 Analysis of two-phase injection heat pump using artificial neural network considering APF and LCCP under various weather conditionsDongwoo Kim, Kang Sub Song, Junyub Lim and Yongchan Kim Energy, 2018, vol. 155, issue C, 117-127 Abstract: 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. Keywords: Two-phase injection; Optimization; Annual performance; LCCP; Artificial neural network (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:155:y:2018:i:c:p:117-127 DOI: 10.1016/j.energy.2018.05.046 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
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