IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i20p5380-d1769996.html

Forecasting the Development of Offshore Wind Energy in Poland in the Context of the Energy Transformation and Sustainable Development Goals

Author

Listed:
  • Aurelia Rybak

    (Faculty of Mining, Safety Engineering and Industrial Automation, Silesian University of Technology, 44-100 Gliwice, Poland)

  • Aleksandra Rybak

    (Department of Physical Chemistry and Technology of Polymers, Faculty of Chemistry, Silesian University of Technology, Strzody 7, 44-100 Gliwice, Poland)

  • Spas D. Kolev

    (School of Chemistry, The University of Melbourne, Melbourne, VIC 3010, Australia
    Department of Chemical Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
    Faculty of Chemistry and Pharmacy, Sofia University “St. Kl. Ohridski”, 1 James Bourchier Blvd., 1164 Sofia, Bulgaria)

Abstract

This article presents the results of research on the potential development of offshore wind energy in Poland. Wind energy generated in offshore farms is intended to be the second pillar (alongside nuclear power) of Poland’s energy transition, creating the foundation for a zero-emission energy system. The authors constructed a neural network that allowed them to forecast the development of the installed offshore energy capacity for Poland by 2030. For this purpose, the factors that have the greatest impact on the development of wind energy in Poland were identified. This knowledge will facilitate the development of state policy consistent with the Sustainable Development Goals (SDGs) and the European Green Deal. Since Poland currently does not have installed offshore wind energy capacity, Germany was used as a benchmark to train the model. The research results fill the identified gap: to date, forecasts of offshore development in Poland based on a model trained on German data have not been presented in the literature. The research results show that by 2030, Poland can achieve the goals set by the United Nations, the European Union, and the Polish Energy Policy 2040 (PEP2040). The PEP2040 assumes that Poland should have 5.9 GW of energy installed in offshore wind farms in the Baltic Sea by 2030. The forecast indicates that this will be approximately 5.3 GW, with the difference between these values remaining within the model’s margin of error.

