IDEAS home Printed from https://ideas.repec.org/a/gam/jcltec/v5y2023i2p26-517d1127160.html
   My bibliography  Save this article

Machine Learning Applications in Renewable Energy (MLARE) Research: A Publication Trend and Bibliometric Analysis Study (2012–2021)

Author

Listed:
  • Samuel-Soma M. Ajibade

    (Department of Computer Engineering, Istanbul Ticaret University, Istanbul 34445, Turkey)

  • Festus Victor Bekun

    (Faculty of Economics Administrative and Social Sciences, Istanbul Gelisim University, Istanbul 34310, Turkey
    Department of Economics, Adnan Kassar School of Business, Lebanese American University, Beirut 1102-2801, Lebanon)

  • Festus Fatai Adedoyin

    (Department of Computing and Informatics, Bournemouth University, Poole BH12 5BB, UK)

  • Bright Akwasi Gyamfi

    (School of Management, Sir Padampat Singhania University, Bhatewar, Udaipur 313601, India)

  • Anthonia Oluwatosin Adediran

    (Department of Estate Management, The Federal Polytechnic, Ado Ekiti 23401, Nigeria)

Abstract

This study examines the research climate on machine learning applications in renewable energy (MLARE). Therefore, the publication trends (PT) and bibliometric analysis (BA) on MLARE research published and indexed in the Elsevier Scopus database between 2012 and 2021 were examined. The PT was adopted to deduce the major stakeholders, top-cited publications, and funding organizations on MLARE, whereas BA elucidated critical insights into the research landscape, scientific developments, and technological growth. The PT revealed 1218 published documents comprising 46.9% articles, 39.7% conference papers, and 6.0% reviews on the topic. Subject area analysis revealed MLARE research spans the areas of science, technology, engineering, and mathematics among others, which indicates it is a broad, multidisciplinary, and impactful research topic. The most prolific researcher, affiliations, country, and funder are Ravinesh C. Deo, National Renewable Energy Laboratory, United States, and the National Natural Science Foundation of China, respectively. The most prominent journals on the top are Applied Energy and Energies, which indicates that journal reputation and open access are critical considerations for the author’s choice of publication outlet. The high productivity of the major stakeholders in MLARE is due to collaborations and research funding support. The keyword co-occurrence analysis identified four (4) clusters or thematic areas on MLARE, which broadly describe the systems, technologies, tools/technologies, and socio-technical dynamics of MLARE research. Overall, the study showed that ML is critical to the prediction, operation, and optimization of renewable energy technologies (RET) along with the design and development of RE-related materials.

