New Model of Photovoltaic System Adapted by a Digital MPPT Control and Radiation Predictions Using Deep Learning

A. Zouhri, M. el Mallahi
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Abstract

Forecasting solar radiation is one of the most useful impacts that can give us a deep vision on maintaining the integrity of solar systems. The availability and ease of use of the data make this process simpler. Predictions may be produced using various data sources. In fact, there are two different forms that can be identified. The first one was the use of historical solar radiation data, while the second one was the use of other meteorological parameters. The availability and choice of the data source can have an effect on the choice of the model and methods used. Our proposed article aims to take research as an example to review the solar radiation situation in Morocco and outline the methods of predicting solar radiation using different machine learning and deep learning methods like ANN, MLP, BPNN, DNN, and LSTM, which are used in different regions in Morocco.
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通过数字 MPPT 控制和深度学习辐射预测调整光伏系统的新模型
太阳辐射预报是最有用的影响之一,可以让我们深入了解如何维护太阳系的完整性。数据的可用性和易用性使这一过程变得更加简单。可以利用各种数据源进行预测。事实上,可以确定有两种不同的形式。第一种是使用历史太阳辐射数据,第二种是使用其他气象参数。数据源的可用性和选择会对所用模型和方法的选择产生影响。我们建议的文章旨在以研究为例,回顾摩洛哥的太阳辐射情况,并概述使用不同机器学习和深度学习方法预测太阳辐射的方法,如在摩洛哥不同地区使用的 ANN、MLP、BPNN、DNN 和 LSTM。
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来源期刊
Journal of Automation, Mobile Robotics and Intelligent Systems
Journal of Automation, Mobile Robotics and Intelligent Systems Engineering-Control and Systems Engineering
CiteScore
1.10
自引率
0.00%
发文量
25
期刊介绍: Fundamentals of automation and robotics Applied automatics Mobile robots control Distributed systems Navigation Mechatronics systems in robotics Sensors and actuators Data transmission Biomechatronics Mobile computing
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