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Autoregressive neural networks with exogenous variables for indoor temperature prediction in buildings

Building Simulation 2021年第1期14卷 165-178页

作者： Benoit Delcroix Jérôme Le Ny Michel Bernier Muhammad Azam Bingrui Qu Jean-Simon Venne Department of Mechanical Engineering Polytechnique Montréal2500 Chemin de PolytechniqueMontréalQCH3T 1J4Canada Department of Electrical Engineering and GERAD Polytechnique Montréal2500 Chemin de PolytechniqueMontréalQCH3T 1J4Canada Smart Building Lab BrainBox AIMontréalQCCanada

Thermal models of buildings are helpful to forecast their energy use and to enhance the control of their mechanical ***,these models are building-specific and require a tedious,error-prone and time-consuming development effort relying on skilled building energy *** to white-box and gray-box models,data-driven(black-box)models require less development time and a minimal amount of information about the building *** this paper,autoregressive neural network models are compared to gray-box and black-box linear models to simulate indoor *** models are trained,validated and compared to actual experimental data obtained for an existing commercial building in Montreal(QC,Canada)equipped with roof top units for air *** show that neural networks mimic more accurately the thermal behavior of the building when limited information is available,compared to gray-box and black-box linear *** gray-box model does not perform adequately due to its under-parameterized nature,while the linear models cannot capture non-linear phenomena such as radiative heat transfer and ***,the neural network models outperform the alternative models in the presented application,reaching a coefficient of determination R2 up to 0.824 and a root mean square error down to 1.11℃,including the error propagation over time for a 1-week period with a 5-minute *** considering a 50-hour time horizon,the best neural networks reach a much lower root mean square error of around 0.6℃,which is suitable for applications such as model predictive control.

关键词： thermal model building simulation experimental validation multi-layer perceptron linear vs.non-linear black-box vs.gray-box

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