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Daily Accumulative Photovoltaic Energy Prediction Using Hybrid Intelligent Model

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Hybrid Artificial Intelligent Systems (HAIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14001))

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Abstract

Nowadays, there is an increase in the use of renewable energies to fight against climatic change. One of the most popular energy is solar one, which could have two different produced energies: thermal and electrical. The case study used in this research is an installation located in the University of A Coruña, in Ferrol, and it is a photovoltaic array with five rows of 12 solar panels each one, with a total peak power of 12,9 kW. The installation is correctly oriented to the South, with an inclination of 35\(^\circ \) to achieve the theoretical performance of 99,82%. The model created in this research predicts the accumulated daily energy produced by the installation base on the solar hours predicted by the meteorological service. The other inputs of the model are the real solar hours and the energy produced the day before the prediction. A hybrid model is created by dividing the dataset with a clustering technique to create groups. Then, each cluster trains a regression algorithm to increase the global prediction performance. K-Means are used to create the clusters and Artificial Neural Networks, Support Vector Machines for Regression and Polynomial Regression are used to create the local models for clusters.

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Acknowledgement

Míriam Timiraos’s research was supported by the “Xunta de Galicia” (Regional Government of Galicia) through grants to industrial PhD (http://gain.xunta.gal/), under the “Doutoramento Industrial 2022” grant with reference: 04_IN606D_2022_2692965.

Author information

Authors and Affiliations

  1. CTC, Department of Industrial Engineering, CITIC, University of A Coruña, EPEF, Calle Mendizábal s/n, Campus de Esteiro, 15403, Ferrol, A Coruña, Spain

    Antonio Díaz-Longueira, Míriam Timiraos, José-Luis Casteleiro-Roca & Esteban Jove

  2. Fundación Instituto Tecnológico de Galicia, Department of Water Technologies, National Technological Center, Cantón Grande 9, Planta 3, 15003, A Coruña, Spain

    Míriam Timiraos

  3. Department of Computer Science and System Engineering, University of La Laguna, Avda. Astrof. Francisco Sánchez s/n, 38200, S/C de Tenerife, Spain

    Juan Albino Méndez Pérez

Authors
  1. Antonio Díaz-Longueira
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  2. Míriam Timiraos
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  3. Juan Albino Méndez Pérez
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  4. José-Luis Casteleiro-Roca
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  5. Esteban Jove
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Corresponding author

Correspondence to Míriam Timiraos .

Editor information

Editors and Affiliations

  1. University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. University of Leon, León, Spain

    Hilde Pérez García

  3. University of La Rioja, Logroño, La Rioja, Spain

    Francisco Javier Martínez de Pisón

  4. Pablo de Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  5. Pablo de Olavide University, Seville, Spain

    Alicia Troncoso Lora

  6. University of Burgos, Burgos, Spain

    Álvaro Herrero

  7. University of A Coruña, Ferrol - Coruña, Spain

    José Luis Calvo Rolle

  8. University of A Coruña, Ferrol - Coruña, Spain

    Héctor Quintián

  9. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Díaz-Longueira, A., Timiraos, M., Pérez, J.A.M., Casteleiro-Roca, JL., Jove, E. (2023). Daily Accumulative Photovoltaic Energy Prediction Using Hybrid Intelligent Model. In: García Bringas, P., et al. Hybrid Artificial Intelligent Systems. HAIS 2023. Lecture Notes in Computer Science(), vol 14001. Springer, Cham. https://doi.org/10.1007/978-3-031-40725-3_49

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  • DOI: https://doi.org/10.1007/978-3-031-40725-3_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-40724-6

  • Online ISBN: 978-3-031-40725-3

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