Suggested Citation

  • Samuel-Soma M. Ajibade & Festus Victor Bekun & Festus Fatai Adedoyin & Bright Akwasi Gyamfi & Anthonia Oluwatosin Adediran, 2023. "Machine Learning Applications in Renewable Energy (MLARE) Research: A Publication Trend and Bibliometric Analysis Study (2012–2021)," Clean Technol., MDPI, vol. 5(2), pages 1-21, April.
    • Handle: RePEc:gam:jcltec:v:5:y:2023:i:2:p:26-517:d:1127160
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-8797/5/2/26/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-8797/5/2/26/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Helbing, Georg & Ritter, Matthias, 2018. "Deep Learning for fault detection in wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 189-198.
    2. Ahmad, Tanveer & Chen, Huanxin, 2019. "Deep learning for multi-scale smart energy forecasting," Energy, Elsevier, vol. 175(C), pages 98-112.
    3. Giovanni Abramo & Ciriaco Andrea D’Angelo & Flavia Costa, 2018. "The effect of multidisciplinary collaborations on research diversification," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 423-433, July.
    4. Jungwon Yu & June Ho Park & Sungshin Kim, 2018. "A New Input Selection Algorithm Using the Group Method of Data Handling and Bootstrap Method for Support Vector Regression Based Hourly Load Forecasting," Energies, MDPI, vol. 11(11), pages 1-20, October.
    5. Davies, Benjamin & Gush, Jason & Hendy, Shaun C. & Jaffe, Adam B., 2022. "Research funding and collaboration," Research Policy, Elsevier, vol. 51(2).
    6. Giovanni Abramo & Ciriaco Andrea D’Angelo & Flavia Costa, 2017. "Do interdisciplinary research teams deliver higher gains to science?," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 317-336, April.
    7. Sinsel, Simon R. & Riemke, Rhea L. & Hoffmann, Volker H., 2020. "Challenges and solution technologies for the integration of variable renewable energy sources—a review," Renewable Energy, Elsevier, vol. 145(C), pages 2271-2285.
    8. Ashiwani Yadav & Nitai Pal & Jagannath Patra & Monika Yadav, 2020. "Strategic planning and challenges to the deployment of renewable energy technologies in the world scenario: its impact on global sustainable development," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(1), pages 297-315, January.
    9. Adams, James D. & Black, Grant C. & Clemmons, J. Roger & Stephan, Paula E., 2005. "Scientific teams and institutional collaborations: Evidence from U.S. universities, 1981-1999," Research Policy, Elsevier, vol. 34(3), pages 259-285, April.
    10. Hua, Haochen & Qin, Yuchao & Hao, Chuantong & Cao, Junwei, 2019. "Optimal energy management strategies for energy Internet via deep reinforcement learning approach," Applied Energy, Elsevier, vol. 239(C), pages 598-609.
    11. Marek Kwiek, 2020. "Internationalists and locals: international research collaboration in a resource-poor system," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 57-105, July.
    12. Li, Liang & Yuan, Zhiming & Gao, Yan, 2018. "Maximization of energy absorption for a wave energy converter using the deep machine learning," Energy, Elsevier, vol. 165(PA), pages 340-349.
    13. del Río, Pablo & Unruh, Gregory, 2007. "Overcoming the lock-out of renewable energy technologies in Spain: The cases of wind and solar electricity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(7), pages 1498-1513, September.
    14. Paul Lehmann & Felix Creutzig & Melf-Hinrich Ehlers & Nele Friedrichsen & Clemens Heuson & Lion Hirth & Robert Pietzcker, 2012. "Carbon Lock-Out: Advancing Renewable Energy Policy in Europe," Energies, MDPI, vol. 5(2), pages 1-32, February.
    15. Bedi, Jatin & Toshniwal, Durga, 2019. "Deep learning framework to forecast electricity demand," Applied Energy, Elsevier, vol. 238(C), pages 1312-1326.
    16. Johari, Anwar & Nyakuma, Bemgba Bevan & Mohd Nor, Shadiah Husna & Mat, Ramli & Hashim, Haslenda & Ahmad, Arshad & Yamani Zakaria, Zaki & Tuan Abdullah, Tuan Amran, 2015. "The challenges and prospects of palm oil based biodiesel in Malaysia," Energy, Elsevier, vol. 81(C), pages 255-261.
    17. Cosimo Magazzino & Marco Mele & Giovanna Morelli, 2021. "The Relationship between Renewable Energy and Economic Growth in a Time of Covid-19: A Machine Learning Experiment on the Brazilian Economy," Sustainability, MDPI, vol. 13(3), pages 1-22, January.
    18. Ma, Ting & Guo, Zhixiong & Lin, Mei & Wang, Qiuwang, 2021. "Recent trends on nanofluid heat transfer machine learning research applied to renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    19. Giovanni Abramo & Ciriaco Andrea D’Angelo & Flavia Di Costa, 2019. "The collaboration behavior of top scientists," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 215-232, January.
    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. Abdelhamid Zaidi & Samuel-Soma M. Ajibade & Majd Musa & Festus Victor Bekun, 2023. "New Insights into the Research Landscape on the Application of Artificial Intelligence in Sustainable Smart Cities: A Bibliometric Mapping and Network Analysis Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 13(4), pages 287-299, July.
    2. Samuel-Soma Ajibade & Abdelhamid Zaidi & Asamh Saleh M. Al Luhayb & Anthonia Oluwatosin Adediran & Liton Chandra Voumik & Fazle Rabbi, 2023. "New Insights into the Emerging Trends Research of Machine and Deep Learning Applications in Energy Storage: A Bibliometric Analysis and Publication Trends," International Journal of Energy Economics and Policy, Econjournals, vol. 13(5), pages 303-314, September.

