Research On Thermal Performance And Energy Efficiency Test Of Solar Water Heater

As an important renewable energy source,solar energy had great significance for energy conservation,the natural environment protection,and climate change mitigation.China which as the largest producer and installation country of solar water heaters had leading position in solar heat utilization in the world.The solar energy efficiency evaluation had played a positive role in regulating the market and guiding the development of the solar energy industry.The evaluation which based of solar water heater thermal performance was the results of outdoor heat collection test and heat loss test.Due to the low energy density of solar energy and the intermittent nature of the weather season,high demands were made for energy efficiency assessment through outdoor testing.The verification efficiency limited by environmental conditions was low.In order to improve the efficiency of test and ensure the accuracy of energy efficiency assessment,computer-aided means proposed herein was used,data collector and test prediction was combined.The content of this article was as follows:（1）summarized the research status and analyzing relevant standards at home and abroad,the basic theory of thermal performance of solar water heaters was combined,methods to improve energy efficiency testing were explored,the useful daily heat transfer optimization equation and the water heater temperature rise prediction model were proposed.The uncertainty analysis of the test instruments was performed to ensure the research of optimization equations and prediction models had credible accuracy.（2）On the basis of the theory of vacuum tube type solar water heater,thermal performance numerical simulation was carried out,the influence of environmental factors on the daily calorie q was analyzed,A useful q₁₇conversion optimization equation was proposed for the empirical formula.Through the verification of the actual test results and literature data,the daily useful heat quantity q17 obtained by the optimization equation was less affected by the daily irradiation quantity H,which improved the accuracy of energy efficiency evaluation.（3）With the environmental condition parameters and temperature change data at each time of the heat collection test,a neural network（GA-BP）and an improved deep belief network（DBN）temperature rise prediction model were established.q₁₇ predicted by the useful daily temperature rise?T17 was solved.The improved DBN daily useful temperature rise prediction still had a better output effect at low irradiation,which indicates that the requirement for daily solar radiation at the time of test could be relaxed,and the energy efficiency of water heaters could be improved.This research had important significance in improving the efficiency of solar energy product detection efficiency and detection accuracy.