A new type of microchannel condenser applied in the air source heat pump water heater(ASHPWH)with cyclic heating was proposed in this study.The operating performance of the ASHPWH was frst tested.Then,the structure of...A new type of microchannel condenser applied in the air source heat pump water heater(ASHPWH)with cyclic heating was proposed in this study.The operating performance of the ASHPWH was frst tested.Then,the structure of the microchannel condenser was optimized with the implement of vortex generators.Finally,a numerical model of the ASHPWH was established and the optimized microchannel condenser was studied.The experimental results showed that the average coefficient of performance(COP)of the 1HP(735 W)ASHPWH reached 3.48.In addition,the optimized microchannel condenser could be matched with a 3 HP(2430W)ASHPWH with an average heating capacity of 10.30 kW,and achieving an average COP of 4.24,14.6%higher than the limit value in the national standard.展开更多
The studies on predicting the energy consumption of air conditioning systems are meaningful to building energy conservation and management. Generally, the more comprehensive the building information is, the easier the...The studies on predicting the energy consumption of air conditioning systems are meaningful to building energy conservation and management. Generally, the more comprehensive the building information is, the easier the prediction model can be developed. However, it is very difficult to get detailed information about existing/old buildings (information-poor buildings), it is a big challenge to predict the energy consumption accurately by limited information. This study aims to predict the electricity consumption of the water source heat pump system of an office building based on meteorological data. The key variables are selected by error analysis and sensitivity analysis, and the effects of each variable on the models’ prediction performance can be obtained. Besides, the prediction models are established by support vector regression algorithm and trained by the local meteorological data. The results show that the positive and negative variables can be identified, and these positive variables are responsible for more than 70% of the total importance. Moreover, the root mean square error falls to 4.6044 from 7.8227 and the relative square error falls to 0.1494 from 0.4313 when the negative inputs are removed. And the errors reduce further to 4.1160 and 0.1194 by parameter optimization.展开更多
基金the National Natural Science Foundation of China(No.51776117)。
文摘A new type of microchannel condenser applied in the air source heat pump water heater(ASHPWH)with cyclic heating was proposed in this study.The operating performance of the ASHPWH was frst tested.Then,the structure of the microchannel condenser was optimized with the implement of vortex generators.Finally,a numerical model of the ASHPWH was established and the optimized microchannel condenser was studied.The experimental results showed that the average coefficient of performance(COP)of the 1HP(735 W)ASHPWH reached 3.48.In addition,the optimized microchannel condenser could be matched with a 3 HP(2430W)ASHPWH with an average heating capacity of 10.30 kW,and achieving an average COP of 4.24,14.6%higher than the limit value in the national standard.
基金This work was supported by the National Natural Science Foundation of China(No.51876070,No.51576074).
文摘The studies on predicting the energy consumption of air conditioning systems are meaningful to building energy conservation and management. Generally, the more comprehensive the building information is, the easier the prediction model can be developed. However, it is very difficult to get detailed information about existing/old buildings (information-poor buildings), it is a big challenge to predict the energy consumption accurately by limited information. This study aims to predict the electricity consumption of the water source heat pump system of an office building based on meteorological data. The key variables are selected by error analysis and sensitivity analysis, and the effects of each variable on the models’ prediction performance can be obtained. Besides, the prediction models are established by support vector regression algorithm and trained by the local meteorological data. The results show that the positive and negative variables can be identified, and these positive variables are responsible for more than 70% of the total importance. Moreover, the root mean square error falls to 4.6044 from 7.8227 and the relative square error falls to 0.1494 from 0.4313 when the negative inputs are removed. And the errors reduce further to 4.1160 and 0.1194 by parameter optimization.
