Detection of the low ∆T syndrome using machine learning models

Anand Thamban, Alet Van Den Brink, Shalika Walker & Wim Zeiler
The low ∆T syndrome has been a prevalent issue in many chilled water systems, leading to an increase in the pump energy consumption, increase in the chiller energy consumption, and/or failure to meet the cooling loads. It is therefore important to detect the low ∆T syndrome using suitable fault detection and diagnosis methods. One such fault detection method is the data-based approach using machine learning algorithms. The main signs indicating the low ∆T syndrome include...
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