IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v132y2020ics136403212030294x.html
   My bibliography  Save this article

Near real-time significant wave height forecasting with hybridized multiple linear regression algorithms

Author

Listed:
  • Ali, Mumtaz
  • Prasad, Ramendra
  • Xiang, Yong
  • Deo, Ravinesh C.

Abstract

Globally, major emphasis is currently being put in utilization and optimization of more sustainable and renewable energy resources, to overcome the future energy demand issues and potential energy crises due to many socioeconomic factors. A near-real-time i.e., half-hourly significant wave height (Hsig) forecast model is designed using a suite of selected model input variables where the multiple linear regression (MLR) model, considering the influence of several variables, is optimized by covariance-weighted least squares (CWLS) estimation algorithm to generate a hybridized MLR-CWLS model with a capability to forecast 30-min ahead Hsig values. First, a diagnostic statistical test based on the correlation coefficient is performed to determine relationships between inputs denoting historical behaviour and the target (Hsig) at one lag of 30-min (t – 1) scale. Subsequently, the data are split into training and testing subsets, following a normalization process, and the MLR-CWLS hybridized model is then trained and validated on the testing dataset adopted from eastern coastal zones of Australia that has a high potential for wave energy generation. Hybridized MLR-CWLS model is benchmarked against competing modelling approaches (multivariate adaptive regression splines-MARS, M5 Model Tree, and MLR) via statistical score metrics. The results show that the hybridized MLR-CWLS model is able to generate reliable forecasts of Hsig relative to the counterpart comparison models. The study ascertains the practical utility of the hybridized MLR-CWLS model for Hsig modelling with significant implications for its potential application in wave and ocean energy generation systems, and some of the other renewable and sustainable energy resource management.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:rensus:v:132:y:2020:i:c:s136403212030294x
    DOI: 10.1016/j.rser.2020.110003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S136403212030294X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2020.110003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Nguyen-Huy, Thong & Deo, Ravinesh C. & An-Vo, Duc-Anh & Mushtaq, Shahbaz & Khan, Shahjahan, 2017. "Copula-statistical precipitation forecasting model in Australia’s agro-ecological zones," Agricultural Water Management, Elsevier, vol. 191(C), pages 153-172.
    2. Beck, Nathaniel & Katz, Jonathan N., 1995. "What To Do (and Not to Do) with Time-Series Cross-Section Data," American Political Science Review, Cambridge University Press, vol. 89(3), pages 634-647, September.
    3. Ali, Mumtaz & Prasad, Ramendra, 2019. "Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 281-295.
    4. Deo, Ravinesh C. & Wen, Xiaohu & Qi, Feng, 2016. "A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset," Applied Energy, Elsevier, vol. 168(C), pages 568-593.
    5. Cuadra, L. & Salcedo-Sanz, S. & Nieto-Borge, J.C. & Alexandre, E. & Rodríguez, G., 2016. "Computational intelligence in wave energy: Comprehensive review and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1223-1246.
    6. Nicola Orsini & Rino Bellocco & Sander Greenland, 2006. "Generalized least squares for trend estimation of summarized dose–response data," Stata Journal, StataCorp LP, vol. 6(1), pages 40-57, March.
    7. Deo, Ravinesh C. & Şahin, Mehmet, 2017. "Forecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in Queensland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 828-848.
    8. Uihlein, Andreas & Magagna, Davide, 2016. "Wave and tidal current energy – A review of the current state of research beyond technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1070-1081.
    9. Ali Rahimikhoob & Maryam Asadi & Mahmood Mashal, 2013. "A Comparison Between Conventional and M5 Model Tree Methods for Converting Pan Evaporation to Reference Evapotranspiration for Semi-Arid Region," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(14), pages 4815-4826, November.
    10. Langhamer, Olivia & Haikonen, Kalle & Sundberg, Jan, 2010. "Wave power--Sustainable energy or environmentally costly? A review with special emphasis on linear wave energy converters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(4), pages 1329-1335, May.
    11. Gunn, Kester & Stock-Williams, Clym, 2012. "Quantifying the global wave power resource," Renewable Energy, Elsevier, vol. 44(C), pages 296-304.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Ngoc-Lan Huynh, Anh & Deo, Ravinesh C. & Ali, Mumtaz & Abdulla, Shahab & Raj, Nawin, 2021. "Novel short-term solar radiation hybrid model: Long short-term memory network integrated with robust local mean decomposition," Applied Energy, Elsevier, vol. 298(C).
    3. 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.
