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Optimizing Execution of ACs

We proposed a dynamic Cooling-system Linear Scheduling Algorithm (CLA) to optimise energy consumption by ACs. We observed a maximum energy saving of up to 62% in real-world deployment.

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Identifying Fault and its Cause

We proposed a domain inspired anomaly detection approach which identifies the  instances of anomalous energy consumption by cooling system. Then, we identify the faulty part which cause the anomalous energy instance. We observed accuracy of up to 82% and energy savings of 46%.

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