Neural networks fusion for temperature forecasting

  • José Gustavo Hernández-Travieso
  • Antonio G. Ravelo-García
  • Jesús B. Alonso-Hernández
  • Carlos M. Travieso-González
S.I. : Advances in Bio-Inspired Intelligent Systems


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.


Score fusion Modeling Temperature prediction Artificial neural networks 



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

Compliance with ethical standards

Conflict of interest

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.


  1. 1.
    World Bank (2017) Percentage of fossil fuel energy consumed by countries. Last visit: 22th November, 2017
  2. 2.
  3. 3.
    Twenergy (2017) An Endesa initiative for efficiency and sustainability. Last visit: 22th November, 2017
  4. 4.
    Brede M, de Vries BJM (2013) The energy transition in a climate-constrained world: regional versus global optimization. Environ Modell Softw 44:44–61. ISSN 1364-8152.,
  5. 5.
    del Viento G (2017) Wind-hydro-pumped station of El Hierro. Last visit: 22th November, 2017
  6. 6.
    Ellouz IK, Ben Jmaa Derbel H, Kanoun O (2009) Temperature prediction of soil-pipe-air heat exchanger using neural networks. In: 6th International multi-conference on systems, signals and devices. SSD ‘09, pp 1–6.,
  7. 7.
    Fan S, Methaprayoon K, Lee WJ (2010) Multi-region load forecasting considering alternative meteorological predictions. In: Power and energy society general meeting, 2010 IEEE, pp 1–7.,
  8. 8.
    Rastogi A, Srivastava A, Srivastava VK, Pandey AK (2011) Pattern analysis approach for prediction using wavelet neural networks. In: 2011 Seventh international conference on natural computation (ICNC), pp 695–699.,
  9. 9.
    Chen X, Xu A (2011) Temperature and humidity of air in mine roadways prediction based on BP neural network. In: 2011 International conference on multimedia technology (ICMT), pp 1273–1276,
  10. 10.
    Routh TK, Bin Yousuf AH, Hossain MN, Asasduzzaman MM, Hossain MI, Husnaeen U, Mubarak M (2012) Artificial neural network based temperature prediction and its impact on solar cell. In: 2012 International conference on informatics, electronics & vision (ICIEV), pp 897–902.,
  11. 11.
    Huang H, Chen L, Mohammadzaheri M, Hu E, Chen M (2013) Multi-zone temperature prediction in a commercial building using artificial neural network model. In: 2013 10th IEEE international conference on control and automation (ICCA), pp 1896–1901.,
  12. 12.
    Li X, Liu Y, Xin W (2009) Wind speed prediction based on genetic neural network. In: 4th IEEE conference on industrial electronics and applications. ICIEA 2009, pp 2448–2451Google Scholar
  13. 13.
    Zhao P, Xia J, Dai Y, He J (2010) Wind speed prediction using support vector regression. In: The 5th IEEE conference on industrial electronics and applications (ICIEA), pp 882–886Google Scholar
  14. 14.
    Tarade RS, Katti PK (2011) A comparative analysis for wind speed prediction. In: 2011 International conference on energy, automation, and signal (ICEAS), pp 1–6Google Scholar
  15. 15.
    Bhaskar K, Singh SN (2012) AWNN-assisted wind power forecasting using feed-forward neural network. IEEE Trans Sustain Energy 3(2):306–315CrossRefGoogle Scholar
  16. 16.
    Nan S, Su-quan Z, Xian-hui Z, Xun-wen S, Xiao-yan Z (2013) Wind speed forecasting based on grey predictor and genetic neural network models. In: International conference on measurement, information and control (ICMIC), vol 02, pp 1479–1482Google Scholar
  17. 17.
    Chen N, Qian Z, Nabney IT, Meng X (2014) Wind power forecasts using gaussian processes and numerical weather prediction. IEEE Trans Power Syst 29(2):656–665CrossRefGoogle Scholar
  18. 18.
    Ruffing SM, Venayagamoorthy GK (2009) Short to medium range time series prediction of solar irradiance using an echo state network. In: 15th International conference on intelligent system applications to power systems. ISAP ‘09, pp 1–6Google Scholar
  19. 19.
    Naing LP, Srinivasan D (2010) Estimation of solar power generating capacity. In: IEEE 11th international conference on probabilistic methods applied to power systems (PMAPS), pp 95–100Google Scholar
  20. 20.
    Wang J, Xie Y, Zhu C, Xu X (2011) Daily solar radiation prediction based on genetic algorithm optimization of wavelet neural network. In: International conference on electrical and control engineering (ICECE), pp 602–605Google Scholar
  21. 21.
    Ji W, Chee KC (2011) Prediction of hourly solar radiation using a novel hybrid model of ARMA and TDNN. In: Solar energy vol 85(5), pp 808–817. July 14, 2014
  22. 22.
    Salcedo-Sanz S, Casanova-Mateo C, Munoz-Mari J, Camps-Valls G (2014) Prediction of daily global solar irradiation using temporal gaussian processes. IEEE Geosci Remote Sens Lett 11(11):1936–1940CrossRefGoogle Scholar
  23. 23.
    Baptista D, Abreu S, Travieso-González C, Morgado-Dias F (2016) Hardware implementation of an artificial neural network model to predict the energy production of a photovoltaic system. Microprocess Microsyst 49:77–86. CrossRefGoogle Scholar
  24. 24.
    Hernández-Travieso JG, Herrera-Jiménez AL, Travieso-González CM, Morgado-Días F, Alonso-Hernández JB, Ravelo-García AG (2017) Sustainability. vol 9, p 193.
  25. 25.
    Haykin S (1999) Neural networks. A comprehensive foundation, 2nd edn. Prentice Hall International, Inc., Upper Saddle RiverzbMATHGoogle Scholar
  26. 26.
    Li X, Zecchin AC, Maier HR (2014) Selection of smoothing parameter estimators for general regression neural networks—applications to hydrological and water resources modelling. Environ Modell Softw 59:162–186. ISSN 1364-8152.,
  27. 27.
    Bakker M, Vreeburg JHG, van Schagen KM, Rietveld LC (2013) A fully adaptive forecasting model for short-term drinking water demand. Environ Modell Softw 48:141–151. ISSN 1364-8152.,
  28. 28.
    Valverde MC, Araujo E, Velho HC (2014) Neural network and fuzzy logic statistical downscaling of atmospheric circulation-type specific weather pattern for rainfall forecasting. Appl Soft Comput 22:681–694, ISSN 1568-4946.,

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Institute for Technological Development and Innovation in Communications (IDeTIC)University of Las Palmas de Gran CanariaLas Palmas de Gran CanariaSpain
  2. 2.Signal and Communications DepartmentUniversity of Las Palmas de Gran CanariaLas Palmas de Gran CanariaSpain
  3. 3.Signal and Communications Department, Institute for Technological Development and Innovation in Communications (IDeTIC)University of Las Palmas de Gran CanariaLas Palmas de Gran CanariaSpain

Personalised recommendations