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Medium, short and very short-term prognosis of load demand for the Greek Island of Tilos using artificial neural networks and human thermal comfort-discomfort biometeorological data

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  • Moustris, K.
  • Kavadias, K.A.
  • Zafirakis, D.
  • Kaldellis, J.K.

Abstract

The objective of the present work is the medium, short and very short-term prognosis of load demand (LD) for the small-scale island of Tilos in Greece. For this purpose, Artificial Neural Network (ANNs) models were developed to forecast the LD of Tilos for different prediction horizons and time intervals, these covering the cases of 24 h ahead in hourly intervals (medium term prognosis), 2 h ahead in 10-min intervals (short term prognosis) and 10-min ahead in 1-min intervals (very short term prognosis). At the same time, stochastic/persistence autoregressive (AR) models were also developed and compared with the respective ANN models with regards to the LD prediction results obtained.

Suggested Citation

  • Moustris, K. & Kavadias, K.A. & Zafirakis, D. & Kaldellis, J.K., 2020. "Medium, short and very short-term prognosis of load demand for the Greek Island of Tilos using artificial neural networks and human thermal comfort-discomfort biometeorological data," Renewable Energy, Elsevier, vol. 147(P1), pages 100-109.
    • Handle: RePEc:eee:renene:v:147:y:2020:i:p1:p:100-109
    DOI: 10.1016/j.renene.2019.08.126
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    1. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
    2. John K. Kaldellis, 2021. "Supporting the Clean Electrification for Remote Islands: The Case of the Greek Tilos Island," Energies, MDPI, vol. 14(5), pages 1-22, March.
    3. Halhoul Merabet, Ghezlane & Essaaidi, Mohamed & Ben Haddou, Mohamed & Qolomany, Basheer & Qadir, Junaid & Anan, Muhammad & Al-Fuqaha, Ala & Abid, Mohamed Riduan & Benhaddou, Driss, 2021. "Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).

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