Effectiveness of Thermal Comfort Models to Evaluate Occupants’ Satisfaction Levels in Office Buildings

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Procedia Engineering

ProcediaEngineering - Engineering 00 (2011) Procedia 20 (2011) 372 000–000 – 379

www.elsevier.com/locate/procedia

The 2nd International Building Control Conference

Effectiveness of Thermal Comfort Models to Evaluate Occupants’ Satisfaction Levels in Office Buildings C. C. Siewa*, A. I. Che-Anib, N.M. Tawilb, N.A.Goh-Abdullahb, N. Utabertab a

School of Quantity Surveying and Construction Management, Legenda Education Group, BUTL Batu 12, Mantin 71700, Malaysia Department of Architecure, Faculty of Engineering & Built Environment,Universiti Kebangsaan Malaysia (UKM), Bangi 43600 , Malaysia

b

Abstract The efforts of integrating the natural ventilation within the building zones to achieve the optimum local thermal comfort levels have recently shown a significant development in the building industry. A number of operation models have been developed to access the feasibility of natural ventilation to enhance the occupants’ thermal comfort levels. However, each of the models has its own limitations in providing a comprehensive solution to overcome the issues of the natural ventilation and thermal comfort within a building layout. In this paper, the author has reviewed two common thermal comfort models, compared the effectiveness of the models, and proposed an enhanced model framework based on integrated evaluation approach for both thermal comfort and ventilation effectiveness.

© 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Universiti Teknologi Mara Perak and Institute of Surveyors Malaysia (ISM) Keywords: thermal comfort; integrated approach.Introduction

1. Introduction Thermal comfort within a building is the result of interaction between the occupants’ and the building and its surrounding areas that directly influence the human satisfaction levels onto particular building

* Coresponding author : Tel.: +6-06-758 7888; fax: +6-06-758 7599. E-mail address: [email protected].

1877-7058 © 2011 Published by Elsevier Ltd. doi:10.1016/j.proeng.2011.11.179

C.C. Siew et al. / ProcediaN. Engineering 20 (2011)Engineering 372 – 379 00 (2011) 000–000 C. C. Siew, A. I. Che-Ani, N.M. Tawil, N.A.Goh-Abdullah, Utaberta/Procedia

spaces and the occupants are given the chance to take various alternative actions or adjustments to adopt themselves to suit the optimum temperature condition [1], [2], [3], [4]. Therefore, according to Cohen (1997) [5], the major factors that influence the thermal comfort level in particular building can be classified into two main categories, which consist of (1) Human factors, and (2) Environment factors. Thermal comfort has been one of the major decision makers to decide the utility of ventilation systems in a building [6], [5]. In terms of the nature of ventilation, ventilation systems in buildings can be classified into (1) Uncontrollable ventilation, and (2) Controllable Ventilation [7], while if evaluate from the mode of operation, ventilation systems include (1) Active and (2) Passive [8]. Basically, the ventilation systems can be classified into three main groups, there are (1) Natural ventilation, (2) Mechanical ventilation and (3) Hybrid ventilation, which is the combination of the previous two methods [9]. Each of the ventilation systems has its potentials and limitations, and thus, a suitable thermal comfort evaluation approach is needed to assess the suitability of use for particular buildings. 2.

Models for thermal comfort study

Basically, thermal comfort models can be divided into two main categories – (1) Objective approach and (2) Subjective approach [4]. The objective approach is well presented by Fanger’s famous PMVPPD Model, which is the classical heat-balance approach, while the subjective approach means for adaptive thermal comfort. 2.1 Fanger’s PMV-PPD Model This model is frequently used to analyze and evaluation building thermal condition because the output in numerical format is easy to understand. The thermal sensation based PMV-PPD index reveals the average thermal preference of most of the occupants onto the reaction of the surrounding environment as they have been exposed. Basically, PMV-PPD model is conducted in a laboratory environment, where a number of respondents are requested to rate their thermal preference based on the parameters highlighted in the model. These parameters consist of air temperature, relative humidity, air velocity, radiant temperature, cloth insulation and human activity. Through the parameters integration process of the model, a thermal comfort satisfaction level is identified and compared with the listing of preference in order to reveal the ranking of its suitability [5], [10]. According to Charles (2003) [11], there are two important characteristics of PMV-PPD Model: (1) Although this model is designated to justify and predicate the average thermal sensation to most of the building occupants, there are still some of the occupants are not satisfied with the thermal condition obtained. Yet, the output is considered acceptable if the unsatisfactory rate is about 5% of the total occupants. (2) This model considers the reaction of occupants’ thermal sensation onto the thermal comfort level that they expose, and do not consider the detailed analysis of thermal satisfaction level. It emphasizes the concept of thermal balance which means the physiology process of a human body tries to achieve the balance between heat gained from metabolism activities and also heat loss through transmission from human body. Basically, this model is suitable to be applied for simple and easy research scope.

