IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i2p416-d1319299.html
   My bibliography  Save this article

A Solar and Wind Energy Evaluation Methodology Using Artificial Intelligence Technologies

Author

Listed:
  • Vladimir Simankov

    (Department of Cybersecurity and Information Security, Institute of Computer Systems and Information Security, Kuban State Technological University, 350072 Krasnodar, Russia
    Deceased.)

  • Pavel Buchatskiy

    (Department of Automated Information Processing and Management Systems, Adyghe State University, 385000 Maykop, Russia)

  • Anatoliy Kazak

    (Humanitarian Pedagogical Academy, V.I. Vernadsky Crimean Federal University, 295007 Simferopol, Russia)

  • Semen Teploukhov

    (Department of Automated Information Processing and Management Systems, Adyghe State University, 385000 Maykop, Russia)

  • Stefan Onishchenko

    (Department of Automated Information Processing and Management Systems, Adyghe State University, 385000 Maykop, Russia)

  • Kirill Kuzmin

    (Department of Automated Information Processing and Management Systems, Adyghe State University, 385000 Maykop, Russia)

  • Petr Chetyrbok

    (Humanitarian Pedagogical Academy, V.I. Vernadsky Crimean Federal University, 295007 Simferopol, Russia)

Abstract

The use of renewable energy sources is becoming increasingly widespread around the world due to various factors, the most relevant of which is the high environmental friendliness of these types of energy resources. However, the large-scale involvement of green energy leads to the creation of distributed energy networks that combine several different generation methods, each of which has its own specific features, and as a result, the data collection and processing necessary to optimize the operation of such energy systems become more relevant. Development of new technologies for the more optimal use of RES is one of the main tasks of modern research in the field of energy, where an important place is assigned to the use of technologies based on artificial intelligence, allowing researchers to significantly increase the efficiency of the use of all types of RES within energy systems. This paper proposes to consider the methodology of application of modern approaches to the assessment of the amount of energy obtained from renewable energy sources based on artificial intelligence technologies, approaches used for data processing and for optimization of the control processes for operating energy systems with the integration of renewable energy sources. The relevance of the work lies in the formation of a general approach applied to the evaluation of renewable energy sources such as solar and wind energy based on the use of artificial intelligence technologies. As a verification of the approach considered by the authors, a number of models for predicting the amount of solar power generation using photovoltaic panels have been implemented, for which modern machine-learning methods have been used. As a result of testing for quality and accuracy, the best results were obtained using a hybrid forecasting model, which combines the joint use of a random forest model applied at the stage of the normalization of the input data, exponential smoothing model, and LSTM model.

Suggested Citation

  • Vladimir Simankov & Pavel Buchatskiy & Anatoliy Kazak & Semen Teploukhov & Stefan Onishchenko & Kirill Kuzmin & Petr Chetyrbok, 2024. "A Solar and Wind Energy Evaluation Methodology Using Artificial Intelligence Technologies," Energies, MDPI, vol. 17(2), pages 1-23, January.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:2:p:416-:d:1319299
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/2/416/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/2/416/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhou, Yuekuan & Zheng, Siqian & Zhang, Guoqiang, 2020. "Machine-learning based study on the on-site renewable electrical performance of an optimal hybrid PCMs integrated renewable system with high-level parameters’ uncertainties," Renewable Energy, Elsevier, vol. 151(C), pages 403-418.
    2. Billah, Baki & King, Maxwell L. & Snyder, Ralph D. & Koehler, Anne B., 2006. "Exponential smoothing model selection for forecasting," International Journal of Forecasting, Elsevier, vol. 22(2), pages 239-247.
    3. You, Minglei & Wang, Qian & Sun, Hongjian & Castro, Iván & Jiang, Jing, 2022. "Digital twins based day-ahead integrated energy system scheduling under load and renewable energy uncertainties," Applied Energy, Elsevier, vol. 305(C).
    4. Milan Belik & Olena Rubanenko, 2023. "Implementation of Digital Twin for Increasing Efficiency of Renewable Energy Sources," Energies, MDPI, vol. 16(12), pages 1-27, June.
    5. Vladimir Simankov & Pavel Buchatskiy & Semen Teploukhov & Stefan Onishchenko & Anatoliy Kazak & Petr Chetyrbok, 2023. "Review of Estimating and Predicting Models of the Wind Energy Amount," Energies, MDPI, vol. 16(16), pages 1-24, August.
    6. Ghimire, Sujan & Deo, Ravinesh C. & Raj, Nawin & Mi, Jianchun, 2019. "Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    7. Jin, Cheng & Lv, Zhiwei & Li, Zengrong & Sun, Kehan, 2023. "Green finance, renewable energy and carbon neutrality in OECD countries," Renewable Energy, Elsevier, vol. 211(C), pages 279-284.
    8. Àlex Alonso-Travesset & Diederik Coppitters & Helena Martín & Jordi de la Hoz, 2023. "Economic and Regulatory Uncertainty in Renewable Energy System Design: A Review," Energies, MDPI, vol. 16(2), pages 1-30, January.
    9. Menegaki, Angeliki, 2008. "Valuation for renewable energy: A comparative review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(9), pages 2422-2437, December.
    10. Evans, Annette & Strezov, Vladimir & Evans, Tim J., 2009. "Assessment of sustainability indicators for renewable energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 1082-1088, June.
    Full references (including those not matched with items on IDEAS)

