2020/01/31 by Evan Baker, Peter Challenor, Matt Eames
Engineering · Mathematics · Physics and Astronomy · #Building Energy and Comfort Optimization #Building energy simulation #Calibration #Efficient energy use #Matching (statistics) #Model Reduction and Neural Networks #Model building #Overheating (electricity) #Reservoir Engineering and Simulation Methods #Set (abstract data type) #stat.AP
paper · pdf · doi:10.1111/rssc.12461
19 pages, 3 figures
openalex created_date 2020/01/23 · openalex publication_date 2020/12/19 · arxiv created 2020/12/21 · arxiv updated 2020/12/22 · openalex updated_date 2026/08/05
Abstract 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).