Performance evaluation of ground source heat pump using linear and nonlinear regressions and artificial neural networks
• An experimental database containing influential parameters and heat transfer capacity was compiled using literature-based data collection and on-site measurement. • Linear and nonlinear regression models were adopted to evaluate the heat transfer performance of GSHP. • BP neural networks were empl...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Xu, Xinjie [verfasserIn] |
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| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Wind resource mapping and energy estimation in complex terrain: A framework based on field observations and computational fluid dynamics - Radünz, William Corrêa ELSEVIER, 2020, design, processes, equipment, economics, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:180 ; year:2020 ; day:5 ; month:11 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.applthermaleng.2020.115914 |
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| 520 | |a • An experimental database containing influential parameters and heat transfer capacity was compiled using literature-based data collection and on-site measurement. • Linear and nonlinear regression models were adopted to evaluate the heat transfer performance of GSHP. • BP neural networks were employed to develop a prediction model for evaluation of heat transfer capacity of GSHP. • A parametric analysis using the trained ANN model was conducted to predict the trend of heat transfer capacity as a function of each parameter. | ||
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Performance evaluation of ground source heat pump using linear and nonlinear regressions and artificial neural networks |
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• An experimental database containing influential parameters and heat transfer capacity was compiled using literature-based data collection and on-site measurement. • Linear and nonlinear regression models were adopted to evaluate the heat transfer performance of GSHP. • BP neural networks were employed to develop a prediction model for evaluation of heat transfer capacity of GSHP. • A parametric analysis using the trained ANN model was conducted to predict the trend of heat transfer capacity as a function of each parameter. |
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• An experimental database containing influential parameters and heat transfer capacity was compiled using literature-based data collection and on-site measurement. • Linear and nonlinear regression models were adopted to evaluate the heat transfer performance of GSHP. • BP neural networks were employed to develop a prediction model for evaluation of heat transfer capacity of GSHP. • A parametric analysis using the trained ANN model was conducted to predict the trend of heat transfer capacity as a function of each parameter. |
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• An experimental database containing influential parameters and heat transfer capacity was compiled using literature-based data collection and on-site measurement. • Linear and nonlinear regression models were adopted to evaluate the heat transfer performance of GSHP. • BP neural networks were employed to develop a prediction model for evaluation of heat transfer capacity of GSHP. • A parametric analysis using the trained ANN model was conducted to predict the trend of heat transfer capacity as a function of each parameter. |
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