Experimental performance analysis of a multiple-source and multiple-use heat pump system: A predictive ANN model of sky-source heat pump

Ke Wen*, Ryozo Ooka, Toshiyuki Hino, Mingzhe Liu, Doyun Lee, Wonjun Choi, Shintaro Ikeda, Djafar Reza Palasz

*Corresponding author for this work

    Research output: Contribution to journalConference articleResearchpeer-review

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    Abstract

    In this study, an artificial neural network (ANN) was used to model the thermal performance of a novel direct-expansion solar-assisted sky-source heat pump (SSHP) during winter. The input parameters of the ANN take into account the weather conditions, water loop characteristics, and the compressor characteristics of the SSHP. The following four output parameters were adopted to evaluate the SSHP performance: the outlet water temperature of the water loop, electricity consumption, heat production, and the coefficient of performance. To increase the accuracy of the ANN and simultaneously investigate the effects of each of the input parameters on the performance of the SSHP, the combination of input parameters for the validation data set was varied in multiple case studies. Additionally, learning curves were introduced to clarify the relationship between the training data size and the generalization performance of the ANN. Finally, the ANNs with the best performance were selected and evaluated based on the test data set by using metrics such as the root mean square error. The reported results demonstrated that the ANN model has comparatively high SSHP winter performance prediction accuracy.
    Original languageEnglish
    Article number05018
    JournalE3S Web of Conferences
    Volume111
    Number of pages8
    ISSN2267-1242
    DOIs
    Publication statusPublished - 2019
    EventClima 2019: 13th REHVA World Congress - Bucharest, Romania
    Duration: 26 May 201929 May 2019
    Conference number: 13

    Conference

    ConferenceClima 2019: 13th REHVA World Congress
    Number13
    Country/TerritoryRomania
    CityBucharest
    Period26/05/201929/05/2019

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