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Performance prediction between horizontal and vertical source heat pump systems for greenhouse heating with the use of artificial neural networks

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Abstract

This paper presents the suitability of artificial neural networks (ANNs) to predict the performance and comparison between a horizontal and a vertical ground source heat pump system. Performance forecasting is the precondition for the optimal control and energy saving operation of heat pump systems. In this study, performance parameters such as air temperature entering condenser fan-coil unit, air temperature leaving condenser fan-coil unit, and ground temperatures (2 and 60 m) obtained experimental studies are input data; coefficient of performance of system (COPsys) is in output layer. The back propagation learning algorithm with three different variants such as Levenberg–Marguardt, Pola–Ribiere conjugate gradient, and scaled conjugate gradient, and also tangent sigmoid transfer function were used in the network so that the best approach can be found. The results showed that LM with three neurons in the hidden layer is the most suitable algorithm with maximum correlation coefficients R2 of 0.999, minimum root mean square RMS value and low coefficient variance COV. The reported results confirmed that the use of ANN for performance prediction of COPsys,H–V is acceptable in these studies.

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Abbreviations

ANN:

Artificial neural network

COPsys :

Heating coefficient of performance of ground-source heat pump system

CGP:

Pola–Ribiere conjugate gradient learning algorithm

cov:

Coefficient of variation (%)

LM:

Levenberg–Marquardt learning algorithm

n :

Number of independent data patterns

RMS:

Root-mean square error

R 2 :

Fraction of variance

SCG:

Scaled conjugate gradient learning algorithm

t:

Target

T o :

Outdoor air temperature (°C)

T i :

Indoor air temperature (°C)

T g1 :

Temperature of ground at 2 m depth (°C)

T g2 :

Temperature of ground at 60 m depth (°C)

T o,wa :

Outlet average water-antifreeze solution temperature of HGHE or VGHE (°C)

T i,wa :

Inlet average water-antifreeze solution temperature of HGHE or VGHE (°C)

T o,air :

Average air temperature leaving condenser fan-coil unit (°C)

T i,air :

Average air temperature entering condenser fan-coil unit (°C)

y :

Calculated neural network output

g 1 :

Ground at 2 m depth

g 2 :

Ground at 60 m depth

H:

Horizontal

HP:

Heat pump

i:

Inlet (inside)

o:

Outlet (outside)

sys:

System

V:

Vertical

mea:

Measured

pre:

Predicted

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Authors and Affiliations

  1. Department of Technical and Vocational Education, Fırat University, 23119, Elazig, Turkey

    Hüseyin Benli

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Benli, H. Performance prediction between horizontal and vertical source heat pump systems for greenhouse heating with the use of artificial neural networks. Heat Mass Transfer 52, 1707–1724 (2016). https://doi.org/10.1007/s00231-015-1723-z

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