A hybrid machine learning approach integrating recurrent neural networks with subspace identification for modelling HVAC systems. (16th March 2022)

• Record Type: Journal Article
• Authors: Hassanpour, Hesam; Mhaskar, Prashant; Risbeck, Michael J.
• Is Part Of: Canadian journal of chemical engineering. Volume 100:Number 12(2022)
• Page Start: 3620
• Page End: 3634
• Publication Date: 2022-03-16
• Subjects: HVAC systems -- recurrent neural networks -- subspace identification -- system identification; Chemical engineering -- Periodicals; Technology -- Periodicals; 660.05
• DOI: 10.1002/cjce.24392
• Languages: English
• ISSNs: 0008-4034

Abstract

Abstract: This paper addresses the problem of system identification for heating, ventilation, and air conditioning (HVAC) systems using a relatively small amount of data for the zone under consideration, by leveraging larger datasets for similar zones. To this end, a hybrid machine learning approach is developed where a pre‐trained recurrent neural network (RNN) model, trained on a large amount of data from a representative zone, is leveraged to build models for the other zones using a smaller amount of data. This is achieved by developing a hybrid model that integrates the pre‐trained RNN model with the models built using the subspace identification (SubID) technique to predict the residuals (differences between the real outputs and the predicted outputs from the pre‐trained RNN model) in the other zones. The effectiveness of the proposed hybrid approach is shown using real data collected from a multi‐zone fitness centre. The results demonstrate the superior performance of the hybrid approach over the cases where individual RNN and SubID models are directly developed using only the data from the zones in question.