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Neural networks fusion for temperature forecasting

  • S.I. : Advances in Bio-Inspired Intelligent Systems
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Abstract

Weather conditions have a direct relationship with energy consumption, touristic activities, and farm tasks. By means of the fusion of artificial neural networks, this work presents a system with a general method that obtains an accurate temperature prediction. The objective is temperature, but the method is easily scalable to obtain any other meteorological parameter; this is one strength of the model. This research carries out a temperature prediction modeling that contributes to obtain better results with different applications as energy generation or in other different fields such as tourism or farming. The database contains data of 5 years from stations located in Gran Canaria at Gran Canaria Airport and in Tenerife at Tenerife Sur Airport. Data are collected hourly, what means more than 100,000 samples. This quantity of samples gives sturdiness to the study. With this method, our best result in terms of mean absolute error and using data from meteorological stations in Canary Islands is 0.41 °C.

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Acknowledgements

This work has been supported by Endesa Foundation and the University of Las Palmas Foundation under Grant “Programa Innova Canarias 2020.”

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Authors and Affiliations

  1. Institute for Technological Development and Innovation in Communications (IDeTIC), University of Las Palmas de Gran Canaria, Campus Universitario de Tafira, 35017, Las Palmas de Gran Canaria, Spain

    José Gustavo Hernández-Travieso

  2. Signal and Communications Department, University of Las Palmas de Gran Canaria, Campus Universitario de Tafira, sn, Ed. de Telecomunicación, Pabellón B, 35017, Las Palmas de Gran Canaria, Spain

    Antonio G. Ravelo-García

  3. Signal and Communications Department, Institute for Technological Development and Innovation in Communications (IDeTIC), University of Las Palmas de Gran Canaria, Campus Universitario de Tafira, sn, Ed. de Telecomunicación, Pabellón B, Despacho 111, 35017, Las Palmas de Gran Canaria, Spain

    Jesús B. Alonso-Hernández & Carlos M. Travieso-González

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  1. José Gustavo Hernández-Travieso
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  2. Antonio G. Ravelo-García
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  3. Jesús B. Alonso-Hernández
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Correspondence to José Gustavo Hernández-Travieso.

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The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

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Hernández-Travieso, J.G., Ravelo-García, A.G., Alonso-Hernández, J.B. et al. Neural networks fusion for temperature forecasting. Neural Comput & Applic 32, 15699–15710 (2020). https://doi.org/10.1007/s00521-018-3450-0

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  • DOI: https://doi.org/10.1007/s00521-018-3450-0

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