返回
CSP mirror soiling characterization and modeling
DOI:10.1016/j.solmat.2018.05.035.png)
摘要
En 中文
Soiling stands as a major problem for solar energy conversion technologies, causing unwanted transmittance, reflectance and absorbance losses. In this paper, a TraCS (Tracking Cleanliness Sensor) is used to quantify soiling effect in a flat mirror and to calculate soiling rates between periods without rain. Environmental parameters such as vertical wind speed, air temperature, relative humidity and particulate matter in the atmosphere are used as predictors to model soiling. Relations and trends between input and output are analyzed using a simple linear regression model and also through an interaction model. Further investigation is performed with a neural network approach to assess its viability for this type of problem and also for comparison with the previous models.
Keyword:
Solar energy
CSP
Soiling
Modeling
AI总结
对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.3
论文数:
1.2W
被引数:
3.6W
机构
引用论文
Modeling of soiled PV module with neural networks and regression using particle size composition使用神经网络对污染的PV模块进行建模,并使用粒度组成进行回归
SOLAR ENERGY
IF6.6
Modelling the spectral irradiance distribution in sunny inland locations using an ANN-based methodology
ENERGY
IF9.4
Effect of dust deposition on the performance of a solar desalination plant operating in an arid desert area
SOLAR ENERGY
IF6.6
Saharan dust transport to Europe and its impact on photovoltaic performance: A case study of soiling in Portugal
SOLAR ENERGY
IF6.6

