Experimental implementation of artificial neural network for cost effective and non-intrusive performance estimation of air conditioning systems

Sholahudin*, Niccolo Giannetti, Seiichi Yamaguchi, Kiyoshi Saito, Yoichi Miyaoka, Katsuhiko Tanaka, Hiroto Ogami

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Owing to the high variability of operating conditions and the complexity of dynamic phenomena occurring within air conditioning cycles, the realistic performance estimation of these systems remains an open question in this field. This paper demonstrates the applicability of a cost-effective estimation method based on an artificial neural network exclusively using four refrigerant temperatures as the network input. The experimental datasets are collected from a reference experimental facility. The system is operated with variable cooling load, outdoor temperature, and indoor temperature settings, as representative of the actual operation. The artificial neural network structure was optimized by considering the effect of previous time step inputs, number of neurons, sampling time, and number of training data. The results reveal that the developed model can successfully estimate the cooling capacity of an air conditioning system during on–off, continuous unsteady, and steady operation, using four temperature inputs with relative averaged error below 5%.

Original languageEnglish
Article number115985
JournalApplied Thermal Engineering
Volume181
DOIs
Publication statusPublished - 2020 Nov 25

Keywords

  • Air conditioning
  • Artificial neural network
  • Cooling capacity
  • Performance estimation

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Industrial and Manufacturing Engineering

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