Enhancement of output power of semitransparent photovoltaic thermal air collector using ANFIS model
Enhancement of output power of semitransparent photovoltaic thermal air collector using ANFIS model
复制标题
利用ANFIS模型提高半透明光伏热空气集热器输出功率
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
H. Gupta
中科院分区：
文献类型：
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作者：
Ruby Beniwal;N. S. Beniwal;H. Gupta
本文旨在开发一种使用自适应神经模糊推理系统（ANFIS）架构的模型，通过预测不同天气条件和不同气候区的光伏电池板的故障来增强半透明光伏热（PV/T）空气收集器的输出功率。光伏组件温度升高是一个大问题，会降低其使用寿命。使用基于 AFIS 的热设计和光伏电池的最佳放置，降低了光伏 (PV) 模块的工作温度和热点温度，从而延长了使用寿命并降低了故障率，从而提高了输出功率。总体分析表明，使用基于 AFIS 的模型通过预测准确的参数，将 100 个时期内的绝对误差最小化至 1.4%，从而增强了输出功率。
The paper aims to develop a model using adaptive neuro-fuzzy inference system (ANFIS) architecture for enhancing output power of semitransparent photovoltaic thermal (PV/T) air collector by predicting the failure of PV panels for different weather conditions and different climate zones. Increased temperature of the photovoltaic module is a big problem which reduces its working life. The working and hotspot temperatures of photovoltaic (PV) modules have been reduced using ANFIS-based thermal design with optimal placement of PV cells which increase their life and reduce the failure rate which in turn increase the output power. The overall analysis reveals that output power is enhanced using ANFIS-based model by minimizing absolute error to 1.4% in 100 epochs by predicting accurate parameters.