Become a Reviewer

Author Guidelines

Review Process

Privacy Policy

Submit Paper



Issues

Current

2016, Volume 1, Issue 1

2016, Volume 1, Issue 2

2017, Volume 1, Issue 1

2017, Volume 2, Issue 1

2017, Volume 2, Issue 2


Google Scholar

ResearchGate


Building Energy Optimization through Thermal Efficiency Determination using Digital Mock-up Simulation for Heritage Building of Cluny Abbey

Yudi Nugraha Bahar and Christophe Nicolle

 Department of Architecture, Gunadarma University, Indonesia

 LE2I, UMR CNRS 6306, Université de Bourgogne, France


ABSTRACT

Improving energy efficiency is the first and most important step toward achieving sustainability in buildings. Buildings designed with sustainable solutions are usually energy-efficient because sustainable design is aimed at producing green buildings. In the case of historic buildings, however, this is quite challenging. Integrated building energy simulation programs have been used in various ways by building professionals to respond to these challenges and to improve building design with respect to sustainability. This paper presents a new approach toward optimizing thermal indoor energy using a digital mock-up simulation of a historical building to explore its potential applicability for other sustainable building projects. The Gunzo room located in an old building situated in the historical site of the Cluny Abbey in France was the object of the experiment. Like many old structures, the building contains rooms with thick walls and large windows that result in temperature fluctuations leading to occupant discomfort. In the present study, we assessed the temperature characteristics of two versions of the room (before and after renovation) based on three types of indicators: Monthly Temperature Profile, Passive Gains Breakdown, and Passive Adaptivity Index.

  Keywords: 3D mock-up, simulation, energy optimization


1. Introduction


The building sector has the greatest potential to reduce energy consumption. Currently, 30% of global CO2 emissions and 40% of global resource consumption result from the constructing of buildings (International WBCSD, 2009). Integrated building energy simulation programs have been used in various ways by building professionals in order to respond to these challenges and to help them improve building designs in the aim of sustainability. This paper presents a new approach to optimizing thermal indoor energy using a digital mock-up simulation in a historical building and explores its great potentials for another sustainable building project. Building thermal simulation is the dynamic analysis of the energy performance of buildings using computer modeling and simulation techniques. In this simulation, a calculation of building thermal loads and thermal consumption are involved in determining the thermal characteristics of the building and its building systems. Building thermal simulation is a powerful method to studying the thermal performance of buildings and to evaluate architectural design. Complex design problems can be investigated and their performance can be quantified and evaluated (Bahar et.al, 2013).

Today, many thermal simulation tools are available and they are different in their manner of simulating thermal parameters. Thermal simulation software is not always based on digital mock-up and does not necessarily present overall results in term of geometry, but rather in term of scales, charts or other notation codes. These formats are sometimes not legible or hard to interpret. Therefore, data exchange methods from design tools to thermal simulation tools with high interoperability still represent a very significant challenge to achieving accuracy in simulation. This study and its experiment was conducted to simulate object-based data models that are specific to the thermal domain using tools provided the IFC (Industry Foundation Classes) format. This choice was made to overcome the limitations of general-purpose geometric representations in particularly in data exchange among the selected tools used for the simulation toward the accurate thermal result.


2. Thermal Performance Analysed in 3D Mock-Up

There are a number of different ways of measuring thermal performance. Thermal analysis is concerned with predicting the usage profile of thermal consumption and calculation within a building by means of the data from internal and external parts of the building. Comprehensive internal and external loads are necessary to provide enough information for a thermal calculation in a building. The external loads are strongly influenced by weather and climate, thus collected and statistically assembled weather data are used in energy performance simulation, while the internal loads came from people, lights and equipment in a space and depend greatly on the actual usage of a space and the behavior of its occupants.