    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. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    2. Dombi, Mihály & Kuti, István & Balogh, Péter, 2014. "Sustainability assessment of renewable power and heat generation technologies," Energy Policy, Elsevier, vol. 67(C), pages 264-271.
    3. Clayton Hunter & James G. Baldwin & Michael L. Mann, 2012. "The Financial Viability of Solar Photovoltaic Canopies as Urban Climate Change Mitigation: An Analysis of the Potential Utilization of Public Space in Boston, Massachusetts," Energy and Environment Research, Canadian Center of Science and Education, vol. 3(1), pages 158-158, June.
    4. Agnieszka Operacz, 2021. "Possibility of Hydropower Development: A Simple-to-Use Index," Energies, MDPI, vol. 14(10), pages 1-19, May.
    5. Zhou, Yuekuan & Zheng, Siqian, 2020. "Uncertainty study on thermal and energy performances of a deterministic parameters based optimal aerogel glazing system using machine-learning method," Energy, Elsevier, vol. 193(C).
    6. He, Zhaoyu & Guo, Weimin & Zhang, Peng, 2022. "Performance prediction, optimal design and operational control of thermal energy storage using artificial intelligence methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    7. 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).
    8. Anabela Botelho & Lina Sofia Lourenço-Gomes & Lígia Costa Pinto & Sara Sousa & Marieta Valente, 2016. "Accounting for local impacts of photovoltaic farms: two stated preferences approaches," NIMA Working Papers 64, Núcleo de Investigação em Microeconomia Aplicada (NIMA), Universidade do Minho.
    9. L. Hay & A. H. B. Duffy & R. I. Whitfield, 2017. "The S‐Cycle Performance Matrix: Supporting Comprehensive Sustainability Performance Evaluation of Technical Systems," Systems Engineering, John Wiley & Sons, vol. 20(1), pages 45-70, January.
    10. Emblemsvåg, Jan, 2022. "Wind energy is not sustainable when balanced by fossil energy," Applied Energy, Elsevier, vol. 305(C).
    11. Dou, Weijing & Wang, Kai & Shan, Shuo & Li, Chenxi & Wang, Yiye & Zhang, Kanjian & Wei, Haikun & Sreeram, Victor, 2024. "Day-ahead Numerical Weather Prediction solar irradiance correction using a clustering method based on weather conditions," Applied Energy, Elsevier, vol. 365(C).
    12. Huang, Shih-Chieh & Lo, Shang-Lien & Lin, Yen-Ching, 2013. "Application of a fuzzy cognitive map based on a structural equation model for the identification of limitations to the development of wind power," Energy Policy, Elsevier, vol. 63(C), pages 851-861.
    13. Fonseca, Juan D. & Commenge, Jean-Marc & Camargo, Mauricio & Falk, Laurent & Gil, Iván D., 2021. "Sustainability analysis for the design of distributed energy systems: A multi-objective optimization approach," Applied Energy, Elsevier, vol. 290(C).
    14. Gottschamer, L. & Zhang, Q., 2016. "Interactions of factors impacting implementation and sustainability of renewable energy sourced electricity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 164-174.
    15. Hong, Sanghyun & Bradshaw, Corey J.A. & Brook, Barry W., 2014. "South Korean energy scenarios show how nuclear power can reduce future energy and environmental costs," Energy Policy, Elsevier, vol. 74(C), pages 569-578.
    16. Xu, Jing & Wang, Xiaoying & Gu, Yujiong & Ma, Suxia, 2023. "A data-based day-ahead scheduling optimization approach for regional integrated energy systems with varying operating conditions," Energy, Elsevier, vol. 283(C).
    17. Philippe St-Aubin & Bruno Agard, 2022. "Precision and Reliability of Forecasts Performance Metrics," Forecasting, MDPI, vol. 4(4), pages 1-22, October.
    18. Mostafa Shaaban & Jürgen Scheffran & Jürgen Böhner & Mohamed S. Elsobki, 2018. "Sustainability Assessment of Electricity Generation Technologies in Egypt Using Multi-Criteria Decision Analysis," Energies, MDPI, vol. 11(5), pages 1-25, May.
    19. Aleksandra Matuszewska-Janica & Dorota Żebrowska-Suchodolska & Urszula Ala-Karvia & Marta Hozer-Koćmiel, 2021. "Changes in Electricity Production from Renewable Energy Sources in the European Union Countries in 2005–2019," Energies, MDPI, vol. 14(19), pages 1-27, October.
    20. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2022. "Interpretable deep learning models for hourly solar radiation prediction based on graph neural network and attention," Applied Energy, Elsevier, vol. 321(C).

    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:jeners:v:17:y:2024:i:2:p:416-:d:1319299. 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.