Each week, we select a recently published Open Access article to feature. This week’s article comes from Journal of the Royal Statistical Society Series C (Applied Statistics) and considers future proofing a building design using history matching inspired level‐set techniques.
The article’s abstract is given below, with the full article available to read here.
Baker, E., Challenor, P. and Eames, M. (2020), Future proofing a building design using history matching inspired level‐set techniques. J R Stat Soc Series C. https://doi.org/10.1111/rssc.12461
How can one design a building that will be sufficiently protected against overheating and sufficiently energy efficient, whilst considering the expected increases in temperature due to climate change? We successfully manage to address this question—greatly reducing a large set of initial candidate building designs down to a small set of acceptable buildings. We do this using a complex computer model, statistical models of said computer model (emulators), and a modification to the history matching calibration technique. This modification tackles the problem of level‐set estimation (rather than calibration), where the goal is to find input settings which lead to the simulated output being below some threshold. The entire procedure allows us to present a practitioner with a set of acceptable building designs, with the final design chosen based on other requirements (subjective or otherwise).