Suggested Citation

  • Aurelia Rybak & Aleksandra Rybak & Spas D. Kolev, 2025. "Forecasting the Development of Offshore Wind Energy in Poland in the Context of the Energy Transformation and Sustainable Development Goals," Energies, MDPI, vol. 18(20), pages 1-21, October.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:20:p:5380-:d:1769996
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/20/5380/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/20/5380/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sungkyun Ha & Sungho Tae & Rakhyun Kim, 2019. "Energy Demand Forecast Models for Commercial Buildings in South Korea," Energies, MDPI, vol. 12(12), pages 1-19, June.
    2. Almorox, J. & Benito, M. & Hontoria, C., 2005. "Estimation of monthly Angström–Prescott equation coefficients from measured daily data in Toledo, Spain," Renewable Energy, Elsevier, vol. 30(6), pages 931-936.
    3. Dohyun Kim & Chungmok Lee & Sangheum Hwang & Myong K Jeong, 2016. "A robust support vector regression with a linear-log concave loss function," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(5), pages 735-742, May.
    4. Martyna Kubiak & Artur Bugała & Dorota Bugała & Wojciech Czekała, 2025. "Simulation Analysis of Onshore and Offshore Wind Farms’ Generation Potential for Polish Climatic Conditions," Energies, MDPI, vol. 18(15), pages 1-42, August.
    5. Daniel Navia Simon & Laura Diaz Anadon, 2025. "Power price stability and the insurance value of renewable technologies," Nature Energy, Nature, vol. 10(3), pages 329-341, March.
    6. Zambrano-Monserrate, Manuel A. & Soto, Gonzalo Hernández & Ahakwa, Isaac & Manigandan, Palanisamy, 2024. "Dynamic effects on modern renewable energy generation: The role of patents in clean energy technology," Energy, Elsevier, vol. 311(C).
    7. Anca Mehedintu & Mihaela Sterpu & Georgeta Soava, 2018. "Estimation and Forecasts for the Share of Renewable Energy Consumption in Final Energy Consumption by 2020 in the European Union," Sustainability, MDPI, vol. 10(5), pages 1-22, May.
    8. Patankar, Neha & Fell, Harrison G. & Rodrigo de Queiroz, Anderson & Curtis, John & DeCarolis, Joseph F., 2022. "Improving the representation of energy efficiency in an energy system optimization model," Applied Energy, Elsevier, vol. 306(PB).
    9. Cassola, Federico & Burlando, Massimiliano, 2012. "Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output," Applied Energy, Elsevier, vol. 99(C), pages 154-166.
    10. Sens, Lucas & Neuling, Ulf & Kaltschmitt, Martin, 2022. "Capital expenditure and levelized cost of electricity of photovoltaic plants and wind turbines – Development by 2050," Renewable Energy, Elsevier, vol. 185(C), pages 525-537.
    11. Hu, Shuai & Xiang, Yue & Huo, Da & Jawad, Shafqat & Liu, Junyong, 2021. "An improved deep belief network based hybrid forecasting method for wind power," Energy, Elsevier, vol. 224(C).
    12. Weihua Tian & Yan Bao & Wei Liu & Naeem Jan, 2022. "Wind Power Forecasting by the BP Neural Network with the Support of Machine Learning," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-10, April.
    13. Madurai Elavarasan, Rajvikram & Pugazhendhi, Rishi & Irfan, Muhammad & Mihet-Popa, Lucian & Campana, Pietro Elia & Khan, Irfan Ahmad, 2022. "A novel Sustainable Development Goal 7 composite index as the paradigm for energy sustainability assessment: A case study from Europe," Applied Energy, Elsevier, vol. 307(C).
    14. Severiano, Carlos A. & Silva, Petrônio Cândido de Lima e & Weiss Cohen, Miri & Guimarães, Frederico Gadelha, 2021. "Evolving fuzzy time series for spatio-temporal forecasting in renewable energy systems," Renewable Energy, Elsevier, vol. 171(C), pages 764-783.
    15. Inês Teixeira & Ana Cristina Ferreira & Nelson Rodrigues & Senhorinha Teixeira, 2024. "Energy Poverty and Its Indicators: A Multidimensional Framework from Literature," Energies, MDPI, vol. 17(14), pages 1-30, July.
    16. Akçay, Hüseyin & Filik, Tansu, 2017. "Short-term wind speed forecasting by spectral analysis from long-term observations with missing values," Applied Energy, Elsevier, vol. 191(C), pages 653-662.
    17. John Boland, 2024. "Constructing Interval Forecasts for Solar and Wind Energy Using Quantile Regression, ARCH and Exponential Smoothing Methods," Energies, MDPI, vol. 17(13), pages 1-17, July.
    18. Fan Li & Hongzhen Wang & Dan Wang & Dong Liu & Ke Sun, 2025. "A Review of Wind Power Prediction Methods Based on Multi-Time Scales," Energies, MDPI, vol. 18(7), pages 1-47, March.
    19. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio, 2009. "Electricity consumption forecasting in Italy using linear regression models," Energy, Elsevier, vol. 34(9), pages 1413-1421.
    20. Sobotka, Anna & Rowicki, Marcin & Badyda, Krzysztof & Sobotka, Piotr, 2021. "Regulatory aspects and electricity production analysis of an offshore wind farm in the Baltic Sea," Renewable Energy, Elsevier, vol. 170(C), pages 315-326.
