Airconditioning systems accounts for the highest portion of energy consumption in buildings, either commercial or residential. Thus, there is a need for energy savings while ensuring internal thermal comfort. The compressor utilizes about 90% of the energy being consumed by the airconditioning system. Common practice in energy savings for airconditioning systems includes the application of variable frequency drive of the compressor for capacity control. However, literature on capacity control for operational centralized multi-circuit systems are scarce. This paper presents a study on the application of a thermal control system known as the Advanced Thermal Control System or ATCS utilizing variable frequency drive technology on an installed and operational multi-circuit centralized water-cooled packaged unit airconditioning system at an administration block in Universiti Teknologi Malaysia. The result shows energy savings of up to 33.0 % when compared to the baseline operation of the existing system.
The suitable application of innovative control strategies in Heating, Ventilation, and Air-conditioning systems is important to improving the energy efficiency and maintenance of temperature set point to improve thermal comfort in buildings. The increased focus on energy savings and appropriate thermal comfort has resulted in the necessity for more dynamic approach to the use of these controllers. However, the design of these controllers requires the use of an accurate dynamic modelling. Substantial progresses have been made in the past on model development to provide better control strategy to ensure energy savings without sacrificing thermal comfort and indoor air quality in the Heating, Ventilation, and Air-conditioning systems. However, there are scarce model using the data driven approach in the Multi-circuit air-conditioning system. This research, carried out a study on the choice of a dynamic model for an operating centralized multi-circuit water-cooled package unit air-conditioning system using a system identification procedure. Baseline data were collected and analyzed, the model development was achieved by processing, estimating and validating the data in system identification. Result shows that the Autoregressive-moving average with exogenous terms (ARMAX) of the third order model, established the best model structure with the highest Best Fit and Lowest Mean Square Error.
Temperature control is important in energy management of buildings. Air conditioning system contributes a high percentage of the total energy consumption, the compressor, which is a major component of the Air conditioning system, utilizes up to 90% of the energy. This can drastically be reduced by varying the frequency of the compressor with respect to the required indoor temperature, as such, reducing the overall energy usage of the air conditioning system. The combination of a well-tuned controller and variable frequency drive can be used to achieve this. It is important to develop a good model which can be used to design the controller. Although there are published research works in the development of models for the control of air conditioning systems, there seems to be a lack of study in the area of multi-circuit centralized air conditioning system. In this study, two models were developed using Long Short Term Memory Neural Network and Recurrent Neural Network, utilizing compressor speed and indoor air temperature of a multi-circuit water cooled packaged unit as input and output respectively. Comparing the two models, results shows that the Long Short-Term Memory Neural Network model performed better across evaluation metrics such as R-squared, Mean Squared Error and Mean Absolute Error, with the value of 0.9638, 0.0049, and 0.0190 respectively
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