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Day-ahead spatio-temporal forecasting of solar irradiation along a navigation route

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  • Lan, Hai
  • Yin, He
  • Hong, Ying-Yi
  • Wen, Shuli
  • Yu, David C.
  • Cheng, Peng

Abstract

Owing to a shortage of fossil fuels and environmental pollution, renewable energy is gradually replacing fossil fuels in the power systems of hybrid ships. To exploit fully solar energy by the successful day-ahead scheduling of ships, this work proposes a new day-ahead spatio-temporal forecasting method. Ensemble empirical mode decomposition (EEMD) is used to extract data features and decompose original historical data into several frequency bands. After the original data are processed, data from the four land weather stations that are closest to the ship and self-organizing map-back propagation (SOM-BP) hybrid neural networks are used to forecast the solar radiation received by the ship in the next 24 h. Multiple comparative experiments are implemented. The results show that the EEMD-SOM-BP spatio-temporal forecasting method can accurately forecast the solar radiation on a ship that is sailing along a navigation route.

Suggested Citation

  • Lan, Hai & Yin, He & Hong, Ying-Yi & Wen, Shuli & Yu, David C. & Cheng, Peng, 2018. "Day-ahead spatio-temporal forecasting of solar irradiation along a navigation route," Applied Energy, Elsevier, vol. 211(C), pages 15-27.
    • Handle: RePEc:eee:appene:v:211:y:2018:i:c:p:15-27
    DOI: 10.1016/j.apenergy.2017.11.014
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    5. Huang, Xiaoqiao & Li, Qiong & Tai, Yonghang & Chen, Zaiqing & Zhang, Jun & Shi, Junsheng & Gao, Bixuan & Liu, Wuming, 2021. "Hybrid deep neural model for hourly solar irradiance forecasting," Renewable Energy, Elsevier, vol. 171(C), pages 1041-1060.
    6. Julián Urrego-Ortiz & J. Alejandro Martínez & Paola A. Arias & Álvaro Jaramillo-Duque, 2019. "Assessment and Day-Ahead Forecasting of Hourly Solar Radiation in Medellín, Colombia," Energies, MDPI, vol. 12(22), pages 1-29, November.
    7. Martins, Guilherme Santos & Giesbrecht, Mateus, 2023. "Hybrid approaches based on Singular Spectrum Analysis and k- Nearest Neighbors for clearness index forecasting," Renewable Energy, Elsevier, vol. 219(P1).
    8. Gao, Bixuan & Huang, Xiaoqiao & Shi, Junsheng & Tai, Yonghang & Zhang, Jun, 2020. "Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks," Renewable Energy, Elsevier, vol. 162(C), pages 1665-1683.
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    11. He Yin & Hai Lan & Ying-Yi Hong & Zhuangwei Wang & Peng Cheng & Dan Li & Dong Guo, 2023. "A Comprehensive Review of Shipboard Power Systems with New Energy Sources," Energies, MDPI, vol. 16(5), pages 1-44, February.
    12. Zhao, Ning & Su, Yi & Dai, Xianxing & Jia, Shaomin & Wang, Xuewei, 2024. "A new decomposition-ensemble strategy fusion with correntropy optimization learning algorithms for short-term wind speed prediction," Applied Energy, Elsevier, vol. 369(C).
    13. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    14. Carneiro, Tatiane C. & Rocha, Paulo A.C. & Carvalho, Paulo C.M. & Fernández-Ramírez, Luis M., 2022. "Ridge regression ensemble of machine learning models applied to solar and wind forecasting in Brazil and Spain," Applied Energy, Elsevier, vol. 314(C).
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