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.
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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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