Data-driven operation performance evaluation of multi-chiller system using self-organizing maps
Industrial plants performance evaluation has become a difficult task due to the machinery complexity. Multi-chiller systems take up big proportion of energy in food and beverage companies. Complex refrigeration generation is usually hard to evaluate as the affectation of external signals plays an important role and also exist too many control features for the facility operator. Develop a method able to detect any deviation respect the optimal operation can provide the necessary information for the purpose of inefficiencies identification and a further optimization. In this paper, data-driven methods are used in order to describe a reliable coefficient of performance indicator (COP) in several known plant conditions. Self-organizing maps (SOM) are used to recognize different operating points among the multi-variable feature space for later performance evaluation. By the analysis of COP in each operating point, the potential energy saving can be illustrated. An experimental study is performed with refrigeration plant indicating the suitability of the proposed method.