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Research activity at Architecture explores a wide variety of significant research areas within architecture and the built environment. Among these is the better exploitation of innovative construction technologies and ICT to optimise 'total building performance', as well as reduce waste and environmental impact. Sustainable architectural and urban design is an important component of this. To this end, the Cluster for Research in Design and Sustainability (CRiDS) focuses its research energies towards developing resilient responses to the social, environmental and economic challenges associated with urbanism and cities, in both the developed and developing world.

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Machine learning for estimation of building energy consumption and performance : a review

Seyedzadeh, Saleh and Pour Rahimian, Farzad and Glesk, Ivan and Roper, Marc (2018) Machine learning for estimation of building energy consumption and performance : a review. Visualization in Engineering, 6 (5). ISSN 2213-7459

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Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance.