Surrogate modelling of building energy use

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Surrogate modelling is emerging as a revolutionary means of estimating energy use for building designs, which will be essential in delivering the low-energy buildings required to help prevent catastrophic climate change. New building designs and retrofits to old buildings must achieve very low levels of energy use to reduce the ~1/3 of carbon emissions currently caused by the building sector. This requires careful balancing of different aspects of performance, e.g. minimising winter heating demand versus summer cooling demand or overheating risk. Energy simulation programs that numerically calculate heat transfer in a proposed building design and thus its energy requirements are currently widely used in industry. However, the computational time required (several minutes per design option) is not conducive to the intuitive exploration of the performance of many different designs. Surrogate modelling performs many simulations spanning the design space of possible building designs, then fits a machine learning model to the resulting synthetic data that maps design variables to performance metrics. These surrogate models are approximate, but have been shown to represent the underlying system with great accuracy. Surrogate models are almost instant to evaluate, allowing designers to explore building performance trade-offs in a more natural manner using interactive apps in which visual performance outputs respond instantly to changes in inputs. The scientific approaches required to improve surrogate modelling span traditional building energy simulations and machine learning methods. The former covers the fundamental physics that governs energy flows in buildings, the computational means of simulating them, and the software methods to vary key parameters automatically. The machine learning techniques necessary for surrogate modelling include the fundamentals of neural networks, the advanced methods needed to capture the complexities of the underlying system, and the statistical techniques required to assess performance. The objective is to develop and refine surrogate modelling techniques that will advance this nascent field in bold new directions. This will involve both the machine learning techniques to be used for such models and their application to specific fields of modelling. The outcomes will have great significance in both short-term benefits and in the potential to revolutionize the field of building energy simulation. The models and methods produced are immediately applicable, as demonstrated by the industry collaborations already applying initial results. In the longer term, surrogate modelling could replace building energy simulation in early-stage design exploration, where instant evaluation is more useful than absolute accuracy. There will also be benefits in the training of highly-qualified personnel who possess a unique skillset spanning existing simulation approaches and emerging machine learning techniques.