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Forecast Horizon and Solar Variability Influences on the Performances of Multiscale Hybrid Forecast Model

Author

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  • Stéphanie Monjoly

    (EA 4935 LaRGE, Laboratoire de Recherche en Géosciences et Énergies, Université des Antilles, 97170 Pointe-á-Pitre, France)

  • Maina André

    (EA 4935 LaRGE, Laboratoire de Recherche en Géosciences et Énergies, Université des Antilles, 97170 Pointe-á-Pitre, France)

  • Rudy Calif

    (EA 4935 LaRGE, Laboratoire de Recherche en Géosciences et Énergies, Université des Antilles, 97170 Pointe-á-Pitre, France)

  • Ted Soubdhan

    (EA 4935 LaRGE, Laboratoire de Recherche en Géosciences et Énergies, Université des Antilles, 97170 Pointe-á-Pitre, France)

Abstract

The tropical insular region is characterized by a large diversity of microclimates and land/sea contrasts, creating a challenging solar forecasting. Therefore, it is necessary to develop and use performant and robustness forecasting techniques. This paper examines the predictive performance of a novel solar forecasting approach, the multiscale hybrid forecast model (MHFM), as a function of several parameters. The MHFM model is a technique recently used for irradiance forecasting based on a hybrid autoregressive (AR) and neural network (NN) model combined with multiscale decomposition methods. This technique presents a relevant performance for 1 h ahead global horizontal irradiance forecast. The goal of this work is to highlight the strength and limits of this model by assessing the influence of different parameters from a metric error analysis. This study illustrates modeling process performance as a function of daily insolation conditions and testifies the influence of learning data and test data time scales. Several forecast horizon strategies and their influence on the MHFM performance were investigated. With the best strategy, a rRMSE value from 4.43 % to 10.24 % was obtained for forecast horizons from 5 min to 6 h. The analysis of intra-day solar resource variability showed that the best performance of MHFM was obtained for clear sky days with a rRMSE of 2.91 % and worst for cloudy sky days with a rRMSE of 6.73 % . These works constitute an additional analysis in agreement with the literature about influence of daily insolation conditions and horizons time scales on modeling process.

Suggested Citation

  • Stéphanie Monjoly & Maina André & Rudy Calif & Ted Soubdhan, 2019. "Forecast Horizon and Solar Variability Influences on the Performances of Multiscale Hybrid Forecast Model," Energies, MDPI, vol. 12(12), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2264-:d:239520
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    References listed on IDEAS

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    Cited by:

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    2. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    3. Joseph Ndong & Ted Soubdhan, 2022. "Extracting Statistical Properties of Solar and Photovoltaic Power Production for the Scope of Building a Sophisticated Forecasting Framework," Forecasting, MDPI, vol. 5(1), pages 1-21, December.
    4. Fateh Mehazzem & Maina André & Rudy Calif, 2022. "Efficient Output Photovoltaic Power Prediction Based on MPPT Fuzzy Logic Technique and Solar Spatio-Temporal Forecasting Approach in a Tropical Insular Region," Energies, MDPI, vol. 15(22), pages 1-21, November.
    5. Monirul Islam Miskat & Protap Sarker & Hemal Chowdhury & Tamal Chowdhury & Md Salman Rahman & Nazia Hossain & Piyal Chowdhury & Sadiq M. Sait, 2023. "Current Scenario of Solar Energy Applications in Bangladesh: Techno-Economic Perspective, Policy Implementation, and Possibility of the Integration of Artificial Intelligence," Energies, MDPI, vol. 16(3), pages 1-27, February.
    6. Jizhong Xue & Zaohui Kang & Chun Sing Lai & Yu Wang & Fangyuan Xu & Haoliang Yuan, 2023. "Distributed Generation Forecasting Based on Rolling Graph Neural Network (ROLL-GNN)," Energies, MDPI, vol. 16(11), pages 1-18, May.

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