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Feasibility of Different Weather Data Sources Applied to Building Indoor Temperature Estimation Using LSTM Neural Networks

Author

Listed:
  • Martín Pensado-Mariño

    (GTE Research Group, Department of Mechanical Engineering, Heat Engines and Fluids Mechanics, Industrial Engineering School, University of Vigo, Maxwell, s/n, 36310 Vigo, Spain)

  • Lara Febrero-Garrido

    (Defense University Center, Spanish Naval Academy, Plaza de España s/n, 36920 Marín, Spain)

  • Pablo Eguía-Oller

    (GTE Research Group, Department of Mechanical Engineering, Heat Engines and Fluids Mechanics, Industrial Engineering School, University of Vigo, Maxwell, s/n, 36310 Vigo, Spain)

  • Enrique Granada-Álvarez

    (GTE Research Group, Department of Mechanical Engineering, Heat Engines and Fluids Mechanics, Industrial Engineering School, University of Vigo, Maxwell, s/n, 36310 Vigo, Spain)

Abstract

The use of Machine Learning models is becoming increasingly widespread to assess energy performance of a building. In these models, the accuracy of the results depends largely on outdoor conditions. However, getting these data on-site is not always feasible. This article compares the temperature results obtained for an LSTM neural network model, using four types of meteorological data sources. The first is the monitoring carried out in the building; the second is a meteorological station near the site of the building; the third is a table of meteorological data obtained through a kriging process and the fourth is a dataset obtained using GFS. The results are analyzed using the CV(RSME) and NMBE indices. Based on these indices, in the four series, a CV(RSME) slightly higher than 3% is obtained, while the NMBE is below 1%, so it can be deduced that the sources used are interchangeable.

Suggested Citation

  • Martín Pensado-Mariño & Lara Febrero-Garrido & Pablo Eguía-Oller & Enrique Granada-Álvarez, 2021. "Feasibility of Different Weather Data Sources Applied to Building Indoor Temperature Estimation Using LSTM Neural Networks," Sustainability, MDPI, vol. 13(24), pages 1-15, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:24:p:13735-:d:701141
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    References listed on IDEAS

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