Seasonal heating performance prediction of air-to-water heat pumps based on short-term dynamic monitoring.

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]