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Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition

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Cited by:

  1. Shankar Subramaniam & Naveenkumar Raju & Abbas Ganesan & Nithyaprakash Rajavel & Maheswari Chenniappan & Chander Prakash & Alokesh Pramanik & Animesh Kumar Basak & Saurav Dixit, 2022. "Artificial Intelligence Technologies for Forecasting Air Pollution and Human Health: A Narrative Review," Sustainability, MDPI, vol. 14(16), pages 1-36, August.
  2. Ali, Mumtaz & Prasad, Ramendra & Xiang, Yong & Deo, Ravinesh C., 2020. "Near real-time significant wave height forecasting with hybridized multiple linear regression algorithms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
  3. Rana Muhammad Adnan Ikram & Xinyi Cao & Kulwinder Singh Parmar & Ozgur Kisi & Shamsuddin Shahid & Mohammad Zounemat-Kermani, 2023. "Modeling Significant Wave Heights for Multiple Time Horizons Using Metaheuristic Regression Methods," Mathematics, MDPI, vol. 11(14), pages 1-24, July.
  4. Pang, Junheng & Dong, Sheng, 2023. "A novel multivariable hybrid model to improve short and long-term significant wave height prediction," Applied Energy, Elsevier, vol. 351(C).
  5. Yuchen Zhang & Zhenquan Zhang & Jun Wang & Jian Qin & Shuting Huang & Gang Xue & Yanjun Liu, 2024. "Research on Excitation Estimation for Ocean Wave Energy Generators Based on Extended Kalman Filtering," Energies, MDPI, vol. 17(3), pages 1-17, February.
  6. Karbasi, Masoud & Jamei, Mehdi & Malik, Anurag & Kisi, Ozgur & Yaseen, Zaher Mundher, 2023. "Multi-steps drought forecasting in arid and humid climate environments: Development of integrative machine learning model," Agricultural Water Management, Elsevier, vol. 281(C).
  7. Daniel Clemente & Felipe Teixeira-Duarte & Paulo Rosa-Santos & Francisco Taveira-Pinto, 2023. "Advancements on Optimization Algorithms Applied to Wave Energy Assessment: An Overview on Wave Climate and Energy Resource," Energies, MDPI, vol. 16(12), pages 1-28, June.
  8. Huang, Weinan & Dong, Sheng, 2021. "Improved short-term prediction of significant wave height by decomposing deterministic and stochastic components," Renewable Energy, Elsevier, vol. 177(C), pages 743-758.
  9. Mahdavi-Meymand, Amin & Sulisz, Wojciech, 2023. "Application of nested artificial neural network for the prediction of significant wave height," Renewable Energy, Elsevier, vol. 209(C), pages 157-168.
  10. González-Cabrera, Nestor & Ortiz-Bejar, Jose & Zamora-Mendez, Alejandro & Arrieta Paternina, Mario R., 2021. "On the Improvement of representative demand curves via a hierarchical agglomerative clustering for power transmission network investment," Energy, Elsevier, vol. 222(C).
  11. Wang, Huaizhi & Xue, Wenli & Liu, Yitao & Peng, Jianchun & Jiang, Hui, 2020. "Probabilistic wind power forecasting based on spiking neural network," Energy, Elsevier, vol. 196(C).
  12. Ali, Mumtaz & Prasad, Ramendra & Jamei, Mehdi & Malik, Anurag & Xiang, Yong & Abdulla, Shahab & Deo, Ravinesh C. & Farooque, Aitazaz A. & Labban, Abdulhaleem H., 2024. "Short-term wave power forecasting with hybrid multivariate variational mode decomposition model integrated with cascaded feedforward neural networks," Renewable Energy, Elsevier, vol. 221(C).
  13. Elham M. Al-Ali & Yassine Hajji & Yahia Said & Manel Hleili & Amal M. Alanzi & Ali H. Laatar & Mohamed Atri, 2023. "Solar Energy Production Forecasting Based on a Hybrid CNN-LSTM-Transformer Model," Mathematics, MDPI, vol. 11(3), pages 1-19, January.
  14. Khan, Zulfiqar Ahmad & Khan, Shabbir Ahmad & Hussain, Tanveer & Baik, Sung Wook, 2024. "DSPM: Dual sequence prediction model for efficient energy management in micro-grid," Applied Energy, Elsevier, vol. 356(C).
  15. Ali, Mumtaz & Prasad, Ramendra & Xiang, Yong & Sankaran, Adarsh & Deo, Ravinesh C. & Xiao, Fuyuan & Zhu, Shuyu, 2021. "Advanced extreme learning machines vs. deep learning models for peak wave energy period forecasting: A case study in Queensland, Australia," Renewable Energy, Elsevier, vol. 177(C), pages 1031-1044.
  16. Gao, Ruobin & Li, Ruilin & Hu, Minghui & Suganthan, Ponnuthurai Nagaratnam & Yuen, Kum Fai, 2023. "Dynamic ensemble deep echo state network for significant wave height forecasting," Applied Energy, Elsevier, vol. 329(C).