Thermal Comfort is defined in the ISO 7730 standard as: "That condition of mind which expresses satisfaction with the thermal environment" (ASHRAE, 1993). A definition most people can agree on, but also a definition which is not easily converted into physical parameters. An establish comfort criteria according to ASHRAE 55-2004 (American Society of Heating, Refrigerating and Air-Conditioning Engineers) is to support the desired quality and occupant satisfaction with building performance. Design of the building envelope and systems must exhibit with the capability to meet the comfort criteria under expected environmental and use conditions. Using information from the building envelope and systems comfort criteria could then be converted into physical parameters.

Thermal comfort standards are essentially based on a set of air and radiant temperatures and relative humidity levels that will satisfy at least 80 % of the occupants at specified metabolic rates and clothing values. As far as individual thermal sensations are concerned, there are six primary factors identified to most affect the thermal comfort. The modeling of four objective parameters (air temperature, mean radiant temperature, air velocity, and relative humidity) and two subjective parameters (metabolic rate, thermal resistance of clothing) should be ensured in a suitable way (Fanger, 1970).

Mean radiant temperature (MRT) is a primary factor and should be identified as having the strongest effect of thermal comfort. MRT is simply the area weighted mean temperature of all the objects surrounding the body. It will be positive when surrounding objects are warmer than the average skin temperature and negative when they are colder. The MRT is a significant factor, especially in buildings whose envelopes were exposed to a strong solar radiation, and where conventional indoor temperature and humidity control cannot guarantee indoor comfort (Atmaca et.al, 2006). The radiant temperature can be calculated from measured values of the temperature of the surrounding walls and surfaces and their positions with respect to the person. If relatively small temperature differences exist between the surfaces of the enclosure, the following equation can be used (ASHRAE, 1993):


Tmr= T1Fp-1  + T1Fp-1  + …+TNFp-N  [1]


Where Tmr= the mean radiant temperature for a person [ºC], Ti = the temperature of surface i[ºC], FP-i= the angular factor between a person and surface i.

This describes that what we feel in terms of thermal comfort comes from the influence of the surface temperatures in the space as well as the dry bulb (air) temperature (Bean, 2010).

Effects of radiant temperature on human thermal comfort of MRT are investigated for this experiment. After completing a long investigation, we determined on the use of Autodesk Ecotect. Ecotect thermal analysis provides optimistic method for this calculation.

Feasibly, any model can be constructed within the Ecotect environment using its own internal drawing commands, extrusions and other modeling features. Imported models from design tool will universally fail, unless special care is taken to establish each piece of geometry as floor/wall/window/door/aperture. Ecotect will not interpret this on its own with an imported model (Wagner, 2010). Therefore, this experiment used Autodesk Revit 2012, one of the leading design tools in the AEC (Architecture, Engineering, Construction) community due to its high interoperability. The choice of this design tool accounted for various technical considerations, in particular the ease of data organization and the flexibility of data transfer. Most Revit models are quite complicated geometrically in comparison to other CAD project model. There are two commonly used format for transferring data from Revit to Ecotect; GbXML and DXF. The IFC format also accepted in Ecotect (in beta version) as a new opportunity for seamless data exchange. IFC and gbXML are both used for common data exchange between AEC applications such as CAD and building simulation tools (Dong et.al, 2007, Dennis et.al, 2010). Both IFC and XML create a common language to transfer BIM information between different BIM and building analyses applications while maintaining the meaning of different pieces of information in the transfer (Haymaker et.al, 2007).


3. Experiment of Digital Mock-up Simulation

3.1. Object of Experiment (Gunzo Room)

The experiment began with the background checking of the object, the realization of a mock-up model of the existing conditions, followed by the exploration for some alternative models (renovated version scenario). The Gunzo room was chosen as the object of the experiment as representatives of other rooms in the building. The room is located in an old building situated on the historical site of the Cluny Abbey which is also a part of Arts et Métiers ParisTech (ENSAM) campus.

