Jose Emmanuel Cruz de la Cruz
ABSTRACT
Today is clean and renewable energy systems are attractive for the various applications that use non-renewable energy, as they provide better performance as well as a low long-term cost of electrical energy consumption. The analysis of these systems using artificial intelligence algorithms helps to improve sustainability and to produce energy efficiently. In this research, a photovoltaic system was analyzed with multiparametric regression models using step-by-step selection techniques such as RFE and SFS adding Shinkrage regularization, thus proposing a hybrid model for the analysis of the 14 independent variables used in this study. The division of the data by cross-validation was 80% for training and 20% for testing, seeds were applied in the randomization of data to obtain a better performance obtaining seed of 8849. The proposed hybrid models RFE-Ridge-Bayesian, RFE -Lasso, and RFE-Ridge discarded the variables: Total energy ',' Daily energy 'and' Irradiance ', while for the proposed hybrid models SFS-Ridge-Bayesian, SFS-Lasso, and SFS-Ridge eliminated:' Frequency ',' Energy daily 'and' Irradiance '. The optimal hyperparameters for the Ridge and Lasso models were also calculated, obtaining alpha values of 1.538 and 0.01 respectively. To validate all the proposed hybrid models, the analysis of linearity, normality of the error terms, non-autocorrelation of the error terms, and homoscedasticity was performed, all models satisfying said validation. The variable to predict with an accuracy of 99.97% in all cases was the active power.
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Index Terms
- Prediction of power of a photovoltaic system in height using hybrid models of Shinkrage regularization with RFE and SFS
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