Solar heat collection system photo-thermal efficiency prediction method based on GA-GRNN

Abstract

The invention discloses a solar heat collection system photo-thermal efficiency prediction method based on GA-GRNN. The method comprises the following steps that 1), parameters of the solar heat collection system are determined; 2), training test data of a network are collected and distributed; 3), a GRNN structure is constructed; 4), an optimal smoothing factor sigma is determined through GA; 5),the GA-GRNN is trained so as to obtain a trained GA-GRNN; 6), the GA-GRNN is tested, and the test data selected in the step 2 are input into the GA-GRNN which is trained in the step 5 for testing; and 7), the photo-thermal efficiency prediction is carried out by using the GA-GRNN trained in the step 6 to obtain the photo-thermal efficiency prediction result of the current solar heat collection system. According to the photo-thermal efficiency prediction method, the uncertainty of weather and climate factors can be remedied, the difficulty of data collection is greatly reduced, and the photo-thermal power of the solar heat collector can be accurately predicted.

Description

Technical field [0001] The invention belongs to the technical field of solar power generation heat collection power prediction, and relates to a GA-GRNN-based solar heat collection system light and heat efficiency prediction method. Background technique [0002] The energy crisis caused by the accelerated development of industry and economy has become a serious threat to global sustainable development. In order to alleviate the pressure on the environment and energy, solar energy has been widely used due to its pollution-free and resource-rich advantages. In addition to photovoltaic power generation and solar thermal power generation, large-scale heating projects have gradually begun to use solar heat collection technology for liquid heating and heating. The production processes of large residential areas and most large industrial enterprises in China require continuous hot water supply. In terms of traditional heating methods, the carbon dioxide emissions of coal-fired boilers ...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a GA-GRNN-based method for predicting the photothermal efficiency of a solar thermal collection system, which solves the problem that in the prior art, in the process of predicting the efficiency of solar thermal utilization, most of them need to rely on manual measurement and calculation, the data cost is high, and the result error bigger problem

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