The room volume is approximately 68.5 m3 (length 4.45 m, width 3.93 m, and height 3.92 m). The stone wall is 85 cm thick, and the doors and window are made of wood. The window consists of two panels, with an interior shutter thickness of 2.5 cm. The exterior window sash has one single glass layer, with a thickness of 3 mm. The window faces south-south west. There are three doors on the other side, opposite the window. As it is typical of old structures, the building consists of rooms enclosed by thick walls and large windows, which have been protected. Thermal problems exist in all of the rooms, so it can be quite uncomfortable for the occupants.

As in all listed historical buildings, changes are not allowed to the exterior façade. Therefore, renovation is limited to certain parts of the interior only. In this case, we found that the window was the most crucial factor effecting indoor energy consumption. The interior configuration is adapted to the addition of a partition surmounted for a new window. This concept is used as part of an interior insulation of a building (Ter Minassian, 2011) which has demonstrated its effectiveness, and is becoming common practice in window renovation especially in the Nordic countries.














The renovated version was created according to several alternatives in order to adapt to the requirements of an efficient environment. The renovated window or the new window consists of two panels; an exterior sash and an interior shutter. Due to the need to preserve the historical façade, no changes were made to the exterior sash. We operated a modification on the interior shutter in terms of its position as well as its material. The modification was affected by moving the interior shutter backwards against the exterior sash. This modification required the addition of a shelf under the window sill.

We also modified the position of the heater. Its former position just under the exterior sash was shifted slightly forward, under the shelf of the modified interior shutter. The former heater was also replaced with a modern one for maximum performance. This change was made to optimize the thermal calculation result, and to provide the room with optimum conditions. Several alternatives were considered and then discussed in terms of their energy characteristics, and the most efficient was specified before proceeding with the experiment.




















3.2. Thermal Simulation and Calculation of the Mock-up in Ecotect

After reviewing and testing some applications used to study thermal calculation and visualization, only limited tools enable us to carry out the experiment, due to its interoperability (Cormier et.al, 2011; Dubois et.al, 2010; Attia, 2010; Hanam, 2010; Crawley et.al, 2008). Finally, the model was carried out using Autodesk Revit and Ecotect to incorporate the design process and building performance analysis.





















To conduct the thermal simulation as required to our case of experimentation, we performed the data transfer by means on the smooth data exchange between Autodesk Revit and Ecotect analysis. Creating models from scratch in Ecotect is time consuming. Modelling in Revit has some great benefits; data for energy analysis is ready stored in the model, and easy to export to Ecotect Analysis modelling tool.

The data contains of geometrical, topological, and common surface elements, in real-time as close as possible to the actual space. The main priorities to handle were the environmental settings (verification of conditions) and the mode of calculation (technical method).































Before the object was engaged in final simulation and analysis in Ecotect, the geometry was already set to an ideal adjustment to be eligible as an import file.

Principal operations performed to analyze object (Gunzo room) through Ecotect were as follows:

• 3D Model (Import), Adjacency Checks.

 - Building Geometry including the layout and configuration of space,

 - Building Orientation,

 - Building construction including the thermal properties of all construction elements,

 - Building usage including functional use,

• Weather Manager / Weather Data Import

• Material Properties / Material checks

• Zone Settings / Internal Load and Schedules

 - Internal loads and schedules for lighting, occupants, and equipment,

 - Heating, ventilating, and air conditioning (HVAC) system type and operating characteristics,

 - Space conditioning requirements,

 - Utility rates, Heater usage only from November to April.

• Thermal Analysis / Calculation Mode

Weather data files are being created for design purposes in an increasing number of cities and regions around the world. These weather files do not reflect a specific year, but provide a statistical reference for the typical weather parameters of a specific location.






























