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Temperature Control by Its Forecasting Applying Score Fusion for Sustainable Development

Author

Listed:
  • José Gustavo Hernández-Travieso

    (Signal and Communications Department, 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)

  • Antonio L. Herrera-Jiménez

    (Signal and Communications Department, 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)

  • Carlos M. Travieso-González

    (Signal and Communications Department, 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)

  • Fernando Morgado-Dias

    (Madeira Interactive Technologies Institute, University of Madeira, 9020-105 Funchal, Portugal)

  • Jesús B. Alonso-Hernández

    (Signal and Communications Department, 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)

  • Antonio G. Ravelo-García

    (Signal and Communications Department, 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)

Abstract

Temperature control and its prediction has turned into a research challenge for the knowledge of the planet and its effects on different human activities and this will assure, in conjunction with energy efficiency, a sustainable development reducing CO 2 emissions and fuel consumption. This work tries to offer a practical solution to temperature forecast and control, which has been traditionally carried out by specialized institutes. For the accomplishment of temperature estimation, a score fusion block based on Artificial Neural Networks was used. The dataset is composed by data from a meteorological station, using 20,000 temperature values and 10,000 samples of several meteorological parameters. Thus, the complexity of the traditional forecasting models is resolved. As a result, a practical system has been obtained, reaching a mean squared error of 0.136 °C for short period of time prediction and 5 °C for large period of time prediction.

Suggested Citation

  • José Gustavo Hernández-Travieso & Antonio L. Herrera-Jiménez & Carlos M. Travieso-González & Fernando Morgado-Dias & Jesús B. Alonso-Hernández & Antonio G. Ravelo-García, 2017. "Temperature Control by Its Forecasting Applying Score Fusion for Sustainable Development," Sustainability, MDPI, vol. 9(2), pages 1-17, January.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:2:p:193-:d:89021
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    References listed on IDEAS

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