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2.2 Adaptive Thermal Comfort Model Generally, Fanger’s PMV-PPD model is suitable to analyze thermal comfort for conditioned building environment, but this model is criticized for lack of accuracy when it is applied in naturally ventilated buildings [12]. This is because thermal comfort levels of the buildings not only depend on fixed standards that set by the researchers as the parameters have many limitations in their actual application [11]. Moreover, this model should consider the local site conditions that varies seasonally and geographically [13]. Attentions, are thus, needed to be focused onto the utility of the site factors in order to fulfill the local thermal comfort requirement [4]. For instance, according to the research carried out by Oselen and Parson (2002) [14], they compare the index of PMV-PPD obtained with the actual reaction of the occupants in the naturally ventilated buildings, and they found out that the occupants are willing to accept higher temperature range and show better tolerance performance towards the changes of indoor environment than predicted by the Fanger’s PMV-PPD model. Therefore, some modifications are needed to be made onto this model to enhance the effectiveness in analyzing indoor thermal comfort performance. After considering the concept of self-adaptation of the building occupants, especially in hot and humid regions, it is hope that the adaptive thermal comfort model may able to produce better prediction results as it has higher flexibility in its application [15]. In the adaptive model, human are assumed to able to adjust themselves such as open window, changes their cloth types and implement other actions that can help them to achieve desire thermal comfort level, even though the indoor temperature is not within the expected level [16], [17]. Based on this concept, an adaptive thermal comfort formula, is thus, set up to verify the satisfaction level of occupants in naturally ventilated buildings [18], so that a more accurate estimation can be made. The summary of difference between objective and subjective approaches of thermal comfort model can be summarized into the following table: Table 1: Differences between Objective and Subjective Approaches of Thermal Comfort Models Objective Approach (PMV-PPD)

Subjective (Adaptive Model)

Suitable for consistent ventilation, such as air-

Suitable for changing ventilation levels, such as

conditioned building

naturally ventilated buildings.

Objective based / Quantitative based data

Subjective based / Qualitative based data

Human as passive role

Human as active role

Narrow tolerance limit of temperature

Wider tolerance limit of temperature

3. The setting up of new model 3.1 Background Although adaptive thermal comfort model is considered more suitable than PMV-PPD model in evaluating the thermal comfort performance in naturally ventilation buildings [4], its application is limited to general conceptual application as the model only provides general temperature range for overall ventilation performance. Therefore, due to the inconsistency nature of the input characteristics, the accuracy of the output is difficult to be justified, and then the variances of results will distort the effectiveness of the overall estimation.

C.C. Siew et al. / ProcediaN. Engineering 20 (2011)Engineering 372 – 379 00 (2011) 000–000 C. C. Siew, A. I. Che-Ani, N.M. Tawil, N.A.Goh-Abdullah, Utaberta/Procedia

Besides that, the adaptive thermal comfort model only provides general guideline onto the suitability of thermal comfort levels in particular building zones. Even though the surrounding factors and other relevant assumptions have been made, the overall performance is quiet hard to assess as this depends on the subject preferences of the researchers. There are no solid data be provided, such as areas of building elements, to help the designers to evaluate their design strategies. This is quiet important especially for building industry which deals with the issues of spacious design and physical dimensions. Therefore a new working model should be setup that not only able to integrate the related parameters that influence the thermal comfort levels in buildings, but also this model should only able to convert the subjective data into objective ratings, which can be highlighted in measurable format to enable the designers and decision makers to justify the best building layouts in order to optimize ventilation performance. 3.2 Procedure of Model Framework The major parameters in the model comprise of physical and non-physical parameters. The physical parameters includes air temperature, air velocity, proportion of passive design areas, and the site dimensions, while the non physical parameters are mainly consist of occupants’ preference onto thermal comfort and availability of passive ventilation elements. The physical parameters play important roles to affect the satisfaction levels of the occupants’ thermal comfort which is expected in the non-physical parameters. The co-relationship between these two groups of parameters can be further compared and determined using Multiple Regression Analysis. The scaled units for physical parameters are evaluated based on respective units of measurements such as follows: Table 2: Types of physical parameter and respective units of measurement Types of physical parameter

Relevant unit of measurement

Air Temperature

Degree in Celsius

Air Velocity

m/s

Proportion of Passive Design

Ratio

Site Dimensions

m2

There is no specified unit of measurement for non-physical parameters as these parameters are subjective based. However, in order to smoothen the operation of the working model, the individual preference are converted to numerical scale, which value of 1 represents the most unsatisfactory level, and value of 9 is the highest scaled satisfaction level. The explanation of the scale can be further highlighted as follows:

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Table 3: Scale factors for satisfaction levels of thermal comfort Scale Value

Indication

1

Most dissatisfied (either too cold or too hot)

3

Quiet dissatisfied

5

Dissatisfied

7

Acceptable

9

Satisfied

2,4,6,8

Immediate value for adjustment

Site observation and measurement will be conducted to collect the objective data for both the buildings and surrounding areas. The data are collected using scientific measurement or physical parameter tools and equipments as listed below: Table 4: Equipments used to measure physical parameters Types of physical parameter