In Ecotect, the Admittance Method is used to determine internal temperatures and heat loads. The underlying assumption of the Admittance Method is that the internal temperature of any building will always tend towards the local mean outdoor temperature. Any fluctuations in outside temperature or solar load will cause the internal air temperature to fluctuate in a similar way, though delayed and dampened somewhat by thermal capacitance and resistance within the building fabric. The Admittance Method encapsulates the effects of conductive heat flow through the building fabric, infiltration and ventilation through openings, direct solar gains through transparent materials, indirect solar gains through opaque elements, internal heat gains from equipment, lights and people and the effects of inter-zonal heat flow. It provides near instantaneous feedback and the accuracy of results can be progressively increased as the building model develops.





















































































The thermal analysis is then performed for the selected date/time to determine the air temperature for each zone and the surface temperatures of the object. With this data, which provide the mean radiant temperature, a clothing value, a metabolic rate and a humidity value, it is possible to display comfort values spatially across the grid.




















4. Results of Energy Optimization

4.1. Monthly Temperature Profile

Humphreys et.al (1998) give equations for calculating the indoor comfort temperature from outdoor monthly mean temperature as follows.

Free Running Building:


Tc = 11.9 + 0.534 To     [2]


Heated or Cooled Building:


Tc = 23.9 + 0.295(To-22) exp([-(To-22)/33.941] ²)   [3]


Where To in this case as the monthly mean of the outdoor air temperature and Tc is comfort temperature.

The monthly temperature graph displays the internal temperatures of Gunzo Room thermal zone over a one-year period as measured by Ecotect. This graph is based on the average daily maximum and minimum temperatures, for each month and as an annual statistic, calculated over the year (Chart 1).






















From January to May, the average temperatures hover between 17.7°C and 18.6 °C in the Gunzo room under existing Condition, and they increase to between 19.9°C and 20.4°C after renovation. There is a significant difference in temperature outcomes between the two versions of the Gunzo room, showing that the Gunzo room in its renovated version is 2°C to 3°C warmer during these months. The indoor temperature range is relatively stable at between 17°C - 20°C on the both versions, whereas outside temperatures fluctuate between -8°C and 20°C.

From May to September when the heating is turned off, the range in temperature rose with the outside temperature. Under these conditions, it is assumed that the window remains closed and that the people who occupy the room are wearing summer clothes. The room temperature become slightly hot sometimes, especially in July when the temperatures exceed the comfort band.

From October to December when the heater is turned on, there was a similar trend to that noted in the January to April temperatures, where the room began to adjust to the comfort temperature band. However, there is a significant temperature difference between the two versions in the lower limit of the comfort band. Temperatures in the Gunzo room under existing conditions are on the verge or even below the comfort band, ranging from 17°C-18°C, while the renovated version they are higher and stable at 20°C.

In summer, from June to September when the heater turned off, outdoor temperature fluctuates with an average minimum of 15°C and an average maximum of 27°C. In this condition, the room consistently adapted to the temperature. The temperature in the Gunzo room under existing condition ranged from 24.8°C to 27.8°C, while these temperatures in the renovated version ranged from 25.2°C to 28°C. We note only a little difference in average (1°C), because both rooms versions are characterized by the fact that the heater is turned off.

From October to December, the heater is turned on. While outdoor temperatures ranged from 5°C to 15°C, Gunzo room existing condition temperatures lie on the verge of the comfort zone, ranging from 18.1°C to 18.7°C, while in the renovated version, they are warmer, ranging from 20°C to 20.5°C.


4.2. Passive Gains Breakdown

Passive Gains Breakdown showed gains and losses when comparing the two rooms. The consequence of the window modification in the renovated version is an improvement in thermal insulation. It not only reduced the conduction of solar heat into the room and reduced leakage from the ventilation, but also saved and accumulated more heat from the internal load (Chart 2).





























4.2.1. Conduction loads through fabric

These loads refer only to the gains due to differentials in air temperature between inside and outside the room. Even though in reality it is impossible to distinguish between conduction and indirect solar loads (sol-air gains), computer analysis can deal with it.

