Abstract
Measurements of heat collection rate and heat loss coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, conventional measurement requires expensive detection devices and undergoes a series of complicated procedures. To simplify the measurement and reduce the cost, software based on artificial neural networks for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters was developed. Using multilayer feed-forward neural networks with back-propagation algorithm, we developed and tested our program on the basis of 915measuredsamples of water-in-glass evacuated tube solar water heaters. This artificial neural networks-based software program automatically obtained accurate heat collection rateand heat loss coefficient using simply "portable test instruments" acquired parameters, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, angle between tubes and ground and final temperature. Our results show that this software (on both personal computer and Android platforms) is efficient and convenient to predict the heat collection rate and heat loss coefficient due to it slow root mean square errors in prediction. The software now can be downloaded from http://t.cn/RLPKF08.
Highlights
The boosting solar water heater market worldwide promotes the demand for advanced technologies and related products
Using the "portable test instruments", for an in service water-in-glass evacuated tube solar water heater, precise values of tube length(mm), number of tubes, tube center distance(mm), heat water mass in tank(kg), collector area(m2), angle between tubes and ground(°) and final temperature(°C)can be obtained from outdoors, whereas the heat collection rate(MJ/m2) and heat loss coefficient[W/(Km3)]can only be determined by the conventional detection device after being dismantled
To avoid complex disassembly and obtain the heat collection rate and heat loss coefficient in real time, here, we aim at using machine learning techniques, MLFNs to develop a series of prediction models for the heat collection rate and heat loss coefficient
Summary
The boosting solar water heater market worldwide promotes the demand for advanced technologies and related products. The solar water heater has become one of the most efficient and economical ways to utilize solar energy. In China, the water-in-glass evacuated tube solar water heaters have been widely used because of their high energy efficiency and simple installation [2, 3].The solar energy industry has developed rapidly in China. The market share of all-glass evacuated tube solar collectors was about 88% in 2003, while by 2009 it increased to 95%[5]. From 2001 to 2009, the production of evacuated solar tubes increased at an annual rate of 30% in China [3]. More than twenty million evacuated solar tubes were produced in 2001, and by 2009 the production increased to350 million [6]
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