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Prediction of daily maximum temperature using a support vector regression algorithm

Author

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  • Paniagua-Tineo, A.
  • Salcedo-Sanz, S.
  • Casanova-Mateo, C.
  • Ortiz-García, E.G.
  • Cony, M.A.
  • Hernández-Martín, E.

Abstract

Daily maximum temperature can be used a good indicator of peak energy consumption, since it can be used to predict the massive use of heating or air conditioning systems. Thus, the prediction of daily maximum temperature is an important problem with interesting applications in the energy field, since it has been proven that electricity demand depends much on weather conditions. This paper presents a novel methodology for daily maximum temperature prediction, based on a Support Vector Regression approach. The paper is focused on different measuring stations in Europe, from which different meteorological variables have been obtained, including temperature, precipitation, relative humidity and air pressure. Two more variables are also included, specifically synoptic situation of the day and monthly cycle. Using this pool of prediction variables, it is shown that the SVMr algorithm is able to give an accurate prediction of the maximum temperature 24 h later. In the paper SVMr technique applied is fully described, including some bounds on the machine hyper-parameters in order to speed up the SVMr training process. The performance of the SVMr has been compared to that of different neural networks in the literature: a Multi-layer perceptron and an Extreme Learning Machine.

Suggested Citation

  • Paniagua-Tineo, A. & Salcedo-Sanz, S. & Casanova-Mateo, C. & Ortiz-García, E.G. & Cony, M.A. & Hernández-Martín, E., 2011. "Prediction of daily maximum temperature using a support vector regression algorithm," Renewable Energy, Elsevier, vol. 36(11), pages 3054-3060.
    • Handle: RePEc:eee:renene:v:36:y:2011:i:11:p:3054-3060
    DOI: 10.1016/j.renene.2011.03.030
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    References listed on IDEAS

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    3. Jenny Cifuentes & Geovanny Marulanda & Antonio Bello & Javier Reneses, 2020. "Air Temperature Forecasting Using Machine Learning Techniques: A Review," Energies, MDPI, vol. 13(16), pages 1-28, August.
    4. Foucquier, Aurélie & Robert, Sylvain & Suard, Frédéric & Stéphan, Louis & Jay, Arnaud, 2013. "State of the art in building modelling and energy performances prediction: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 272-288.
    5. Miller, J. Isaac & Nam, Kyungsik, 2022. "Modeling peak electricity demand: A semiparametric approach using weather-driven cross-temperature response functions," Energy Economics, Elsevier, vol. 114(C).
    6. Rana Muhammad Adnan & Sarita Gajbhiye Meshram & Reham R. Mostafa & Abu Reza Md. Towfiqul Islam & S. I. Abba & Francis Andorful & Zhihuan Chen, 2023. "Application of Advanced Optimized Soft Computing Models for Atmospheric Variable Forecasting," Mathematics, MDPI, vol. 11(5), pages 1-29, March.
    7. Samuel Asante Gyamerah & Victor Owusu, 2024. "Short- and long-term weather prediction based on a hybrid of CEEMDAN, LMD, and ANN," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-23, July.
    8. Giuseppina Nicolosi & Roberto Volpe & Antonio Messineo, 2017. "An Innovative Adaptive Control System to Regulate Microclimatic Conditions in a Greenhouse," Energies, MDPI, vol. 10(5), pages 1-17, May.
    9. Antonanzas-Torres, F. & Sanz-Garcia, A. & Martínez-de-Pisón, F.J. & Antonanzas, J. & Perpiñán-Lamigueiro, O. & Polo, J., 2014. "Towards downscaling of aerosol gridded dataset for improving solar resource assessment, an application to Spain," Renewable Energy, Elsevier, vol. 71(C), pages 534-544.
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