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Multi-parameter Regression of Photovoltaic Systems using Selection of Variables with the Method: Recursive Feature Elimination for Ridge, Lasso and Bayes

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Machine Learning, Optimization, and Data Science (LOD 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12566))

  • 1343 Accesses

Abstract

The research focuses on the application of regularization techniques in a multiparameter linear regression model to predict the DC voltage levels of a photovoltaic system from 14 variables. Two predictions were made, in the first prediction, all the variables were taken, 14 independent variable and one dependent variable; Shrinkage Regularization types were applied, as a variable selection method. In the second prediction we propose the use of semiautomatic methods, we used Recursive Feature Elimination (RFE) as a variable selection method and to obtained results. We applied the following Shrinkage regularization methods: Lasso, Ridge and Bayesian Ridge.

The results were validated demonstrating: linearity, normality of error terms, non-self-correlation and homoscedasticity. In all cases the precision obtained is greater than 91.99%.

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Authors and Affiliations

  1. Universidad Nacional del Altiplano, Puno, Peru

    Jose Cruz, Wilson Mamani, Christian Romero & Ferdinand Pineda

Authors
  1. Jose Cruz
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  2. Wilson Mamani
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  3. Christian Romero
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  4. Ferdinand Pineda
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Corresponding authors

Correspondence to Jose Cruz , Wilson Mamani , Christian Romero or Ferdinand Pineda .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Giorgio Jansen

  5. Almawave, Rome, Italy

    Vincenzo Sciacca

  6. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Harvard University, Cambridge, MA, USA

    Renato Umeton

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Cruz, J., Mamani, W., Romero, C., Pineda, F. (2020). Multi-parameter Regression of Photovoltaic Systems using Selection of Variables with the Method: Recursive Feature Elimination for Ridge, Lasso and Bayes. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science(), vol 12566. Springer, Cham. https://doi.org/10.1007/978-3-030-64580-9_16

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  • DOI: https://doi.org/10.1007/978-3-030-64580-9_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64579-3

  • Online ISBN: 978-3-030-64580-9

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