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Open AccessArticle

Minimising the Deviation between Predicted and Actual Building Performance via Use of Neural Networks and BIM

Faculty of Built Environment, UNSW Sydney, Sydney 2052, Australia
Buildings 2019, 9(5), 131; https://doi.org/10.3390/buildings9050131
Received: 8 April 2019 / Revised: 6 May 2019 / Accepted: 8 May 2019 / Published: 23 May 2019
(This article belongs to the Special Issue Innovative Measurement Techniques in Buildings)
Building energy performance tools are widely used to simulate the expected energy consumption of a given building during the operation phase of its life cycle. Deviations between predicted and actual energy consumptions have however been reported as a major limiting factor to the tools adopted in the literature. A significant reason highlighted as greatly influencing the difference in energy performance is related to the occupant behaviour of the building. To enhance the effectiveness of building energy performance tools, this study proposes a method which integrates Building Information Modelling (BIM) with artificial neural network model for limiting the deviation between predicted and actual energy consumption rates. Through training a deep neural network for predicting occupant behaviour that reflects the actual performance of the building under examination, accurate BIM representations are produced which are validated via energy simulations. The proposed method is applied to a realistic case study, which highlights significant improvements when contrasted with a static simulation that does not account for changes in occupant behaviour. View Full-Text
Keywords: building performance; energy predictions; artificial neural networks; BIM building performance; energy predictions; artificial neural networks; BIM
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Hammad, A.W. Minimising the Deviation between Predicted and Actual Building Performance via Use of Neural Networks and BIM. Buildings 2019, 9, 131.

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