    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. Abramo, Giovanni & D'Angelo, Ciriaco Andrea & Di Costa, Flavia, 2019. "Diversification versus specialization in scientific research: Which strategy pays off?," Technovation, Elsevier, vol. 82, pages 51-57.
    2. Lim, Juin Yau & Safder, Usman & How, Bing Shen & Ifaei, Pouya & Yoo, Chang Kyoo, 2021. "Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model," Applied Energy, Elsevier, vol. 283(C).
    3. Ahmad, Tanveer & Chen, Huanxin, 2019. "Deep learning for multi-scale smart energy forecasting," Energy, Elsevier, vol. 175(C), pages 98-112.
    4. Md Tariqul Islam & M. J. Hossain, 2023. "Artificial Intelligence for Hosting Capacity Analysis: A Systematic Literature Review," Energies, MDPI, vol. 16(4), pages 1-33, February.
    5. Zhao, Congyu & Dong, Kangyin & Jiang, Hong-Dian & Wang, Kun & Dong, Xiucheng, 2023. "How does energy poverty eradication realize the path to carbon unlocking? The case of China," Energy Economics, Elsevier, vol. 121(C).
    6. Jun Hao & Xiaolei Sun & Qianqian Feng, 2020. "A Novel Ensemble Approach for the Forecasting of Energy Demand Based on the Artificial Bee Colony Algorithm," Energies, MDPI, vol. 13(3), pages 1-25, January.
    7. Ma, Shuaiyin & Zhang, Yingfeng & Lv, Jingxiang & Ge, Yuntian & Yang, Haidong & Li, Lin, 2020. "Big data driven predictive production planning for energy-intensive manufacturing industries," Energy, Elsevier, vol. 211(C).
    8. Yinqi Ma & Qi Xiu & Lingzhi Shao & Hao Yao, 2022. "Promoting the Sustainable Improvement of Educational Empirical Research Quality: What Kinds of Collaborative Production Relationships Make Sense?," Sustainability, MDPI, vol. 14(6), pages 1-23, March.
    9. Lima, Marcello Anderson F.B. & Carvalho, Paulo C.M. & Fernández-Ramírez, Luis M. & Braga, Arthur P.S., 2020. "Improving solar forecasting using Deep Learning and Portfolio Theory integration," Energy, Elsevier, vol. 195(C).
    10. Zapata, Sebastian & Castaneda, Monica & Aristizabal, Andres J. & Dyner, Isaac, 2022. "Renewables for supporting supply adequacy in Colombia," Energy, Elsevier, vol. 239(PC).
    11. Abdeshahian, Peyman & Lim, Jeng Shiun & Ho, Wai Shin & Hashim, Haslenda & Lee, Chew Tin, 2016. "Potential of biogas production from farm animal waste in Malaysia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 714-723.
    12. Shufang Huang & Jin Chen & Liang Mei & Weiqiao Mo, 2019. "The Effect of Heterogeneity and Leadership on Innovation Performance: Evidence from University Research Teams in China," Sustainability, MDPI, vol. 11(16), pages 1-14, August.
    13. Marian-Gabriel Hâncean & Matjaž Perc & Jürgen Lerner, 2021. "The coauthorship networks of the most productive European researchers," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 201-224, January.
    14. Wang, Mingtao & Zhang, Juan & Liu, Huanwei, 2022. "Thermodynamic analysis and optimization of two low-grade energy driven transcritical CO2 combined cooling, heating and power systems," Energy, Elsevier, vol. 249(C).
    15. Bruno Cárdenas & Lawrie Swinfen-Styles & James Rouse & Seamus D. Garvey, 2021. "Short-, Medium-, and Long-Duration Energy Storage in a 100% Renewable Electricity Grid: A UK Case Study," Energies, MDPI, vol. 14(24), pages 1-28, December.
    16. Prince Waqas Khan & Yung-Cheol Byun & Sang-Joon Lee & Dong-Ho Kang & Jin-Young Kang & Hae-Su Park, 2020. "Machine Learning-Based Approach to Predict Energy Consumption of Renewable and Nonrenewable Power Sources," Energies, MDPI, vol. 13(18), pages 1-16, September.
    17. Marek Kwiek & Wojciech Roszka, 2022. "Academic vs. biological age in research on academic careers: a large-scale study with implications for scientifically developing systems," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3543-3575, June.
    18. Carillo, Maria Rosaria & Papagni, Erasmo & Sapio, Alessandro, 2013. "Do collaborations enhance the high-quality output of scientific institutions? Evidence from the Italian Research Assessment Exercise," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 47(C), pages 25-36.
    19. Laurent R. Bergé, 2017. "Network proximity in the geography of research collaboration," Papers in Regional Science, Wiley Blackwell, vol. 96(4), pages 785-815, November.
    20. Gregorio González-Alcaide, 2021. "Bibliometric studies outside the information science and library science field: uncontainable or uncontrollable?," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6837-6870, August.

    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:gam:jcltec:v:5:y:2023:i:2:p:26-517:d:1127160. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.