Machine Learning in Renewable Energy Application: Intelligence System for Solar Panel Cleaning

Q3 Social Sciences
Ahmad Al-dahoud, M. Fezari, A. Aldahoud
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引用次数: 1

Abstract

The objective of this study is to develop an automatic cleaning system for Photovoltaic (PV) solar panels using machine learning algorithms. The experiment includes two phases. Phase one is to perform testing and reading of the sensor in 4 different classes which include no-dust, little dust, dusty, and very dusty during day and night time. The reading was taken using a visual inspection of the solar panel and the sensor reading using a multimeter. Phase two uses supervised learning to test and calibrate the sensor using the KNN algorithm. The classification was done using the data gathered from the sensor with one of the main classes identified. A total of 800 readings were taken. The results show the sensor reading taken during the night was more stable and accurate due to the sensor’s sensitivity to noise which includes: heat and light during the daytime. Secondly, using machine learning (KNN algorithm) we get a 95% (with K=5) correct classification for the four main classes which determines the level of cleaning needed for the solar panel.
机器学习在可再生能源应用中的应用:太阳能电池板清洁智能系统
本研究的目的是使用机器学习算法开发光伏太阳能电池板的自动清洁系统。实验包括两个阶段。第一阶段是对传感器进行4个不同类别的测试和读取,包括无灰尘、少灰尘、多灰尘和在白天和晚上非常多灰尘。使用万用表对太阳能电池板进行目视检查并获取传感器读数。第二阶段使用监督学习来使用KNN算法测试和校准传感器。使用从传感器收集的数据进行分类,其中一个主要类别已确定。总共读取了800个读数。结果显示,由于传感器对噪声的敏感性,夜间获取的传感器读数更加稳定和准确,噪声包括:白天的热和光。其次,使用机器学习(KNN算法),我们对四个主要类别进行了95%(K=5)的正确分类,这决定了太阳能电池板所需的清洁水平。
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来源期刊
CiteScore
1.90
自引率
0.00%
发文量
118
期刊介绍: WSEAS Transactions on Environment and Development publishes original research papers relating to the studying of environmental sciences. We aim to bring important work to a wide international audience and therefore only publish papers of exceptional scientific value that advance our understanding of these particular areas. The research presented must transcend the limits of case studies, while both experimental and theoretical studies are accepted. It is a multi-disciplinary journal and therefore its content mirrors the diverse interests and approaches of scholars involved with sustainable development, climate change, natural hazards, renewable energy systems and related areas. We also welcome scholarly contributions from officials with government agencies, international agencies, and non-governmental organizations.
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