Impact of dust accumulation on photovoltaic panels: a review paper

IF 3.6 Q3 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY
Haneen Abuzaid, M. Awad, A. Shamayleh
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引用次数: 4

Abstract

ABSTRACT Photovoltaic systems (PV) have been extensively used worldwide as a reliable and effective renewable energy resource due to their environmental and economic merits. However, PV systems are prone to several environmental and weather conditions that impact their performance. Amongst these conditions is dust accumulation, which has a significant adversative impact on the solar cells’ performance, especially in hot and arid regions. This study provides a comprehensive review of 278 articles focused on the impact of dust on PV panels’ performance along with other associated environmental factors, such as temperature, humidity, and wind speed. The review highlights the importance of modelling dust accumulation along with other ecological factors due to their interactive nature, and the differences between cleaning techniques and schedules effectiveness. Moreover, the study provides a review of statistical and artificial intelligence models used to predict PV performance and its prediction accuracies in terms of data size and complexity. Finally, the study draws attention to several research gaps that warrant further investigation. Among these gaps is the need for proper dynamic optimisation models for cleaning schedules and a more advanced machine and deep learning models to predict dust accumulation while considering environmental and ageing factors.
粉尘堆积对光伏板的影响:综述
摘要光伏系统作为一种可靠、有效的可再生能源,由于其环境和经济优势,已在世界范围内得到广泛应用。然而,光伏系统容易受到影响其性能的几种环境和天气条件的影响。其中包括灰尘积聚,这对太阳能电池的性能有重大不利影响,尤其是在炎热和干旱地区。这项研究对278篇文章进行了全面综述,重点关注灰尘对光伏电池板性能的影响以及其他相关环境因素,如温度、湿度和风速。该综述强调了建模灰尘积聚和其他生态因素的重要性,因为它们具有相互作用的性质,以及清洁技术和时间表有效性之间的差异。此外,该研究还回顾了用于预测光伏性能的统计和人工智能模型及其在数据大小和复杂性方面的预测准确性。最后,本研究提请注意几个值得进一步调查的研究空白。这些差距包括需要适当的清洁时间表动态优化模型,以及更先进的机器和深度学习模型来预测灰尘积聚,同时考虑环境和老化因素。
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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来源期刊
International Journal of Sustainable Engineering
International Journal of Sustainable Engineering GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY-
CiteScore
7.70
自引率
0.00%
发文量
19
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