Dissimilarity-based boosting technique for the modelling of complex HVAC systems

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Article number111151
Journal / PublicationEnergy and Buildings
Online published31 May 2021
Publication statusPublished - 15 Sep 2021


The decision variables of local control loops significantly influence the overall energy use of the heating ventilation and air-conditioning (HVAC) systems. Therefore, model-based real-time optimization (MRTO) of those decision variables has been widely studied in recent years. The decision making by MRTO relies on the accuracy of the performance model used to describe the relationship between cost function and decision variables. However, due to high diversity in ambient conditions and demand load, it is very difficult to develop an accurate model of these systems without a downside. This paper presents a dissimilarity-based ensemble model of the HVAC system, which systematically ensembles two powerful modelling methods from the literature: simplified or semi-physical model; and artificial neural network model. A semi-physical model of the system was used as a generalized model to provide reliability in performance. A batch trained nonlinear-autoregressive neural network with exogenous input (NARX) was used as an artificial neural network model to provide robust and accurate performance. The ensemble technique proposed in this study is a dissimilarity-based boosting technique, i.e. able to capture the performance of the NARX model and ensure accuracy and reliability in final ensemble prediction. To evaluate its performance, it was tested for the performance prediction of a complex HVAC system under actual ambient conditions over a week and its performance was compared with both semi-physical and NARX models. It was demonstrated that the proposed dissimilarity-based ensemble model could provide improved accuracy with robust operation and reliability while maintaining reasonable computational efficiency. When compared with the semi-physical model, the proposed dissimilarity-based ensemble model provided; 60–78% reduction in prediction error in terms of normalized root-mean-square error, improvement in terms of peak signal-to-noise ratio by 55–63%, and consistent performance in terms of reduction in mean absolute percentage deviation by 51–75%.

Research Area(s)

  • Dissimilarity-based boosting, Heating ventilation and air-conditioning system, Model-based real-time optimization, Performance model