Intelligent predictive model of electrical power in photovoltaic systems through solar radiation and temperature on site

Luis Omar Lara Cerecedo; Nun Pitalua-Diaz; Jesus Fernando Hinojosa Palafox; Juan Anzurez Marin; Salvador Ramirez Zavala

doi:10.1109/ROPEC53248.2021.9667977

Intelligent predictive model of electrical power in photovoltaic systems through solar radiation and temperature on site

Luis Omar Lara Cerecedo, Nun Pitalua-Diaz, Jesus Fernando Hinojosa Palafox, Juan Anzurez Marin, Salvador Ramirez Zavala

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

A Neuro-fuzzy ANFIS model is presented that allows predicting the generation of electrical power in a photovoltaic system considering the solar radiation and environmental temperature of the geographical area. This model enables fuzzy modeling to learn from the data set and, as a result, compute the most appropriate membership function parameters. It also makes use of neural networks' abilities to classify data and find patterns. The algorithm is developed using MATLAB® software and is trained with the acquired data weather station located in Hermosillo, Sonora City, and the electrical power output of the system at the site. The algorithm is evaluated using rigorous evaluation factors which shows how much the predicted value differs from the actual value, resulting in the following predicted values: RSME: 295.2686, NRMSE: 0.09496, RMSPE: 9.4306, MAE: 218.6996, and MAPE: 6.985, respectively.

Original language	English
Title of host publication	2021 23rd IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665434270
DOIs	https://doi.org/10.1109/ROPEC53248.2021.9667977
State	Published - 2021
Event	23rd IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2021 - Virtual, Ixtapa, Mexico Duration: 10 Nov 2021 → 12 Nov 2021

Publication series

Name	2021 23rd IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2021

Conference

Conference	23rd IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2021
Country/Territory	Mexico
City	Virtual, Ixtapa
Period	10/11/21 → 12/11/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

Estimation error
Fuzzy neural networks
Hybrid intelligent systems
Photovoltaic systems
Prediction algorithms
Solar power generation

Access to Document

10.1109/ROPEC53248.2021.9667977

Cite this

Cerecedo, L. O. L., Pitalua-Diaz, N., Palafox, J. F. H., Marin, J. A., & Zavala, S. R. (2021). Intelligent predictive model of electrical power in photovoltaic systems through solar radiation and temperature on site. In 2021 23rd IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2021 (2021 23rd IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ROPEC53248.2021.9667977

Cerecedo, Luis Omar Lara ; Pitalua-Diaz, Nun ; Palafox, Jesus Fernando Hinojosa et al. / Intelligent predictive model of electrical power in photovoltaic systems through solar radiation and temperature on site. 2021 23rd IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (2021 23rd IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2021).

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abstract = "A Neuro-fuzzy ANFIS model is presented that allows predicting the generation of electrical power in a photovoltaic system considering the solar radiation and environmental temperature of the geographical area. This model enables fuzzy modeling to learn from the data set and, as a result, compute the most appropriate membership function parameters. It also makes use of neural networks' abilities to classify data and find patterns. The algorithm is developed using MATLAB{\textregistered} software and is trained with the acquired data weather station located in Hermosillo, Sonora City, and the electrical power output of the system at the site. The algorithm is evaluated using rigorous evaluation factors which shows how much the predicted value differs from the actual value, resulting in the following predicted values: RSME: 295.2686, NRMSE: 0.09496, RMSPE: 9.4306, MAE: 218.6996, and MAPE: 6.985, respectively.",

keywords = "Estimation error, Fuzzy neural networks, Hybrid intelligent systems, Photovoltaic systems, Prediction algorithms, Solar power generation",

author = "Cerecedo, {Luis Omar Lara} and Nun Pitalua-Diaz and Palafox, {Jesus Fernando Hinojosa} and Marin, {Juan Anzurez} and Zavala, {Salvador Ramirez}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 23rd IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2021 ; Conference date: 10-11-2021 Through 12-11-2021",

year = "2021",

doi = "10.1109/ROPEC53248.2021.9667977",

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Cerecedo, LOL, Pitalua-Diaz, N , Palafox, JFH, Marin, JA & Zavala, SR 2021, Intelligent predictive model of electrical power in photovoltaic systems through solar radiation and temperature on site. in 2021 23rd IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2021. 2021 23rd IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2021, Institute of Electrical and Electronics Engineers Inc., 23rd IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2021, Virtual, Ixtapa, Mexico, 10/11/21. https://doi.org/10.1109/ROPEC53248.2021.9667977

Intelligent predictive model of electrical power in photovoltaic systems through solar radiation and temperature on site. / Cerecedo, Luis Omar Lara; Pitalua-Diaz, Nun ; Palafox, Jesus Fernando Hinojosa et al.
2021 23rd IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (2021 23rd IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Intelligent predictive model of electrical power in photovoltaic systems through solar radiation and temperature on site

AU - Cerecedo, Luis Omar Lara

AU - Pitalua-Diaz, Nun

AU - Palafox, Jesus Fernando Hinojosa

AU - Marin, Juan Anzurez

AU - Zavala, Salvador Ramirez

PY - 2021

Y1 - 2021

N2 - A Neuro-fuzzy ANFIS model is presented that allows predicting the generation of electrical power in a photovoltaic system considering the solar radiation and environmental temperature of the geographical area. This model enables fuzzy modeling to learn from the data set and, as a result, compute the most appropriate membership function parameters. It also makes use of neural networks' abilities to classify data and find patterns. The algorithm is developed using MATLAB® software and is trained with the acquired data weather station located in Hermosillo, Sonora City, and the electrical power output of the system at the site. The algorithm is evaluated using rigorous evaluation factors which shows how much the predicted value differs from the actual value, resulting in the following predicted values: RSME: 295.2686, NRMSE: 0.09496, RMSPE: 9.4306, MAE: 218.6996, and MAPE: 6.985, respectively.

AB - A Neuro-fuzzy ANFIS model is presented that allows predicting the generation of electrical power in a photovoltaic system considering the solar radiation and environmental temperature of the geographical area. This model enables fuzzy modeling to learn from the data set and, as a result, compute the most appropriate membership function parameters. It also makes use of neural networks' abilities to classify data and find patterns. The algorithm is developed using MATLAB® software and is trained with the acquired data weather station located in Hermosillo, Sonora City, and the electrical power output of the system at the site. The algorithm is evaluated using rigorous evaluation factors which shows how much the predicted value differs from the actual value, resulting in the following predicted values: RSME: 295.2686, NRMSE: 0.09496, RMSPE: 9.4306, MAE: 218.6996, and MAPE: 6.985, respectively.

KW - Estimation error

KW - Fuzzy neural networks

KW - Hybrid intelligent systems

KW - Photovoltaic systems

KW - Prediction algorithms

KW - Solar power generation

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M3 - Contribución a la conferencia

AN - SCOPUS:85124967291

T3 - 2021 23rd IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2021

BT - 2021 23rd IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 23rd IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2021

Y2 - 10 November 2021 through 12 November 2021

ER -

Cerecedo LOL, Pitalua-Diaz N , Palafox JFH, Marin JA, Zavala SR. Intelligent predictive model of electrical power in photovoltaic systems through solar radiation and temperature on site. In 2021 23rd IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. (2021 23rd IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2021). doi: 10.1109/ROPEC53248.2021.9667977

Intelligent predictive model of electrical power in photovoltaic systems through solar radiation and temperature on site

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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