    21. Agnieszka Brelik & Piotr Nowaczyk & Katarzyna Cheba, 2023. "The Economic Importance of Offshore Wind Energy Development in Poland," Energies, MDPI, vol. 16(23), pages 1-23, November.
    22. Gambella, Claudio & Ghaddar, Bissan & Naoum-Sawaya, Joe, 2021. "Optimization problems for machine learning: A survey," European Journal of Operational Research, Elsevier, vol. 290(3), pages 807-828.
    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. Kamani, D. & Ardehali, M.M., 2023. "Long-term forecast of electrical energy consumption with considerations for solar and wind energy sources," Energy, Elsevier, vol. 268(C).
    2. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    3. Wenting Zhao & Haoran Xu & Peng Chen & Juan Zhang & Jing Li & Tingting Cai, 2025. "Elastic Momentum-Enhanced Adaptive Hybrid Method for Short-Term Load Forecasting," Energies, MDPI, vol. 18(13), pages 1-25, June.
    4. Ioannis Laios & Dimitrios Zafirakis & Konstantinos Moustris, 2025. "From Data-Rich to Data-Scarce: Spatiotemporal Evaluation of a Hybrid Wavelet-Enhanced Deep Learning Model for Day-Ahead Wind Power Forecasting Across Greece," Energies, MDPI, vol. 18(21), pages 1-18, October.
    5. Weronika Kosek & Waldemar Woźniak & Norbert Chamier-Gliszczynski & Wiesław Staniuk, 2025. "Offshore Wind Farm Supply Chains and Regional Development: The Role of Ports in Economic and Logistical Growth in the Central Baltic Region," Energies, MDPI, vol. 18(10), pages 1-21, May.
    6. Niu, Xinsong & Wang, Jiyang, 2019. "A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 241(C), pages 519-539.
    7. Fan Li & Hongzhen Wang & Dan Wang & Dong Liu & Ke Sun, 2025. "A Review of Wind Power Prediction Methods Based on Multi-Time Scales," Energies, MDPI, vol. 18(7), pages 1-47, March.
    8. Zaidan, Sara & El Fadel, Mutasem, 2026. "How representative is SDG7 of energy poverty in politically unstable landscapes? Guiding measurement and monitoring from perceptions to realities," Energy Policy, Elsevier, vol. 210(C).
    9. Li, Chen & Zhu, Zhijie & Yang, Hufang & Li, Ranran, 2019. "An innovative hybrid system for wind speed forecasting based on fuzzy preprocessing scheme and multi-objective optimization," Energy, Elsevier, vol. 174(C), pages 1219-1237.
    10. 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.
    11. Song, Jingjing & Wang, Jianzhou & Lu, Haiyan, 2018. "A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 215(C), pages 643-658.
    12. Makade, Rahul G. & Chakrabarti, Siddharth & Jamil, Basharat & Sakhale, C.N., 2020. "Estimation of global solar radiation for the tropical wet climatic region of India: A theory of experimentation approach," Renewable Energy, Elsevier, vol. 146(C), pages 2044-2059.
    13. Andreas Dellnitz & Andreas Kleine & Madjid Tavana, 2024. "An integrated data envelopment analysis and regression tree method for new product price estimation," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 46(4), pages 1189-1211, December.
    14. Li, Min & Yang, Yi & He, Zhaoshuang & Guo, Xinbo & Zhang, Ruisheng & Huang, Bingqing, 2023. "A wind speed forecasting model based on multi-objective algorithm and interpretability learning," Energy, Elsevier, vol. 269(C).
    15. Wenyu Zhang & Zhongyue Su & Hongli Zhang & Yanru Zhao & Zhiyuan Zhao, 2014. "Hybrid Wind Speed Forecasting Model Study Based on SSA and Intelligent Optimized Algorithm," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).
    16. Hu, Junjie & López Cabrera, Brenda & Melzer, Awdesch, 2021. "Advanced statistical learning on short term load process forecasting," IRTG 1792 Discussion Papers 2021-020, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    17. Heo, SungKu & Byun, Jaewon & Ifaei, Pouya & Ko, Jaerak & Ha, Byeongmin & Hwangbo, Soonho & Yoo, ChangKyoo, 2024. "Towards mega-scale decarbonized industrial park (Mega-DIP): Generative AI-driven techno-economic and environmental assessment of renewable and sustainable energy utilization in petrochemical industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    18. Fang, Ping & Fu, Wenlong & Wang, Kai & Xiong, Dongzhen & Zhang, Kai, 2022. "A compositive architecture coupling outlier correction, EWT, nonlinear Volterra multi-model fusion with multi-objective optimization for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 307(C).
    19. 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).
    20. Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2024. "Wind power forecasting: A temporal domain generalization approach incorporating hybrid model and adversarial relationship-based training," Applied Energy, Elsevier, vol. 355(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:gam:jeners:v:18:y:2025:i:20:p:5380-:d:1769996. 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 The email address of this maintainer does not seem to be valid anymore. Please ask MDPI Indexing Manager to update the entry or send us the correct address (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.