  17. Zhao, Lingxiao & Li, Zhiyang & Pei, Yuguo & Qu, Leilei, 2024. "Disentangled Seasonal-Trend representation of improved CEEMD-GRU joint model with entropy-driven reconstruction to forecast significant wave height," Renewable Energy, Elsevier, vol. 226(C).
  18. Saqib Iqbal & Kamyar Mehran, 2024. "Data-Driven Management Systems for Wave-Powered Renewable Energy Communities," Energies, MDPI, vol. 17(5), pages 1-19, March.
  19. Chi, Lixun & Su, Huai & Zio, Enrico & Qadrdan, Meysam & Li, Xueyi & Zhang, Li & Fan, Lin & Zhou, Jing & Yang, Zhaoming & Zhang, Jinjun, 2021. "Data-driven reliability assessment method of Integrated Energy Systems based on probabilistic deep learning and Gaussian mixture Model-Hidden Markov Model," Renewable Energy, Elsevier, vol. 174(C), pages 952-970.
  20. Ali, Mumtaz & Prasad, Ramendra & Xiang, Yong & Jamei, Mehdi & Yaseen, Zaher Mundher, 2023. "Ensemble robust local mean decomposition integrated with random forest for short-term significant wave height forecasting," Renewable Energy, Elsevier, vol. 205(C), pages 731-746.
  21. Elham Alzain & Shaha Al-Otaibi & Theyazn H. H. Aldhyani & Ali Saleh Alshebami & Mohammed Amin Almaiah & Mukti E. Jadhav, 2023. "Revolutionizing Solar Power Production with Artificial Intelligence: A Sustainable Predictive Model," Sustainability, MDPI, vol. 15(10), pages 1-21, May.
  22. Zhang, Zhenquan & Qin, Jian & Wang, Dengshuai & Wang, Wei & Liu, Yanjun & Xue, Gang, 2023. "Research on wave excitation estimators for arrays of wave energy converters," Energy, Elsevier, vol. 264(C).
  23. Meng, Anbo & Zhu, Zibin & Deng, Weisi & Ou, Zuhong & Lin, Shan & Wang, Chenen & Xu, Xuancong & Wang, Xiaolin & Yin, Hao & Luo, Jianqiang, 2022. "A novel wind power prediction approach using multivariate variational mode decomposition and multi-objective crisscross optimization based deep extreme learning machine," Energy, Elsevier, vol. 260(C).
  24. Izanloo, Milad & Aslani, Alireza & Zahedi, Rahim, 2022. "Development of a Machine learning assessment method for renewable energy investment decision making," Applied Energy, Elsevier, vol. 327(C).
  25. Prasad, Ramendra & Ali, Mumtaz & Xiang, Yong & Khan, Huma, 2020. "A double decomposition-based modelling approach to forecast weekly solar radiation," Renewable Energy, Elsevier, vol. 152(C), pages 9-22.
  26. Nguyen, Hoang & Bui, Xuan-Nam & Topal, Erkan, 2023. "Reliability and availability artificial intelligence models for predicting blast-induced ground vibration intensity in open-pit mines to ensure the safety of the surroundings," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
  27. Antonio Manuel Gómez-Orellana & Juan Carlos Fernández & Manuel Dorado-Moreno & Pedro Antonio Gutiérrez & César Hervás-Martínez, 2021. "Building Suitable Datasets for Soft Computing and Machine Learning Techniques from Meteorological Data Integration: A Case Study for Predicting Significant Wave Height and Energy Flux," Energies, MDPI, vol. 14(2), pages 1-33, January.
  28. Fu, Yang & Ying, Feixiang & Huang, Lingling & Liu, Yang, 2023. "Multi-step-ahead significant wave height prediction using a hybrid model based on an innovative two-layer decomposition framework and LSTM," Renewable Energy, Elsevier, vol. 203(C), pages 455-472.
  29. Yang, Shaobo & Deng, Zegui & Li, Xingfei & Zheng, Chongwei & Xi, Lintong & Zhuang, Jucheng & Zhang, Zhenquan & Zhang, Zhiyou, 2021. "A novel hybrid model based on STL decomposition and one-dimensional convolutional neural networks with positional encoding for significant wave height forecast," Renewable Energy, Elsevier, vol. 173(C), pages 531-543.
  30. Penalba, Markel & Aizpurua, Jose Ignacio & Martinez-Perurena, Ander & Iglesias, Gregorio, 2022. "A data-driven long-term metocean data forecasting approach for the design of marine renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
  31. Zheng, Zihao & Ali, Mumtaz & Jamei, Mehdi & Xiang, Yong & Abdulla, Shahab & Yaseen, Zaher Mundher & Farooque, Aitazaz A., 2023. "Multivariate data decomposition based deep learning approach to forecast one-day ahead significant wave height for ocean energy generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
  32. Yin, Hao & Ou, Zuhong & Huang, Shengquan & Meng, Anbo, 2019. "A cascaded deep learning wind power prediction approach based on a two-layer of mode decomposition," Energy, Elsevier, vol. 189(C).
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