The Gunzo room in the existing condition showed losses of 38.3% and gains of 2.0% while the renovated version losses totaled 47.9% and gains 1.6%. The Gunzo room renovated version losses were primarily due to the interior shutter being firmly sealed. The space between the interior shutter and the exterior sash created an intermediary empty space that reduced solar heat entering the room. This is a consequence of the modification of the window to save heat from the internal load. Gunzo room existing condition losses were 38.3%, 9.6% lower than those of the renovated version because heat from solar source was able to easily emanate from the imperfectly sealed window.


4.2.2. Indirect solar loads through opaque objects

This refers to additional gains due to the effects of incident solar radiation on the external surface of exposed opaque objects. The solar radiation acts to raise the external surface temperature which in turn increases the conducted heat flow. As describes earlier, the renovated version of the Gunzo room had improved thermal insulation. The Gunzo room in the existing conditions gains more indirect solar radiation than the renovated version, 20.7% compared to 18.7%. The external surface heated by incident solar radiation in the renovated version was mitigated by the empty space between the two window panels.


4.2.3. Direct solar gains through transparent objects

These loads refer to solar radiation entering the space through a window, void or other transparent/translucent surface. It should be noted that the Admittance method does not track these gains through the window and onto individual internal surfaces. It simply treats them as a space load (as opposed to a fabric load) and uses the admittance of the materials in each zone to diffuse and distribute the heat. Similar to the explanation in point 1 and point 2, the Gunzo room renovated version is protected from direct solar radiation. The Gunzo room in the existing condition gains more direct solar radiation (36.9%) while the renovated version shows 21.7%. The Gunzo room in existing condition can get more heat from direct solar radiation because it is not well sealed.


4.2.4. Ventilation and infiltration gains

This refers to heat transfer due to the movement of air through cracks and openings in the building, such as windows, etc. As the infiltration rate can be easily obtained for each zone, ventilation and infiltration are lumped together in this type of analysis. The Gunzo room in existing conditions in fact shows serious losses in term of ventilation (24.2%). On the other hand, the renovated version loses only 8.3%, due to the window panel modification which is tightly sealed, meaning that thermal insulation is much improved.

4.2.5. Internal loads from lights, people and equipment

These load patterns were accessed by mean of the schedules and object activations. There is little differences between the two versions of the room, the renovated version showing more internal gains, 57.7% as opposed to 38.9%. Certain changes, particularly to the window paneling in the renovated version improved thermal insulation to retain heat better and accumulate it internally.


4.3. Passive Adaptivity Index

The Passive Adaptivity Index (PAI) graph in Ecotect is to evaluate the passive performance of a building. This thermal calculation plots temperature of the selected zone against the prevailing outside temperature for the selected period, and then draws a 'line of best fit'. This gives an index value between 0.0 and 1.0, where a lower index value indicates better passive performance. This line will use to evaluate its consistency to the environment temperature range for comfort (Chart 3).





















Note the colors used to plot the relationship between the Gunzo Room temperature zone and outside temperature - green represents times when both the zone/outside temperature fall within the defined thermal comfort band. Blue represents times when the temperatures are above the thermal comfort band (indicating a cooling load is required), while red represents times when the temperatures are below the thermal comfort band (indicating a heating load is required). With an index value of 0.90, this suggests that this zone does not perform particularly well from a passive perspective; note also that the distribution of blue plotted points indicates that a cooling load is required in this room.






















Chart 4 indicates that there are different levels of adaptation to outdoor temperature between the two versions of the Gunzo Room. The following are analysis of the Chart of adaptability Index of the both version:

- The Index values of Gunzo Room renovated version is 0.23, while at the existing condition it is 0.26. This indicates that the renovated version has a better rate of adaptivity, represented by more green dots around the band of comfortable temperature. The Green dots that form a horizontal line at temperature of 18°C shows the effect of heater use that assures room temperature at least at the lower limit of comfort band.

