV = 0.2262 Ta 4.969 (for M = 1 met, Icl = 1 clo, vi = 0.15 m/s, RHi = 50% and Tmrt = Ta). It was concluded that the index I 2C has a better prevalence in comparison with the I1C index. The authors also proposed an adaptive PID-fuzzy controller that yielded lower energy costs and lower deviations from the PMV setpoint (in comparison with the classic hysteresis ON/OFF controller).

PROPOSED METHOD Fig. 1 depicts the proposed system for thermal comfort monitoring.

Figure 1. High-level scheme of the thermal comfort monitoring system. Step 1 (Data validation): All sensor measurements are checked by a data validation module (also known as data reconciliation/cleansing) responsible for detection of sensor faults, such as outliers, outof-range data, frozen value, missing data and other sensor issues [Kadlec et al. 2003]. More details on the data validation algorithms can be found in our previous papers [Trojanova et al. 2009; Vass et al. 2010]. Step 2 (Look-up tables): This step involves determination of parameters needed for PMV computation, namely the metabolic activity M and the clothing level Icl. The parameters are determined using look-up tables (inspired by the ASHRAE [2005] standard) that utilize the following input parameters: room type (office, gym, shopping mall, etc), occupant type (men, women, mixed) and the current season of the year (summer, winter, swing). The purpose of this module is to determine average or typical values for a given room (or building), which is justified by the fact that most people in a certain room/building usually do a similar activity and have similar clothing insulation (which is especially the case in commercial buildings). In the simplest case, one could only distinguish between the most distinctive room types, for example: •

Office room: Icl = 1 clo (light business suit) and M = 1 met (sedentary activity)



Ballroom: Icl = 0.6 clo (trousers and shirt), M = 2.4 met (dancing)



Gym: Icl = 0.3 clo (shorts and T-shirt) and M = 3.5 met (strenuous labour)

Step 3 (Virtual datapoint computation): The purpose of this module is to estimate all datapoints required for PMV computation but possibly unavailable due to missing sensors. In certain situations, it

may be satisfactory to replace the missing datapoint by a constant value (e.g. air velocity and/or relative humidity); however, it should be kept in mind that different rooms may require different simplifications and assumptions. In case that a particular missing sensor plays a significant role for thermal comfort in a given room/zone, the methods of inferential sensing should be employed to estimate more accurate values of the corresponding datapoint. The following list provides a summary of possible simplification approaches found in the literature: ● Tmrt ≅ Ta (mean radiant temperature is approximated by indoor air temperature). This assumption is valid under the conditions reported in Appendix C of ASHRAE [2004]. ● Tmrt = const and vi = const, i.e. values of mean radiant temperature and air velocity can be regarded as constant in a confined and thermally moderate environment [Calvino et al. 2010]. Step 4 (PMV computation): This module is responsible for computation of the PMV thermal comfort index, and thus represents the essential part of the comfort monitoring system. The standard formulae for PMV computation are utilized (Eq. 1 and 2); however, the datapoints that may not be available (e.g. due to missing sensors) must be supplied by the preceding module (virtual datapoint computation). The subject of dealing with missing data will be discussed in the "Missing Sensors" Section. The output of the PMV computation module will be referred to as the PMV signal (time series of PMV values computed for all available timestamps). Step 5 (PMV scheduler): PMV Scheduler determines PMV thresholds based on the geographical location of a given building (e.g. state or city), current season (summer, winter, swing) as well as possible thermal comfort adjustment by room occupants. The comfort adjustment may be expressed in terms of PMV offset that can be selected manually (e.g. by facility manager or occupants) or determined automatically by means of next-generation thermostats. Step 6 (PMV thresholding): This module compares the PMV signal with a set of PMV thresholds, and performs classification of each PMV sample to a specific thermal comfort category. The PMV thresholds are depicted in Fig. 2.

