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New approach to estimate 5-min global solar irradiation data on tilted planes from horizontal measurement

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  • Takilalte, Abdelatif
  • Harrouni, Samia
  • Yaiche, Mohamed Rédha
  • Mora-López, Llanos

Abstract

This paper presents a new methodology to estimate global tilted irradiation in 5-min steps using only global irradiation on the horizontal plane. The methodology is based on a combination of the two well-known conventional Perrin Brichambaut and Liu and Jordan models. Tilted irradiation is of great importance for the design and short-term performance assessment of fixed-tilt flat-plate collectors and photovoltaic (PV) systems. Intermediate key parameters of the state of the sky, referred to here as cloudiness factors, are determined and introduced to transform isotropic models into an anisotropic model. The results of the proposed model for all sky conditions with regard to normalized root mean square error (nRMSE), relative percentage error (RPE), normalized mean absolute error (nMAE) and coefficient of correlation (R2) range from 4.7 to 6.41%, 5.5–5.9%, 3.07–4.73% and 0.97 to 0.99, respectively, which are very accurate results, especially for such a short time step. A comparison with the best conventional models and even artificial neural network (ANN) models described in the literature has confirmed that the developed model has smaller errors.

Suggested Citation

  • Takilalte, Abdelatif & Harrouni, Samia & Yaiche, Mohamed Rédha & Mora-López, Llanos, 2020. "New approach to estimate 5-min global solar irradiation data on tilted planes from horizontal measurement," Renewable Energy, Elsevier, vol. 145(C), pages 2477-2488.
    • Handle: RePEc:eee:renene:v:145:y:2020:i:c:p:2477-2488
    DOI: 10.1016/j.renene.2019.07.165
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    Cited by:

    1. Rodríguez, Fermín & Martín, Fernando & Fontán, Luis & Galarza, Ainhoa, 2021. "Ensemble of machine learning and spatiotemporal parameters to forecast very short-term solar irradiation to compute photovoltaic generators’ output power," Energy, Elsevier, vol. 229(C).
    2. Fernando Venâncio Mucomole & Carlos Augusto Santos Silva & Lourenço Lázaro Magaia, 2024. "Regressive and Spatio-Temporal Accessibility of Variability in Solar Energy on a Short Scale Measurement in the Southern and Mid Region of Mozambique," Energies, MDPI, vol. 17(11), pages 1-29, May.
    3. Ramez Abdallah & Emad Natsheh & Adel Juaidi & Sufyan Samara & Francisco Manzano-Agugliaro, 2020. "A Multi-Level World Comprehensive Neural Network Model for Maximum Annual Solar Irradiation on a Flat Surface," Energies, MDPI, vol. 13(23), pages 1-31, December.

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