Sunshine based models for estimation of monthly mean daily global solar radiation (Case studies of north eastern location (Patna) in India)

Sarvesh Kumar, Vishwajith Kumar, Amit Kumar, D. Sahu
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Abstract

In learning of solar energy utilization, one should know about global solar radiation data precisely and it is compulsory on behalf of optimal design and evaluation of the performance of any solar energy conversion system. In this topic, a north-eastern location in India (Patna) as case studies, six models based on empirical formula are like quadratic, cubic, logarithmic, exponential and exponent i.e. derivative of Angstrom- Prescott obtained linear regression model; relating the global solar radiation data with the sunshine data. Estimate the coefficients (constants) of different models by the curve estimation technique with the help of curve fitting in MATLAB. Utilizing seven statistical indicators, correlation among estimated and determined global solar radiation has been performed. As a result, the quadratic model is obtained as the most appropriate for the expectation of solar radiation in Patna. Also, this model may then be utilized for areas in India with comparative meteorological and geological attributes at which sun-based information is not available.
基于日照的月平均日全球太阳辐射估算模式(以印度东北部(巴特那)为例)
在学习太阳能利用时,必须准确地了解全球的太阳辐射数据,这对于任何太阳能转换系统的优化设计和性能评估都是必不可少的。本课题以印度东北部某地(巴特那)为例,基于经验公式的二次型、三次型、对数型、指数型和指数型即导数的Angstrom- Prescott获得线性回归模型;将全球太阳辐射数据与日照数据联系起来。利用MATLAB中的曲线拟合技术,利用曲线估计技术对不同模型的系数(常数)进行估计。利用7个统计指标,对估算和确定的太阳总辐射进行了相关性分析。结果表明,二次模型最适合于巴特那地区太阳辐射的期望。此外,该模型还可用于印度具有比较气象和地质属性的地区,这些地区无法获得基于太阳的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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