Updated: May 14, 2020
How do you design a sustainable building that is both energy-efficient and comfortable? The passive design of a sustainable building varies from building to building. Every building has its own geometry, its own location, its own local climate and context, and its own function. These factors all influence a building’s energy consumption and indoor environment. The design details of a comfortable and energy-efficient building for one structure might not work for another. How do you know if the building’s passive design will both be energy-efficient and have a comfortable indoor environment?
The use of building simulations can help guide the design of a building. By using simulations you can input the design details and test beforehand how the passive design will perform under a unique set of circumstances.This method enables us to answer questions such as: Will the indoor climate comfortable? Will it not become too hot in summer or too cold in winter? How much energy will this building use for heating and for cooling? How much will this passive design cost? Using simulations also allows you to combine the building costs and energy costs with the performance in comfort and energy efficiency.
The traditional method of design analysis is called simulation analysis. This can be used to analyse one situation or to perform multiple analyses to get an optimal sustainable design solution for a specific design request/problem. For example; what should the length of an overhang at a large sun-orientated window be in order for it to shade the window in summer, but allow sun in in winter? Through this method of design analysis, you can test different overhang lengths to find out which composition is ideal, whilst also taking into consideration the construction costs, energy consumption and the indoor climate. Unfortunately, as soon as something in the design assumptions used for the input changes, then this entire study will have to be done again. For the example of the overhang, the energy engineer may have already tested the design, and suggests that for an optimised sustainable design solution, double glazing should be used. He then learns in the design meeting that the owner has made a good deal and has bought triple-glazed glass for the building. This has a significant influence on the performance of the building and the overhang design. So the energy engineer has to start his analysis all over again, which wastes a lot of time and money, and thus delays the design process. He goes back to his office and only returns two weeks later with the input that the architect needs to proceed with the design.
Parametric design techniques respond better to the wishes of the design team. In a parametric design many, many designs are analysed simultaneously, instead of starting from scratch every time a requirement changes. By analysing an enormous number of designs, it is possible to achieve the optimal results for varying situations. And by doing so, you are able to better align with the wishes of the client and the design team, in a time- and cost-efficient manner.
To better illustrate the benefits of parametric analysis, consider the case of a luxury home built in Stellenbosch, South Africa. To deal with the vast amounts of designs and their results, we use interactive charts (of which screenshots are shown in this article). The graphs consist of a number of components: the blue lines are the different models and their results – every blue line is one simulation result; and the vertical black lines are axes, on which the values of designs are given. The axis to the left provides design input values, such as glazing type (single-tinted glazing, double glazing, single clear glazing) and insulation thicknesses (in mm) for roof, wall and floor. The other axes show the performance results. In this case, they display how big should the air-conditioning system be; how much energy will be needed for air-conditioning; how much the insulation and the glazing will cost; as well as the energy costs for heating and cooling.
Chart showing all iterations of the design simulated.
This graph shows that you can apply filters to look at designs separately. One could filter for a situation that would be specific to the South African context, for example a European client building a house in South Africa, who is used to building standards with thick insulation, wants a very well-insulated building. So in the graph, the design is filtered to use double glazing and thick insulation for the roof, walls and floor. In the output section of this design, you see the result as follows: Heating capacity required = 5.6 kW; the cooling capacity required =15.9 kW; material costs = R340,190, and annual energy costs = R5,960.
Thickly insulated design with double glazing.
For illustration, it is now possible to compare this with a local more standard approach, such as the one applied to the case of the luxury building in Stellenbosch. In this design, single glazing is used and the only insulation is insulation applied to the roof (130 mm) and not to the floor and walls. In the results, you can see an increase in the required heating capacity (to 8.5 kW) and a minor decrease in required cooling capacity (15.4 kW). But the material costs drop significantly to R156,410 and the annual energy costs are reduced by 9% to R5,480.
Local standard – 130 mm roof insulation and single clear glazing.
It is also possible to use the interactive graph to reverse the study of the analysed models. In other words, you can filter the output. So you could filter the results and see if it is possible to sustainably design the building such that it has lower material costs and is even more energy-efficient. If you filter these results, you will see that in this case, that you can save money and energy by using double glazing and only use a little bit of insulation (40 mm) in the roof. The material costs drop slightly to R151,130 and the annual energy costs drop another 8% to R5,080.
A more cost-effective design.
But during the design process, there are always other issues that emerge. The following scenario could happen in the design meeting: the customer announces that he wants to use a pellet burner for the heating of the building, so he has a lot of heat at his disposal. But he does not want to cool. If you now include this in the design, you can filter the results to arrive at a different solution as shown below. By going for a minimum required cooling energy, the warm days in the building will be limited. This results in a design with single-tinted glazing, 200 mm roof insulation and 30 mm of wall insulation. The material costs will be R263,260, which is more expensive than the standard design but is still 23% cheaper than the original well-insulated design. The energy usage will be R5,170 – which is 13% less than in the well-insulated design.
Using heating but no cooling.
And again, during the design meeting, another surprise arises: even though you have specified that single-tinted glazing should be used, the client informs you that he has already ordered the double glazing for the house and that it is already being made. So again, you can feed this information into the interactive graph, and another design solution will be displayed. In this case, the envelope costs and the amount of uncomfortable warm days increase compared to the last model, but the energy usage goes down. And in the end, the design is still 16% cheaper in material costs and uses 27% less energy than the original well-insulated design.
Using double glazing, heating and no cooling.
The parametric analysis proves to be a highly effective tool for affordable, sustainable building design. It accelerates the design process by allowing you to consider different scenarios in one go instead of having to restart your analysis every time something changes. The parametric approach enables you to act quickly and to deliver sustainable, energy-efficient and comfortable building designs.
Please note the values as shown here are purely illustrative, and that they are specific to this example building with its own unique design. The values indicated cannot be reused for another building design – this may produce dissimilar results where the energy usage for heating and cooling goes up significantly. In this example, the system design and costs are not included in the equation. The impact of the inclusion of the low-tech system design will be illustrated in the next article.