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1st International Conference on Data For Low Energy Buildings 2018
The conference Data for Low-Energy Buildings 2018 aims to facilitate the exchange and share of ideas in the fields of energy use in buildings and data science. The event will have representation from industry and academia and will cover the common ground of these two important disciplines. With this, it is intended to facilitate an interdisciplinary arena in which the participants will be able to discuss their most recent findings, the recent innovations, and the trends that this field of study has shown in the last years.
Buildings are responsible for up to 40% of the carbon emission in developed countries. The International Panel of Climate Change on its last report (2016) has informed that these carbon emissions are changing the climate, resulting on extremely pernicious phenomena for society. The reason for envisioning low-energy buildings is therefore twofold: Climate change needs to be decelerated via the reduction of emissions (mitigation) but at the same time, global warming and more frequent and severe extreme events force us to design better buildings or to adapt those existing to the conditions that will be seen in the near future (adaptation).
The challenge of improving the building stock for the new scenarios is not trivial. More than 40,000 people died in Europe in 2003 due to bad internal conditions in their buildings. These “bad conditions” can be motivated for exogenous factors as it can be the future weather or by endogenous ones: for example, fuel poverty. The two families of factors that can contribute to the pernicious working of buildings have one thing in common, they are both complex factors to study from the point of view of their prediction and analysis. Future weather events are difficult to treat, but also the effect of internal conditions on vulnerable people is a complex matter.
In conjunction with the problems of the current building stock, one can find that architects and engineers are finding difficult to create methods that are able to help with the design of low-energy but yet resilient buildings. This points to new opportunities also in the front of design tools.
The rather unpromising situation described above, is occurring at the same time as society is witnessing how information technology and data analysis are pushing a technological revolution. The amount of devices in current days able to capture data is massive, and the connections of human activities with the internet almost full. This new paradigm has in many cases considered as the era of the Internet of Things (IoT), or the era of the Big Data (BD).
With respect to buildings, it seems like the major enemy is uncertainty and variability. The well-known issue of the Performance Gap is making building designs that on paper seem to be low-energy and resilient underperform when built. There could be many factors that make this happen, but the main reasons point to uncertainty on the quality of workmanship, weather conditions, and occupants’ behaviour.
The scientific community is starting to realise the parallelism between the issue of low-energy robust building design and construction and the potential of current trends in data science. The large deal of uncertainty found when trying to find solutions for a better building stock is similar to the uncertainty that is found by data scientists that are currently making substantial progress on big data from social media, smart infrastructure or crowdsourcing. It is for this reason that one can already see initiatives centred on data analysis for the design of better buildings, the management of buildings, the study of weather conditions or the understanding of occupant behaviour.
This new data driven paradigm is opening a new avenue of research that not only is being rather fruitful in terms of scientific output, but also shown to be the stepping stone for the creation of a whole new sector in industry, that could result on large added value companies and the creation of highly specialised jobs.
THE ORGANISING COMMITTEE