    4. 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).
    5. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    6. Chengcheng Gu & Hua Li, 2022. "Review on Deep Learning Research and Applications in Wind and Wave Energy," Energies, MDPI, vol. 15(4), pages 1-19, February.
    7. 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).
    8. 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).
    9. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ali, Mumtaz & Prasad, Ramendra, 2019. "Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 281-295.
    2. 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.
    3. Keiner, Dominik & Salcedo-Puerto, Orlando & Immonen, Ekaterina & van Sark, Wilfried G.J.H.M. & Nizam, Yoosuf & Shadiya, Fathmath & Duval, Justine & Delahaye, Timur & Gulagi, Ashish & Breyer, Christian, 2022. "Powering an island energy system by offshore floating technologies towards 100% renewables: A case for the Maldives," Applied Energy, Elsevier, vol. 308(C).
    4. 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.
    5. Deo, Ravinesh C. & Şahin, Mehmet & Adamowski, Jan F. & Mi, Jianchun, 2019. "Universally deployable extreme learning machines integrated with remotely sensed MODIS satellite predictors over Australia to forecast global solar radiation: A new approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 235-261.
    6. Wang, Liguo & Isberg, Jan & Tedeschi, Elisabetta, 2018. "Review of control strategies for wave energy conversion systems and their validation: the wave-to-wire approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 366-379.
    7. Foteinis, S. & Tsoutsos, T., 2017. "Strategies to improve sustainability and offset the initial high capital expenditure of wave energy converters (WECs)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 775-785.
    8. Feiyan Chen & Zhigao Zhou & Aiwen Lin & Jiqiang Niu & Wenmin Qin & Zhong Yang, 2019. "Evaluation of Direct Horizontal Irradiance in China Using a Physically-Based Model and Machine Learning Methods," Energies, MDPI, vol. 12(1), pages 1-19, January.
    9. Ghimire, Sujan & Deo, Ravinesh C. & Raj, Nawin & Mi, Jianchun, 2019. "Wavelet-based 3-phase hybrid SVR model trained with satellite-derived predictors, particle swarm optimization and maximum overlap discrete wavelet transform for solar radiation prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    10. Francisco Francisco & Jennifer Leijon & Cecilia Boström & Jens Engström & Jan Sundberg, 2018. "Wave Power as Solution for Off-Grid Water Desalination Systems: Resource Characterization for Kilifi-Kenya," Energies, MDPI, vol. 11(4), pages 1-14, April.
    11. Rosa-Santos, Paulo & Taveira-Pinto, Francisco & Rodríguez, Claudio A. & Ramos, Victor & López, Mario, 2019. "The CECO wave energy converter: Recent developments," Renewable Energy, Elsevier, vol. 139(C), pages 368-384.
    12. Mohanad S. Al-Musaylh & Ravinesh C. Deo & Yan Li, 2020. "Electrical Energy Demand Forecasting Model Development and Evaluation with Maximum Overlap Discrete Wavelet Transform-Online Sequential Extreme Learning Machines Algorithms," Energies, MDPI, vol. 13(9), pages 1-19, May.
    13. Clemente, D. & Rosa-Santos, P. & Taveira-Pinto, F., 2021. "On the potential synergies and applications of wave energy converters: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    14. 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.
    15. Anh Ngoc-Lan Huynh & Ravinesh C. Deo & Duc-Anh An-Vo & Mumtaz Ali & Nawin Raj & Shahab Abdulla, 2020. "Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network," Energies, MDPI, vol. 13(14), pages 1-30, July.
    16. Qin, Wenmin & Wang, Lunche & Lin, Aiwen & Zhang, Ming & Xia, Xiangao & Hu, Bo & Niu, Zigeng, 2018. "Comparison of deterministic and data-driven models for solar radiation estimation in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 579-594.
    17. Fadaeenejad, M. & Shamsipour, R. & Rokni, S.D. & Gomes, C., 2014. "New approaches in harnessing wave energy: With special attention to small islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 345-354.
    18. 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).
    19. Carrelhas, A.A.D. & Gato, L.M.C. & Falcão, A.F.O. & Henriques, J.C.C., 2021. "Control law design for the air-turbine-generator set of a fully submerged 1.5 MW mWave prototype. Part 2: Experimental validation," Renewable Energy, Elsevier, vol. 171(C), pages 1002-1013.
    20. Faedo, Nicolás & Peña-Sanchez, Yerai & Pasta, Edoardo & Papini, Guglielmo & Mosquera, Facundo D. & Ferri, Francesco, 2023. "SWELL: An open-access experimental dataset for arrays of wave energy conversion systems," Renewable Energy, Elsevier, vol. 212(C), pages 699-716.

    More about this item

    Keywords

    Wave energy; Significant wave height; MLR; CWLS; MARS; M5 tree;
    All these keywords.

    JEL classification:

    • M5 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:rensus:v:132:y:2020:i:c:s136403212030294x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.