Figure 2. Definition of PMV thresholds for the cooling and heating season. The specific values of PMV thresholds (Tlow, Thigh and TOK) depend on the desired comfort class (i.e. Class A with a high comfort, Class B with a normal comfort, and Class C with a relaxed standard of comfort) and the geographical location of a given building. Here we will focus on Class B spaces only (i.e. 80% acceptability), which correspond to Tlow = -0.84 and Thigh = +0.84 (corresponding to PPD of 20%). The threshold TOK can be adjusted automatically (depending on the current season) between e.g. TOK = +0.3 (for cooling season) and TOK = -0.3 (for heating season). The idea is to stay on the

advantageous side of the PMV range at the right time, i.e. to provide a slightly warmer (but acceptable) environment during the cooling season (to save cooling energy) and a slightly cooler environment during the heating season (to save heating energy). As a consequence, the cooling setpoint could be slightly increased (e.g. by 1 °C) during the cooling season, which may bring energy savings of approximately 6% (as reported by Tham [1993]). As discussed earlier, the PMV offset can be utilized to shift all thresholds towards a more suitable comfort range for a given geographical region. For example, as occupants in North America tend to prefer cooler room environment than Europeans, the PMV offset of -0.5 can be applied to all PMV thresholds. Step 7 (Zone air homogenity analysis): As an optional step, larger rooms (with multiple zones) can be used for statistical analysis of zone air homogenity in order to evaluate the quality of air mixing achieved by different control strategies. Details of this step are beyond the scope of this paper. Step 8 (Online data visualization): Online visualization of thermal comfort (see Fig. 3) serves primarily as a user awareness tool. Displaying the current thermal comfort together with energy required for maintaining such comfort may motivate some users for changing their requirements (setpoints). The awareness tool should stimulate the occupants to think about environmental impact of their thermal comfort (e.g. Do I really need my cool office (22 °C) during hot (32 °C) summer days? How about being more environmentally friendly and decreasing my setpoint to 25 °C?). Step 9 (Historical data visualization): Long-term comfort level visualizations (see Fig. 4) are based on archived historical data and provide a basis for analyzing thermal comfort from a global perspective (weekly/monthly comparisons, comparisons of multiple rooms/floors/buildings, etc). Such visualizations can be useful to identify rooms whose thermal comfort deviates significantly from the expected behaviour and to analyze patterns in occurrences of discomfort. RESULTS Fig. 3 depicts the current zone air properties (black circle) in the PMV space. This visualization can be used for evaluating possible setpoint adjustments to save energy. For example, during the cooling season the cooling energy can be reduced by keeping the PMV as high as acceptable, i.e. the current conditions (black circle) should be maintained within the red area (as shown in Fig. 3). Similarly, during the heating season the heating energy can be reduced by keeping the PMV as low as acceptable, i.e. the current conditions should be kept within the blue area.

Figure 3. Online comfort level visualization using the PMV space.

In case that some variable (e.g. air velocity) is not measured, the online monitoring can be performed within 2-D temperature-humidity subspace. The missing variable could be replaced by a constant value that represents the most realistic estimate for a given room, e.g. vi = 0.3 m/s (barely noticeable) for an office room. Similarly, if the relative humidity sensor is missing (e.g. may not be needed due to low usefulness in a given geographical region), the visualization can be projected to the 2-D temperaturevelocity subspace. Fig. 4 shows a long-term comfort visualization allowing to observe the actual vs. expected comfort level over a period of time. This tool provides an intuitive picture showing whether the actual comfort is satisfied (or compromised) and can indicate whether there are any savings opportunities: e.g. during cooling season the actual comfort index may be acceptable, but cooler than necessary to meet the expected occupant’s comfort (this case is classified as luxury cool). Increasing the PMV would not violate the comfort and would lead to energy savings (by using less mechanical cooling energy).