- Neither version of the room shows red dots, but instead green and small number of blue dots (fig.12), indicating that the rooms were not below of the lower limit of comfort band. Both versions of the room are relatively comfortable, however, the temperature sometimes slightly exceed the higher limit of comfort band. Both graphs have dominant green dots that indicate the rooms have good average level of adaptation to outdoor weather. There appear just a small number of blue dots appear that indicate the rooms have an uncomfortable period when the outdoor temperature is hot (especially in summer).

- There is a difference in the position of the green dots between the two versions of the room. The existing condition version has more green dots distributed further from the comfort band, while after the renovation, the green dots more centered around the band. The 'line of best fit' is not the same in the two versions. After renovation, the minimum point and maximum point of the line were adapted the range of the comfort band. This indicates an increased adaptivity index in the renovated version. For example, at an outdoor temperature of 10°C-12°C, green dots in the Gunzo Room existing conditions graph appears more spread out under the 'Line of best fit', while in the renovated version only few such dots remain.


5. Conclusion

The finding of this study shows an alternative solution for the interior design of an old building, which generally has problems with energy efficiency. Digital simulation is used as a reference experiment to adapt the latest functions of modern buildings. Some alternatives are tried to change the condition of the room. The changes were made through a digital mock-up simulation study, based on energy efficiency parameters.

The Gunzo Room is an object modeled for the present experiment, taken from real office space located in the ENSAM in Cluny. We re-create the room in a digital mock-up in two conditions; actual condition and renovated version. Several alternatives of the renovated version were created to adapt the needs of an efficient environment situation. There were two steps to simulate the mock-up and to obtain the results. Firstly, the mockup was created using Design Tools (Revit) and secondly, thermal analyzing was performed using Thermal Calculation tools (Ecotect). The combination of Ecotect and BIM model of Revit provides a convenient tool to conduct whole calculation through the easier data flow from the BIM model to Ecotect.

Calculation of results from the two versions of the Gunzo Room (existing conditions and renovated version) indicated that there are differences in temperature value between them. This is an indication of the consequence of the renovation. Different effects were produced as designs changed. The third modification by moving the interior shutter backwards against the exterior sash and modifying the position of the heater, were more effective in reducing energy consumption than others.

This experiment examined the temperature characteristics of the two versions of the room through three types of indicators: Monthly Temperature Profile, Passive Gains Breakdown, and Passive Adaptivity Index.

The Monthly Temperature Profile shows that, in winter, temperature in the Gunzo Room renovated version is always above the lower limit of the comfort band, while the temperature in the Gunzo Room existing conditions remains at the lower limit of comfort band. In summer, when the heater is turned off, the indoor temperature in both room versions follows the outdoor average temperature.

Passive Gains Breakdown showed gains and losses when comparing the two rooms. The consequence of the window modification in the renovated version is an improvement in thermal insulation. It not only reduced the conduction of solar heat into the room and reduced leakage from the ventilation, but also saved and accumulated more heat from the internal load.

The discrepancy of 0.03% in the Passive Adaptivity Index, means that during the year, the temperatures in Gunzo room renovated version are relatively more comfortable than the existing conditions. After renovation, the level of room's adaptability index seems to correspond better to the comfort band. Its lower temperature increased significantly to adapt to the comfort band.

This experiment shows the use in simulating interoperable digital mock-up to investigate indoor thermal efficiency. It provides an alternative on the evaluation of the indoor thermal simulation method based on energy determination especially for old building.

Although the functionality of digital mock-up and the use of building thermal tools has progressed significantly in the past few years, much of the potential of their evaluation in terms of energy determination for inside heritage building through interoperability remains largely untapped. Further work is needed to determine if the appropriate design information for use in thermal analyses can be captured simultaneously in real time along the year, in particular its interoperability enhancements for the purposes of sustainable building design.


Acknowledgements

The authors wish to express their gratitude to Gunzo Team of ENSAM Cluny, for their help, as well as providing the laboratory and necessary information regarding the research.


