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The air conditioner is one of the devices that uses a high amount of electricity – more electricity consumption means more heat and greenhouse gases emitted to the environment if the electricity is generated by fossil fuel sources such as coal, diesel, natural gas etc. Energy-efficient control algorithms and strategies can be proposed to reduce the power consumption without sacrificing thermal comfort – time and cost can be saved by developing and testing these control algorithms and strategies via computer simulation instead of developing and testing them on the actual site, but this requires the availability of the mathematical model representing the dynamic behaviour of the system that is desired to be simulated. In this research, a black box model representing the dynamic indoor air temperature behaviour of the Industrial Instrumentation Laboratory at Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia (UTM) Kuala Lumpur is developed based on the continuoustime transfer function model using the System Identification ToolboxTM in MATLAB® software to get a new model with a simpler model structure for a more practical simulation than the autoregressive–moving-average (ARMA) model developed in the previous research representing the same behaviour without sacrificing a significant level of accuracy. Both the continuoustime transfer function model and the ARMA model are developed based on the actual recorded data from the laboratory and minimal physical characteristics knowledge of the laboratory. The result shows that the optimised continuous-time transfer function model generated by the System Identification ToolboxTM in this research has a significantly simpler model structure than the optimised ARMA model developed in the previous research (standardised eight poles and two zeros for all inputs versus standardised 41 past inputs and outputs for all inputs and output), and only slightly less accurate in terms of percentage of fitting, %Fit value (82.60% and 80.17% versus 87.45% and 80.36% for training and testing data set).

Black Box Modelling and Simulating the Dynamic Indoor Air Temperature of a Laboratory Using the Continuous-Time Transfer Function Model