Figure 4. Long-term comfort level visualization (real building data). MISSING SENSORS This section is concerned with challenges related to applying the proposed system to real buildings. As introduced earlier, the main difficulty in determining the PMV index is the fact that its computation requires sensors that are not commonly available in commercial buildings. Therefore, the proposed system should be seen as a high-level concept only, with many practical challenges still to be solved. On the other hand, we believe that the general idea is correct and the proposed comfort visualizations will be found valuable by other authors. The use of PMV sensors by Ye et al. [2003] represent an interesting area to apply our comfort monitoring system in practice, although more field testing is still needed to yield a robust version of the PMV sensor applicable to a real building environment. Therefore, an alternative direction to overcome the missing sensors is the use of inferential techniques (soft sensors) that may be developed using data from thermal comfort data loggers (such as INNOVA 1221 [LumaSense 2007]). Such data may serve as referential (training) datasets for developing various data mining algorithms. It should also be noted that even in cases when the room temperature is the only available sensor (RHi, vi, Tr are replaced by constants), the PMV provides the advantage of taking into account the room parameters (metabolic rate, clothing level), and thus distinguishing between various room types (office, gym, dancing hall, etc). As a result, it is more straightforward to monitor thermal comfort by thresholding the PMV values rather than thresholding temperatures only. In other words, it may be difficult to choose a temperature threshold (e.g. 27 °C for "too hot"), while the PMV thresholds can be

used for comfort classification in a straightforward manner (see Fig. 2). In addition, thresholding of temperatures would require adjustment of thresholds for each room type, while the PMV thresholds are more general. REFERENCES Ahmed, A., Plönnigs, J., Gao, Y. and Menzel, K. [2009], Analyse building performance data for energy-efficient building operation, In: Proc. 26th International Conference on Managing IT in Construction, Istanbul, Turkey, 2009. ASHRAE [2004], Standard 55-2004: Thermal Environmental Conditions for Human Occupancy, American Society of Heating, Refrigerating and Air-Conditioning Engineers, Atlanta, GA, 2004. ASHRAE [2005], Handbook of Fundamentals, Chapter 8: Thermal Comfort, American Society of Heating, Refrigerating and Air Conditioning Engineers, Atlanta, GA, 2005, ISBN 1-931862-71-0. BaaS Project [2012], Building as a Service, 2012, website: http://www.baas-project.eu. Calvino, F., Gennusa, M., Morale, M., Rizzo, G. and Scaccianoce, G. [2010], Comparing different control strategies for indoor thermal comfort aimed at the evaluation of the energy cost of quality of building, Applied Thermal Engineering, 2010, Vol. 30, No. 16, pp. 2386-2395, DOI: 10.1016/j.applthermaleng.2010.06.008. DOE-2, http://www.doe2.com, Homepage of DOE-2 based Building Energy Use and Cost Analysis Software. Dounis, A.I. and Caraiscos, C. [2009], Advanced control systems engineering for energy and comfort management in a building environment – A review, Renewable and Sustainable Energy Reviews Vol. 13, pp. 1246–1261. Fanger, P.O. [1972], Thermal Comfort - Analysis and Application in Environmental Engineering, McGraw-Hill, New York, pp. 110-114. ISO [2005], Standard ISO 7730:2005: Ergonomics of the Thermal Environment - Analytical Determination and Interpretation of Thermal Comfort Using Calculation of the PMV and PPD Indices and Local Thermal Comfort Criteria, International Organization for Standardization, http://www.iso.org, Geneva, 2005. Kadlec, P., Gabrys, B. and Strandt, S. [2009], Data-driven Soft Sensors in the Process Industry, Computers and Chemical Engineering, Vol. 33, No. 4, pp. 795–814. LumaSense [2007], Thermal Comfort Datalogger INNOVA 1221 - User Manual, BB1023-16, 30 pages, LumaSense Technologies. http://innova.lumasenseinc.com/downloads, http://lumasenseinc.com/EN/products/gas-monitoring/thermal-comfort LumaSense [2012], PMV Calculation, LumaSense Technologies http://lumasenseinc.com/EN/products/thermal-comfort/pmv-calculation/ http://lumasenseinc.com/uploads/Products/Gas_Monitoring_Products/Gas_Monitoring_Instruments/So ftware/PMVcalc_v2_English.xls Marik, K., Rojicek, J., Stluka, P. and Vass, J. [2011], Advanced HVAC Control: Theory vs. Reality, In: Proc. 18th IFAC World Congress, Milan, Italy, 2011.

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