Introduction
Global warming and its impacts have increased the need for the reduction of energy demand in buildings. Just in Europe the building sector accounts to 40% of Europe’s energy and CO2 emissions, while globally it is responsible for 39% of the carbon emissions with 28% accounting to operations and 11% to embodied emissions from building materials and construction processes.[1] Considering this, the goal to reduce energy consumption and CO2 emissions have been targeted by the European Commission, this being translated into regulations and policies that impose energy efficiency requirements for new and retrofitted buildings.[2] With this in mind, the design parameters to vary in the building parametric model were defined in order to find a balance within them to reach the best design alternative reaching a good performance of the building.
The chosen civil system to parametric model is a Residence Building and the main goal of the development this parametric model for this typology of building is to support energy efficiency and to fulfill the need of the increasing demand of student accommodation.
Design Parameters
The whole design was influenced by the main purpose of the building, that is providing housing to students. Therefore, the main parameters influencing the building physical embodiment are the Student Room En-suite dimensions (Width and Length). Considering a layout with common areas on the core of the building with the student rooms on the building’s perimeter, the number of rooms per floor will influence on the building form and vice versa.
The selected parameters to control the parametric model are the following:
- Student Room En-suite Width (WR)
- Student Room En-suite Length (LR)
- Floor Height (H)
- Student Residence Building Width (WB)
- Student Residence Building Length (LB)
- Window Area (Aw)
Design Parameters and High-performance Criteria Relation
The concept of building aspect ratio has been employed as an indicator to define the behavior of forms regarding the climate. One concept is expressed as volume to surface ratio (V/S), which has been defined as Compactness (C). The surface area of a building includes wall surfaces, roof surface and ground surface. Since heat losses are proportional to the surface area of the envelope, by having a more compact form, the less heat loss will be. In a context like European countries, a higher C (compactness) will lead to a lower heating load requirement per year. [3]
Although windows provide a lot of benefits offering occupant comfort by passive heating, daylight and views, excessive window area causes increased heating loads thanks to heat-losses, glare, and increased cooling loads during summer months. Therefore, window-to-wall ratio (WWR) is an important parameter to consider for high-performance building design. Based on studies, the optimal WWR for European countries like Germany relies on around 30%-45%[4], which is the goal percentage range for this model.
A third HPC was set in order to identify how the building design fulfills its purpose, which is offering student housing to a sufficient number of students. Therefore, a number of en suite-rooms per floor NR will identify how many students per floor will the different design alternatives offer housing to.
High-performance Criteria Calculations
Knowing and having the previous parametric design parameters, it was possible to calculate the predefined high-performance criteria: Compactness C and number of en suite-rooms per floor NR and Window-to-Wall Ratio WWR.
- For the calculation of Compactness, the volume, and surfaces (façade, roof, and ground surface) were calculated in order to calculate the volume to surface ratio.
- The number of rooms per floor were calculated based on the en suite and building dimensions.
The total WWR was calculated by calculating the total façade area and the total glazing area.
Design Alternatives Identified
Varying the design parameters, it was possible to experiment different configurations and assess the designs in base of their outcomes for the high-performance criteria and how they fulfill the requirements of the building. Three design alternatives are presented in the table below, in which the HPC results for each configuration can be compared.
The third option offers a bigger number of rooms per floor compared to the other two options, option 1 and option 2, having a number of rooms per floor of 14 and 12, respectively. In reference to the to the total WWR, different window sizes for the width part and length part of the building were set, considering some floor to ceiling windows and other smaller windows, taking into account that the total WWR should be within the 30% – 45% range. All three options lay within this range, being the second option with the highest WWR and the first option with the lower. Finally, in reference to the compactness of the building, the first and second option have an equal V/S ratio of 1.98. While, the third option offers the bigger number of rooms per floor, it has the highest compactness value of 2.12. This makes the third option the best alternative in terms of compactness by offering a building with less envelope surface area, this leading to less heat losses and a better energy performance of the building.
Based on these results, all of them are considered good alternatives, however the third design alternative offers a higher number of rooms fulfilling the needs of the high demand of student housing and having the highest compactness value of the three options presented with an acceptable WWR leading to a better thermal and energy performance, by reducing the heating load and by this the energy demand and costs of the operation of the building.
Preview of the Parametric Model
By using nodes from a dynamo package called ‘MeshToolkit’, the model in dynamo was exported into a .dae format. Then this file was uploaded to sketchfab. The resulting 3D representation is presented below.
Student Residence
by Ana GG
on Sketchfab
Download the Parametric Model
The ontology can be downloaded by clicking the download button bellow . Or right click on the ‘Download Parametric Model’ button and click on ‘Save link as …’ then save the dynamo file to your computer.
[1] “New Report: The Building and Construction Sector Can Reach Net Zero Carbon Emissions by 2050,” World Green Building Council, September 23, 23AD, https://www.worldgbc.org/news-media/WorldGBC-embodied-carbon-report-published#_ftn1.
[2] Ioan Petri et al., “Optimizing Energy Efficiency in Operating Built Environment Assets through Building Information Modeling: A Case Study,” Energies 10, no. 8 (August 8, 2017): 1167, https://doi.org/10.3390/en10081167.
[3] Gratia, E. & De Herde, A. (2002). Design of low energy office buildings. Energy and Buildings. 35, 473-491
[4] Francesco Goia, “Search for the Optimal Window-to-Wall Ratio in Office Buildings in Different European Climates and the Implications on Total Energy Saving Potential,” Solar Energy 132 (July 2016): 467–92, https://doi.org/10.1016/j.solener.2016.03.031.
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