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首页> 外文期刊>Arabian Journal for Science and Engineering >A Predictive Model for Solar Photovoltaic Power based on Computational Intelligence Technique
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A Predictive Model for Solar Photovoltaic Power based on Computational Intelligence Technique

机译：基于计算智能技术的太阳能光伏发电预测模型

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摘要

This paper introduces a novel method for representing the photovoltaic (PV) characteristics using Takagi-Sugeno type neurofuzzy network (NF). The proposed NF uses four layers with sixty-four fuzzy rules. Moreover, an improved self-tuning method is developed based on the PV system and its high-performance requirements, to adjust the parameters of the fuzzy logic in order to minimize the square of the error between actual and reference outputs. The developed PV model has a compact structure, an interpretable set of rules and ultimately is accurate in predicting the output values for given input samples. The NF-PV model has been applied for reconstructing a set of practical current-voltage characteristics, and it has been shown to compare well with the measured values. The proposed approach can also be used to predict and extract the maximum power points of individual PV modules in real time. Numerical and experimental data have confirmed its accuracy.
机译：本文介绍了一种使用Takagi-Sugeno型神经模糊网络（NF）表示光伏（PV）特性的新方法。所提出的NF使用具有64个模糊规则的四层。此外，基于光伏系统及其高性能要求，开发了一种改进的自调整方法，以调整模糊逻辑的参数，以最大程度地减少实际输出与参考输出之间的误差平方。所开发的PV模型具有紧凑的结构，可解释的规则集，并且最终可以准确地预测给定输入样本的输出值。 NF-PV模型已被用于重建一组实际的电流-电压特性，并且已显示出与测量值的良好对比。提出的方法还可以用于实时预测和提取单个PV模块的最大功率点。数值和实验数据已证实其准确性。

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