Equipments / Tools applied

Air Temperature

Temperature & Humidity Datalogger

Air Velocity

Anemometer

Proportion of Passive Design

Laser Distance Meter

Site Dimensions

For non-physical parameters, the data is collected through questionnaire by requiring the respondents to fill up their scaled preference for the criteria specified. After the data from both parameters are collected, they will be tabled and compare the relationship between groups using Multiple Regression Analysis. The co-relationship between the two main groups can be expressed into the following formula: YTC // f (XAT, XAV, XPD, XSD) Where YTC // f(….) XAT XAV XPD XSD

= = = = = = =

Y factor of thermal comfort co-relate with function X factor of air temperature (AT) X factor of air velocity (AV) X factor of passive design proportion (PD) X factor of site dimension

After a series of iteration process for the Xs parameters is conducted, constant values are formed and applied in the formula. The formula can be applied to predict the thermal comfort level for different

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building sites in order to enable the respective parties to take any necessary adjustments or alternatives to enhance their physical building layout design. In short, the operation summary of the model framework is then illustrated as follows:

Air Temperature

Air Velocity Thermal Comfort Preference

Co-relation Index

Proportion of Passive Design

Site Dimensions

Optimum Passive Design Proportion

Figure 1: The Operation Procedure of Alternative Thermal Comfort Model 4. Discussion This innovative thermal comfort model is expected to provide more comprehensive and focused result for the decision makers and designers to finalize their final building layout based on the following reasons: (1) Same as Fanger’s model, this model also takes into account about the reaction o physical human body onto the thermal performance of surrounding environment which can be evaluated in terms of objective basis and scale. This enable the iteration process become easier to be operated and the result output can then be more accurate and justifiable. (2) If compared to adaptive thermal comfort, this new model also considers the subjective and localized factors when operating the analysis works. Subjective values are then transferred into measurable scale for further evaluation. (3) Furthermore, this new model takes into consideration the physical building design and its elements which are more relevant to physical building design. However, this component is not included in both Fanger’s PMV-PPD and Adaptive models. In this model, the effects of openings and blockages are considered, and thus, the results produced are much more reliable. 5. Limitation However, this conceptual thermal comfort model is also suffering from a number of limitations: (1) The model is unable to justify the types of passive design needed to be verified in order to optimize the natural ventilation application. However, it may only provide some general guidelines for the

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designers and decision makers to review their alternative design concepts, based on the suggested proportion between passive design and overall building areas to achieve certain thermal comfort levels. (2) This model is only suitable to be used in hot and humid regions with low density development and surrounded with green spaces. It is less efficient to be applied in high density development within city centres. (3) This model only limits the study areas between the changes of thermal comforts due to external environment (such as air temperature, air velocity, and site dimensions) and the man-made factors (such as passive designs). The issue of operation costs and energy consumption is not included in the model framework. 6. CONCLUSION Basically, the thermal comfort study deals with the human reactions onto the environmental conditions within a particular building space. The two well established models, such as Fanger’s PMV-PPD and Adaptive Thermal Comfort models, are applied widely, but these models do not consider and evaluate the effects of passive design elements onto the effectiveness of ventilation performance, especially natural ventilation. Therefore, an innovated model was proposed to include this component in order to ensure the model is more comprehensive to provide a reliable guidance for the designers and decision makers to optimize their building layout design options. References [1] Michael Humphreys (1997). An adaptive approach to thermal comfort criteria. In D. ClementsCroome (Ed.). Naturally ventilated buildings: Buildings for the senses, economy and society (pp.129138). London: E & FN Spon [2] Dean Hawkes (1997). The user’s role in environmental control: Some reflections on theory in practice. D.Clements-Croome (Ed.). In Naturally ventilated buildings: Buildings for the senses, economy and society (pp. 93-104). London: E & FN Spon [3] Jose A. Orosa (2009). Research on General Thermal Comfort Models. European Journal of Scientific Research, 27(2), 217-227 [4] Noel Djongyang, Rene Tchinda & Donatien Njomo (2010). Thermal comfort: A review paper. Renewable and Sustainable Energy Reviews, 14, 2626-2640 [5] Robert Cohen (1997). Environmental criteria for naturally ventilated buildings. In D.ClementsCroome (Ed.). Naturally ventilated buildings: Buildings for the senses, economy and society, (pp. 105-122). London: E & FN Spon [6] Eley Associates (2004). Hawaii Commercial Building Guidelines for Energy Efficiency. State of Hawaii: Department of Business Economic Development & Tourism (DBEDT) [7] The Building Regulation 2000 (2006). Approved Document F: Means of ventilation. Office of the Deputy Prime Minister: Creating sustainable communities. London: RIBA [8] Ecophon Saint-Gobain. Knowledge Guide: Thermally activated building systems. Sweden: Ecophon Saint-Gobain [9] Heiselberg P. (2002). Principles of hybrid ventilation. Annex 35 report. Hybrid ventilation in new and retrofitted buildings, Hybrid Ventilation Centre, Aalborg University, Denmark. [10] Liang Zhou & Fariborz Haghighat. (2009). Optimization of ventilation system design and operation in office environment, Part I: Methodology. Building and Environment, 44(4), 651-656 [11] Kate E. Charles (2003). Fanger’s Thermal Comfort and Draught Models. IRC Research Report RR162, 1-29

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