KEYWORDS: Education and training, Data modeling, Principal component analysis, Electronic design automation, Solar cells, Machine learning, Cross validation, Error analysis, Random forests, Modeling
The variation in temperature of PV modules can affect the efficiency of power generation. Therefore, many studies have been conducted to predict the temperature of PV modules. In this paper, the extreme random tree algorithm is utilized with EDA and PCA analysis to accurately predict the temperature of PV modules in the next hour by filtering and structuring available features. EDA and PCA analysis enhance the model's understanding of the degree of addition, subtraction, and similarity between different features. They also help filter out and construct new key features. Compared to the random forest commonly used in previous studies, the extreme random tree algorithm is more random in the choice of bifurcation in tree building, enabling it to jump out of the vicious circle of local optima and learn the data adequately. After training, the model is tested using actual operating wind turbine data for validation, and the results indicate that the method is highly accurate, noise-resistant, targeted, and practical.
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