Li, Ze-dong, Gao, Wen-feng, Liu, Tao, Lin, Wen-xian, and Zhao, Jia-yin (2013) The hourly cycling forecasting model of thermal performance for an evacuated solar water heater. Renewable Energy Resources, 31 (12). pp. 11-16.

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Abstract

Due to various influences such as geographic locations, seasons, and climates, it is usually hard to carry out a thermal performance test of a solar water heater under an all-weather outdoor condition. In this paper, an approximate neural network model that complies with the requirements for the heat gain of the heater was founded. Experimental measurements have been made on a heater and the neural network is trained with the measured data obtained under standard clear climate conditions are then put in the trained neural network to produce predicted results that are found to be very close to the measured data, which indicates the network possesses good extrapolating ability. The artificial neural network is then established for evacuated-tube solar water heaters, which can be used to produce a continuous prediction of water temperature. It is found that the predictions are excellent, with the deviations with 4% between the predicted data and the measured data for the heater. It is therefore concluded that the neural network can be used to accurately predict the thermal performance of a solar water heater by using the measured data obtained under the climate conditions required by the testing.

Item ID:	38388
Item Type:	Article (Research - C1)
ISSN:	1671-5292
Keywords:	neural network; physical model; domestic solar water heater; cycling prediction model
Funders:	National Natural Science Foundation of China, Yunnan Provincial Science Foundation, China
Date Deposited:	21 May 2015 01:23
FoR Codes:	09 ENGINEERING > 0915 Interdisciplinary Engineering > 091505 Heat and Mass Transfer Operations @ 60% 09 ENGINEERING > 0915 Interdisciplinary Engineering > 091504 Fluidisation and Fluid Mechanics @ 40%
SEO Codes:	85 ENERGY > 8505 Renewable Energy > 850506 Solar-Thermal Energy @ 100%
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