Performance analysis and comparison of data-driven models for predicting indoor temperature in multi-zone commercial buildings

Borui Cui,Piljae Im,Mahabir Bhandari,Sangkeun Lee

Energy and Buildings（2023）

Cited 0|Views9

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Abstract

•The RC model served as a benchmark model with maximum temporal data available.•The RNN models were tested under scenarios with various input data and information.•Specific ranges were set for the selected important hyper-parameters of RNN models.•Feature importance analysis was conducted to select the key variables for RNN models.

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Key words

Building envelope thermal model,Machine learning,Time series data,Resistance-capacitance model,Recurrent neural network,Feature importance analysis

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