TY - JOUR
T1 - Analysis of two-phase injection heat pump using artificial neural network considering APF and LCCP under various weather conditions
AU - Kim, Dongwoo
AU - Song, Kang Sub
AU - Lim, Junyub
AU - Kim, Yongchan
N1 - Funding Information:
This work was supported by the Industrial Core Technology Development Program (No. 10049090 ) of the Korea Evaluation Institute of Industrial Technology (KEIT) funded by the Korea Government Ministry of Trade, Industry and Energy .
Funding Information:
This work was supported by the Industrial Core Technology Development Program (No. 10049090) of the Korea Evaluation Institute of Industrial Technology (KEIT) funded by the Korea Government Ministry of Trade, Industry and Energy.
Publisher Copyright:
© 2018 Elsevier Ltd
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/7/15
Y1 - 2018/7/15
N2 - The objective of this study is to optimize the performance of a two-phase injection (TPI) heat pump considering annual performance factor (APF) and life cycle climate performance (LCCP). The performances of non-injection (NI), vapor injection (VI), and TPI heat pumps are measured under various outdoor temperatures. Based on the measured data, artificial neural network models for the NI, VI, and TPI heat pumps are developed to predict the performance indexes during cooling and heating seasons. As a result, the TPI heat pump shows higher heating capacity than the NI and VI heat pumps with a lower compressor discharge temperature in cold weather conditions. Therefore, the application of the TPI has a merit on reducing the size of the heat pump due to its lower back-up heater loss and over-capacity penalty. When the objective function maximizes the APF for system optimization in three climate regions, the TPI heat pump shows a 1.4–2.7% higher APF than the NI heat pump, and a 11.1%–18.1% smaller optimum rated heating capacity.
AB - The objective of this study is to optimize the performance of a two-phase injection (TPI) heat pump considering annual performance factor (APF) and life cycle climate performance (LCCP). The performances of non-injection (NI), vapor injection (VI), and TPI heat pumps are measured under various outdoor temperatures. Based on the measured data, artificial neural network models for the NI, VI, and TPI heat pumps are developed to predict the performance indexes during cooling and heating seasons. As a result, the TPI heat pump shows higher heating capacity than the NI and VI heat pumps with a lower compressor discharge temperature in cold weather conditions. Therefore, the application of the TPI has a merit on reducing the size of the heat pump due to its lower back-up heater loss and over-capacity penalty. When the objective function maximizes the APF for system optimization in three climate regions, the TPI heat pump shows a 1.4–2.7% higher APF than the NI heat pump, and a 11.1%–18.1% smaller optimum rated heating capacity.
KW - Annual performance
KW - Artificial neural network
KW - LCCP
KW - Optimization
KW - Two-phase injection
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U2 - 10.1016/j.energy.2018.05.046
DO - 10.1016/j.energy.2018.05.046
M3 - Article
AN - SCOPUS:85047964445
SN - 0360-5442
VL - 155
SP - 117
EP - 127
JO - Energy
JF - Energy
ER -