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

Jose Cruz; Wilson Mamani; Christian Romero; Ferdinand Pineda

doi:10.1007/978-3-030-64580-9_16

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

Jose Cruz, Wilson Mamani, Christian Romero, Ferdinand Pineda

Universidad Nacional del Altiplano

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

1 Cita (Scopus)

Resumen

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%.

Idioma original	Inglés
Título de la publicación alojada	Machine Learning, Optimization, and Data Science - 6th International Conference, LOD 2020, Revised Selected Papers
Editores	Giuseppe Nicosia, Varun Ojha, Emanuele La Malfa, Giorgio Jansen, Vincenzo Sciacca, Panos Pardalos, Giovanni Giuffrida, Renato Umeton
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	191-202
Número de páginas	12
ISBN (versión impresa)	9783030645793
DOI	https://doi.org/10.1007/978-3-030-64580-9_16
Estado	Publicada - 2020
Publicado de forma externa	Sí
Evento	6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020 - Siena, Italia Duración: 19 jul. 2020 → 23 jul. 2020

Serie de la publicación

Nombre	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen	12566 LNCS
ISSN (versión impresa)	0302-9743
ISSN (versión digital)	1611-3349

Conferencia

Conferencia	6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020
País/Territorio	Italia
Ciudad	Siena
Período	19/07/20 → 23/07/20

ODS de las Naciones Unidas

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10.1007/978-3-030-64580-9_16

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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. En G. Nicosia, V. Ojha, E. La Malfa, G. Jansen, V. Sciacca, P. Pardalos, G. Giuffrida, & R. Umeton (Eds.), Machine Learning, Optimization, and Data Science - 6th International Conference, LOD 2020, Revised Selected Papers (pp. 191-202). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12566 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-64580-9_16

Cruz, Jose ; Mamani, Wilson ; Romero, Christian et al. / Multi-parameter Regression of Photovoltaic Systems using Selection of Variables with the Method : Recursive Feature Elimination for Ridge, Lasso and Bayes. Machine Learning, Optimization, and Data Science - 6th International Conference, LOD 2020, Revised Selected Papers. editor / Giuseppe Nicosia ; Varun Ojha ; Emanuele La Malfa ; Giorgio Jansen ; Vincenzo Sciacca ; Panos Pardalos ; Giovanni Giuffrida ; Renato Umeton. Springer Science and Business Media Deutschland GmbH, 2020. pp. 191-202 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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

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%.",

keywords = "Auto correlation, Bayesian Ridge, Homoscedasticity, Lasso, Linear regression, Regularizaci{\'o}n shrinkage, RFE, Ridge, Subset selection",

author = "Jose Cruz and Wilson Mamani and Christian Romero and Ferdinand Pineda",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020 ; Conference date: 19-07-2020 Through 23-07-2020",

year = "2020",

doi = "10.1007/978-3-030-64580-9_16",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "Machine Learning, Optimization, and Data Science - 6th International Conference, LOD 2020, Revised Selected Papers",

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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. En G Nicosia, V Ojha, E La Malfa, G Jansen, V Sciacca, P Pardalos, G Giuffrida & R Umeton (eds.), Machine Learning, Optimization, and Data Science - 6th International Conference, LOD 2020, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12566 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 191-202, 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020, Siena, Italia, 19/07/20. https://doi.org/10.1007/978-3-030-64580-9_16

Multi-parameter Regression of Photovoltaic Systems using Selection of Variables with the Method: Recursive Feature Elimination for Ridge, Lasso and Bayes. / Cruz, Jose; Mamani, Wilson; Romero, Christian et al.
Machine Learning, Optimization, and Data Science - 6th International Conference, LOD 2020, Revised Selected Papers. ed. / Giuseppe Nicosia; Varun Ojha; Emanuele La Malfa; Giorgio Jansen; Vincenzo Sciacca; Panos Pardalos; Giovanni Giuffrida; Renato Umeton. Springer Science and Business Media Deutschland GmbH, 2020. p. 191-202 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12566 LNCS).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Multi-parameter Regression of Photovoltaic Systems using Selection of Variables with the Method

T2 - 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020

AU - Cruz, Jose

AU - Mamani, Wilson

AU - Romero, Christian

AU - Pineda, Ferdinand

PY - 2020

Y1 - 2020

N2 - 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%.

AB - 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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KW - Bayesian Ridge

KW - Homoscedasticity

KW - Lasso

KW - Linear regression

KW - Regularización shrinkage

KW - RFE

KW - Ridge

KW - Subset selection

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DO - 10.1007/978-3-030-64580-9_16

M3 - Conference contribution

AN - SCOPUS:85101400719

SN - 9783030645793

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 191

EP - 202

BT - Machine Learning, Optimization, and Data Science - 6th International Conference, LOD 2020, Revised Selected Papers

A2 - Nicosia, Giuseppe

A2 - Ojha, Varun

A2 - La Malfa, Emanuele

A2 - Jansen, Giorgio

A2 - Sciacca, Vincenzo

A2 - Pardalos, Panos

A2 - Giuffrida, Giovanni

A2 - Umeton, Renato

PB - Springer Science and Business Media Deutschland GmbH

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Cruz J, Mamani W, Romero C, Pineda F. Multi-parameter Regression of Photovoltaic Systems using Selection of Variables with the Method: Recursive Feature Elimination for Ridge, Lasso and Bayes. En Nicosia G, Ojha V, La Malfa E, Jansen G, Sciacca V, Pardalos P, Giuffrida G, Umeton R, editores, Machine Learning, Optimization, and Data Science - 6th International Conference, LOD 2020, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2020. p. 191-202. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-64580-9_16

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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