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Seasonal heating performance prediction of air-to-water heat pumps based on short-term dynamic monitoring.
- Source :
-
Renewable Energy: An International Journal . Dec2021, Vol. 180, p829-837. 9p. - Publication Year :
- 2021
-
Abstract
- Air-to-water heat pumps (AWHPs) utilize renewable energy and have found worldwide applications, with the seasonal coefficient of performance (SCOP) as a key index. Considering reliability and costs, short-term dynamic monitoring combined with regression analysis and extrapolation is used for predicting SCOP. Different regression models are being researched. A statistical analysis method is proposed to work out the optimal scheme. A series of prediction models with different independent variables, fitting methods and training dataset acquisition methods are discussed. Two indicators, the qualification rate R ±10% and the maximum relative error E max , are proposed for accuracy evaluation. For analysis a typical AWHP heating system in Beijing was monitored for 78d. Linear fitting performs better than quadratic polynomial fitting. For the consecutive-day method, the prediction deviation decreases with a longer test time and presents diminishing marginal benefits. A critical value of 10-day is identified and unrepresentative days should be avoided. For the typical-meteorological-day method, three days with an outdoor air temperature (T out) range covering over 50% days of the heating season and including the average T out of local winter are recommended. Satisfactory prediction results are realized, with R ±10% >97% and E max <14%, while using T out or the difference between water temperature and T out presents consistent accuracy. • Multiple regression models for evaluating AWHP's SCOP are reviewed. • A field test of a practical AWHP heating system is conducted with 78-day data. • A statistical analysis method is proposed to work out the optimal scheme. • Independent variable, fitting method and dataset acquisition method are discussed. • Typical-day data using linear fitting with Tout is feasible for prediction. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09601481
- Volume :
- 180
- Database :
- Academic Search Index
- Journal :
- Renewable Energy: An International Journal
- Publication Type :
- Academic Journal
- Accession number :
- 153031235
- Full Text :
- https://doi.org/10.1016/j.renene.2021.08.130