Air conditioning system is a complex system and consumes the most energy in a building. Any fault in the system operation such as cooling tower fan faulty, compressor failure, damper stuck, etc. could lead to energy wastage and reduction in the system’s coefficient of performance (COP). Due to the complexity of the air conditioning system, detecting those faults is hard as it requires exhaustive inspections. This paper consists of two parts; i) to investigate the impact of different faults related to the air conditioning system on COP and ii) to analyse the performances of machine learning algorithms to classify those faults. Three supervised learning classifier models were developed, which were deep learning, support vector machine (SVM) and multi-layer perceptron (MLP). The performances of each classifier were investigated in terms of six different classes of faults. Results showed that different faults give different negative impacts on the COP. Also, the three supervised learning classifier models able to classify all faults for more than 94%, and MLP produced the highest accuracy and precision among all.

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Fuzzy logic controller has been proven to control nonlinear process system and HVAC is a type of nonlinear process systems. This paper studies the performance of fuzzy logic controller with three and five term membership function in centralized chilled water system. Three different cases are simulated and analyzed for both type of controllers. Results show that the performances between both controllers are almost similar with no significant difference. It is also encountered that in certain cases, 3-mf fuzzy logic controller outperformed 5-mf fuzzy logic controller.