Modelling and experimental performance analysis of solar-assisted ground source heat pump system


Esen H., Esen M., Ozsolak O.

JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE, vol.29, no.1, pp.1-17, 2017 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 29 Issue: 1
  • Publication Date: 2017
  • Doi Number: 10.1080/0952813x.2015.1056242
  • Journal Name: JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.1-17
  • Keywords: Solar energy, ground source heat pump, artificial neural network, adaptive neuro-fuzzy inference system, ARTIFICIAL NEURAL-NETWORK, VERTICAL TEMPERATURE PROFILES, BOREHOLE RESISTANCE, THERMAL PERFORMANCE, EXCHANGERS, ENERGY, ANFIS, ANN

Abstract

In this study, slinky (the slinky-loop configuration is also known as the coiled loop or spiral loop of flexible plastic pipe)type ground heat exchanger (GHE) was established for a solar-assisted ground source heat pump system. System modelling is performed with the data obtained from the experiment. Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are used in modelling. The slinky pipes have been laid horizontally and vertically in a ditch. The system coefficient of performance (COPsys) and the heat pump coefficient of performance (COPhp) have been calculated as 2.88 and 3.55, respectively, at horizontal slinky-type GHE, while COPsys and COPhp were calculated as 2.34 and 2.91, respectively, at vertical slinky-type GHE. The obtained results showed that the ANFIS is more successful than that of ANN for forecasting performance of a solar ground source heat pump system.