REFERENCES

American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Handbook-Fundamentals, Physiological principles and thermal comfort, 1993. Atlanta, Georgia (Chapter 8).

Attia, S. Building performance simulation tools: Selection criteria and user survey, 2010. ResearchBased Report. Architecture et climat, Université catholique de Louvain, Louvain La Neuve, Belgium. 14-27.

Atmaca, I., Kaynakli, O., Yigit, A., 2006. Effects of radiant temperature on thermal comfort, Journal Building and Environment, Elsevier, 42. 3210-3220.

Yudi Nugaraha Bahar, Christian Pere, Jérémie Landrieu, Christophe Nicolle, 2013.

A Thermal Simulation Tool for Building and Its Interoperability through the Building Information Modeling (BIM) Platform, Journal Buildings. 3(2). 380-398.

Bean, R., Mean Radiant Temperature (MRT), 2010. available online:  http://www.healthyheating.com/Definitions/Mean Radiant.htm#.UTB1sFcySpA (accessed on 16 January 2013).

Cormier A, Robert S, Roger P, Stephan L, Wurtz E., 2011. Towards a BIM-based service oriented platform: application to building energy performance simulation. 12th Conference of International Building Performance Simulation Association, Sydney. 2309-2316.

Crawley, D.B., Hand, J.W., Kummert, M., Griffith, BT., 2008. Contrasting the capabilities of building energy performance simulation programs.Journal Building and Environment Elsevier. 43, 661-673.

Dennis, K.; Roth, S.; Rosen, S.L. Using BIM in HVAC Design, 2010. ASHRAE Journal. vol. 52, 6, 24-32.

Dong, B.; Lam, K.P.; Huang, Y.C.; Dobbs, G.M., 2007. A comparative study of the IFC and gbXML informational infrastructures for data exchange in computational design support environments, Proceedings: Building Simulation. 1530-1537.

Dubois, MC, Horvat, M., 2010. State-of-the-art of digital tools used by Architects for solar design. IEA SHC Task 41 - Solar Energy and Architecture, Subtask B - Methods and Tools for Solar Design.

Fanger, P.O., 1970. Thermal Comfort, Analysis and Applications in Environmental Engineering, Mc.Graw-Hill Book Company, New York.

Hanam, B., 2010. Development of an open source hourly building energy modeling software tool. Thesis Master of Applied Science in Civil Engineering, University of Waterloo, Ontario, Canada. 9-55.

Haymaker, J., Welle, B., 2007. An Integrated Conceptual Design Process for energy, thermal comfort, and daylighting, CIFE Proposal, Stanford University, 2-11.

Humphreys, M.A.; Nicol, J.F., 1998.Understanding the adaptive approach to thermal comfort. ASHRAE Transactions, 104 (1): 991-1004.

International WBCSD, 2009. Energy efficiency in buildings: Transforming the market. World Business Council for Sustainable Development, available online: www.wbcsd.org/ (accessed on 10 February 2015).

Ter Minassian, H., 2011. La réhabilitation thermique des bâtiments anciens à Paris: comment concilier protection du patrimoine et performance énergétique? Cybergeo: European Journal of Geography, Aménagement, Urbanisme, article 536. available online: http://cybergeo.revues.org/23737 (Accessed: 21 January 2016).

Wagner, AD. Revit Architecture+Ecotect.BIM/CAD Camp 2010. US CAD Honolulu Hawai. www.uscad.com, www.uscadbim.com. (accessed: 22 January 2015).


Home 		Submit Paper   Articles 		Reviewer 		Guidelines 		Process 		About 		Contact

PDF

Journal of Buildings and Sustainability - 2017 - Volume 2, Issue 1


INSIGHTCORE ® - Open Access & Scholarly Source of Buildings and Urban Sciences

Author Guidelines     Privacy Policy      Contact      About

Creative Commons License
© This article is licensed under a Creative Commons Attribution 4.0 International License.