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Atmospheric Sciences

greenhouse gas

How can we use meteorological models to improve building energy simulations?

How can we use meteorological models to improve building energy simulations?

Climate change is calling for various and multiple approaches in the adaptation of cities and mitigation of the coming changes. Because buildings (residential and commercial) are responsible of about 40% of energy consumption, it is necessary to build more energy efficient ones, to decrease their contribution to greenhouse gas emissions.

But what is the relation with the atmosphere. It is two folds: firstly, in a previous post, I have already described what is the impact of the buildings / obstacles on the air flow and on the air temperature. Secondly, the fact that the climate or surrounding environment is influenced, there will be a significant change in the energy consumption of these buildings.  Currently, building energy simulation tool are using data usually gathered outside of the city and hence not representative of the local context. Thus it is crucial to be able to have necessary tools that capture both the dynamics of the atmosphere and also those of a building to design better and more sustainable urban areas.

In the present work, we have brought these two disciplines together by developing a multi-scale model. On the one side, a meteorological model, the Canopy Interface Model (CIM), was developed to obtain high resolution vertical profile of wind speed, direction and air temperature. On the other hand, an energy modelling tool, CitySim, is used to evaluate the energy use of the buildings as well as the irradiation reaching the buildings.

With this coupling methodology setup we have applied it on the EPFL campus, in Switzerland.  We have compared the modelling results with data collected on the EPFL campus for the year 2015. The results show that the coupling lead to a computation of the meteorological variables that are in very good agreement. However, we noted that for the wind speed at 2m, there is still some underestimation of the wind speed. One of the reason for this is that the wind speed close to the ground is very low and there is a higher variability at this height.

Comparison of the wind speed (left) and air temperature (right) at 2m (top) and 12m (bottom).

We intend to improve this by developing new parameterization in the future for the wind speed in an urban context by using data currently being acquired in the framework of the MoTUS project. One surprising result from this part of the study, is the appearance inside of an urban setup of a phenomena call Cold Air Pools which is very typical of valleys. The reason for this is the lack of irradiation reaching the surface inside of dense urban parts.

Furthermore, we have seen some interesting behaviour in the campus for some particular buildings such as the Rolex Learning Center. Buildings with different forms and configuration, reacted very differently with the local and standard dataset. We designed a series of additional simulation using multiple building configuration and conducted a sensitivity analysis in order to define which parameters between the wind speed and the air temperature had a more significant impact on the heating demand (see Figure 1). We showed that the impact of a reduction of 1°C was more important than a reduction of 1m s-1.

Figure 1. Heating demand of the five selected urban configurations (black dots), as function of the variation by +1°C (red dots) and -1°C (blue dots) of the air temperature, and by +1.5 m s-1 (violet dots) and -1.5 m s-1 (orange dots).

Finally, we also analysed the energy consumption of the whole EPFL campus. When using standard data, the difference between the simulated and measured demand was around 15%. If localized weather data was used, the difference was decreased to 8%. We have thus been able to reduce the uncertainty of the data by 2. The use of local data can hence improve the estimation of building energy use and will hence be quite important when building become more and more efficient.

Reference / datasets

The paper (Mauree et al., Multi-scale modelling to evaluate building energy consumption at the neighbourhood scale, PLOS One, 2017) can be accessed here and data needed to reproduce the experiment are also available on Zenodo.

An unlikely choice between a gasoline or diesel car…

An unlikely choice between a gasoline or diesel car…

I have recently been confronted with the choice of buying a “new” car and this has proved to be a very tedious task with all the diversity of car that exists on the market today. However, one of my primary concerns was, of course, to find the least polluting car based on my usage (roughly 15000km/year).

Cars (or I should say motor vehicles) pollution is one of the major sources of air pollution (particulate matter, soot, NOx, …) in urban areas. These often cause, during both winter and summer seasons, long and prolonged exposition to ozone or PMs which can have significant effect on the health of urban population. Besides, vehicles are also one of the most important sources of greenhouse gases emissions (around 30%). Extensive research in various areas (air pollution and monitoring in urban areas, efficiency of motor vehicles, mobility and public transportation, urban planning,…) are thus being conducted to help reduce the exposition to dangerous pollutants and emissions of GHG.

Manufacturers have been more and more constrained by new regulations to decrease the pollutant emissions (with EURO6 norm now in the EU) and the increase the efficiency of motor vehicles. Governments around the world and more particularly in Europe, after the financial crisis of 2007/2008 have introduced new subsidies to incite people to buy new more energy efficient vehicles. One of the main issues here is that often the more efficient vehicles are not necessarily the less polluting vehicles. Policies have been based on GHG emissions from vehicle consumption without consideration of the full life cycle cost and analysis and also on other pollutants emissions.

Thus if we take for example an electric car, the GHG emissions (and also other pollutants) are pretty low or close to zero as there are none released by the car itself. But we also need to evaluate the emissions from the electricity power plant (most likely to be a centralized one based on either fossil fuel or nuclear energy). Furthermore if the life cycle cost of the battery in such cars, are taken into consideration, the picture is not so black and white anymore as it has been pointed out by numerous studies (ADEME – sorry for the French link!). Besides electric vehicle remain quite expensive and not really adapted to all usage.

If we compare both diesel and gasoline cars, then it becomes a bit more tedious. Diesel engines consumes generally less than gasoline one. However their PM emissions, for example, can be quite high and hence they need really efficient filters to get rid of these pollutants. More stringent regulations have forced manufacturers to improve significantly the quality of the air coming out of their diesel engines but still remain on average more polluting than gasoline cars. Countries, like France, that have strongly subsidized the use of diesel in the past, are now finding it quite difficult to phase out these types of cars. And besides they are more efficient and hence emits lets GHG.

Coming back to my choice of cars then… The choice for me in the end was then between the long term or short term benefits. Using a gasoline car or an electric car (in a country where the energy is coming from renewables!) would be more ecologically sound if we drive mostly in urban areas. However if you are thinking about the long term benefits (with climate change) then you should probably opt for a more efficient diesel car.

All of this, points out that research still need to be conducted and new innovative ideas are really needed (like Elon Musk’s battery, maybe?) so as to bridge the enormous gap between having an efficient car, the life cycle analysis and living in a pollution-free urban environment. But of course…, the best solution is to use public transport or bikes… well this is not always possible!

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