E3S Web Conf.
Volume 111, 2019CLIMA 2019 Congress
|Number of page(s)||8|
|Section||Information and Communication Technologies (ICT) for the Intelligent Building Management|
|Published online||13 August 2019|
Experimental performance analysis of a multiple-source and multiple-use heat pump system: a predictive ANN model of sky-source heat pump
1 Graduate School of Engineering, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505, Japan
2 Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505, Japan
3 Tokyo University of Science, 6-3-1, Niijuku, Katsushika-ku, Tokyo 125-8585, Japan
4 Technical University of Denmark, Anker Engelunds Vej 1 Bygning 101A, 2800 Kgs. Lyngby, Denmark
* Corresponding author: firstname.lastname@example.org
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.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.