水体上方太阳能光伏板工作温度的预测建模:与地面安装设备的性能对比分析

Energies Pub Date : 2024-07-16 DOI:10.3390/en17143489
Karmendra Kumar Agrawal, Shibani Khanra Jha, Ravi Kant Mittal, Ajit Pratap Singh, Sanjay Vashishtha, Saurabh Gupta, M. K. Soni
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引用次数: 0

摘要

太阳能电池板的效率在很大程度上受其工作温度的影响。新兴可再生能源替代品的最新进展使得光伏(PV)模块可以安装在水体上,从而利用其更高的效率和相关优势。本文研究了水体上方安装的太阳能电池板的运行性能,并将其与地面安装的太阳能光伏装置进行了比较。根据印度 BITS Pilani 的实验装置,开发了电池板温度回归模型。开发的回归模型包括线性模型、二次模型和指数模型,用于预测水体上方太阳能光伏装置的运行温度。这些模型纳入了环境温度、太阳日照、风速、水温和湿度等参数。其中,三个参数的一度回归模型优于四个或五个参数的模型,预测误差为 5.5 °C。值得注意的是,研究发现,与记录数据相比,最佳模型的年能量输出估计值误差小于 0.2%。研究表明,水体上方的太阳能光伏电池板的年发电量比地面安装系统高出约 2.59%。新开发的回归模型为估算水体上方太阳能光伏装置的工作温度提供了一个预测工具,只需使用三个气象参数:环境温度、太阳日照和风速,即可准确预测温度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predictive Modeling of Solar PV Panel Operating Temperature over Water Bodies: Comparative Performance Analysis with Ground-Mounted Installations
Solar panel efficiency is significantly influenced by its operating temperature. Recent advancements in emerging renewable energy alternatives have enabled photovoltaic (PV) module installation over water bodies, leveraging their increased efficiency and associated benefits. This paper examines the operational performance of solar panels placed over water bodies, comparing them to ground-mounted solar PV installations. Regression models for panel temperature are developed based on experimental setups at BITS Pilani, India. Developed regression models, including linear, quadratic, and exponential, are utilized to predict the operating temperature of solar PV installations above water bodies. These models incorporated parameters such as ambient temperature, solar insolation, wind velocity, water temperature, and humidity. Among these, the one-degree regression models with three parameters outperformed the models with four or five parameters with a prediction error of 5.5 °C. Notably, the study found that the annual energy output estimates from the best model had an error margin of less than 0.2% compared to recorded data. Research indicates that solar PV panels over water bodies produce approximately 2.59% more annual energy output than ground-mounted systems. The newly developed regression models provide a predictive tool for estimating the operating temperature of solar PV installations above water bodies, using only three meteorological parameters: ambient temperature, solar insolation, and wind velocity, for accurate temperature prediction.
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