基于温度的遗传算法预测太阳总辐射模型[GA]

R. Meenal, A. Selvakumar
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引用次数: 1

摘要

全球太阳辐射(GSR)数据是太阳能应用最重要的参数。但是,由于成本较高和测量困难,并非印度所有地区都能获得辐射数据。因此,有必要开发利用现有气象参数(如日照数据、相对湿度和温度)预测GSR的方法,其中温度是常用的数据。本文采用遗传算法(GA)和统计回归技术(SRT)估算了印度月平均日太阳辐射水平。为此,建立了基于最高和最低温度的埃型经验模型。利用遗传算法和SRT确定经验系数。模型结果与印度气象局浦那的GSR实测数据进行了对比验证。结果表明,基于遗传算法获得的经验系数比回归模型具有更高的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Temperature based model for predicting global solar radiation using genetic algorithm [GA]
Global solar radiation (GSR) data is the most important parameter for solar energy applications. But radiation data is not available in all the locations of India, due to higher cost and difficulty in measurements. Therefore it is necessary to develop methods to predict GSR using available meteorological parameters like sunshine data, relative humidity and temperature out of which temperature is the commonly available data. In this work, Genetic algorithm (GA) and statistical regression technique (SRT) is used to estimate the monthly mean daily global solar radiation on horizontal surface in India. For this purpose, angstrom type empirical model based on maximum and minimum temperature is developed. The empirical coefficients are determined using GA and SRT. The model results are validated against the measured GSR data of India Meteorological Department (IMD), Pune. From the results, it is found that the obtained empirical coefficients based on GA have more accuracy than the regressive model.
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