IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i5p2359-d1084685.html
   My bibliography  Save this article

Large Scale, Mid Term Wind Farms Power Generation Prediction

Author

Listed:
  • Marcin Blachnik

    (Department of Industrial Informatics, Silesian University of Technology, 44-100 Gliwice, Poland
    Interdisciplinary Division for Energy Analysis, National Centre for Nuclear Research, 05-400 Otwock, Poland)

  • Sławomir Walkowiak

    (Interdisciplinary Division for Energy Analysis, National Centre for Nuclear Research, 05-400 Otwock, Poland)

  • Adam Kula

    (Department of Industrial Informatics, Silesian University of Technology, 44-100 Gliwice, Poland)

Abstract

Renewable energy sources, such as wind turbines, have become much more prevalent in recent years, and thus a popular form of energy generation. This is in part due to the ‘Fit for 55’ EU initiative, and in part, to rising fossil fuel prices, as well as the perceived requirement for nations to have power independence, and due to the influence of renewable energy sources we can see a marked increase in large wind farms in particular. However, wind farms by their very nature are highly inconsistent regarding power generation and are weather-dependent, thus presenting several challenges for transmission system operators. One of the options to overcome these issues is a system being able to forecast the generated power in a wide-ranging period—ranging from 15 min up to 36 h, and with an adequate resolution. Such a system would better help manage the power grid and allow for greater utilization of the green energy produced. In this document, we present a process of development for such a system, along with a comparison of the various steps of the process, including data preparation, feature importance analysis, and the impact of various data sources on the forecast horizon. Lastly, we also compare multiple machine learning models and their influence on the system quality and execution time. Additionally, we propose an ensemble that concatenates predictions over the forecast horizon. The conducted experiments have been evaluated on seven wind farms located in Central Europe. Out of the experiments conducted, the most efficient solution with the lowest error rate and required computational resources has been obtained for random forest regression, and two independent models; one for the short-term horizon, and the other, for the mid- to long-term horizon, which was combined into one forecasting system.

Suggested Citation

  • Marcin Blachnik & Sławomir Walkowiak & Adam Kula, 2023. "Large Scale, Mid Term Wind Farms Power Generation Prediction," Energies, MDPI, vol. 16(5), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2359-:d:1084685
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/5/2359/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/5/2359/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Grzegorz Dudek, 2022. "A Comprehensive Study of Random Forest for Short-Term Load Forecasting," Energies, MDPI, vol. 15(20), pages 1-19, October.
    2. Khosravi, A. & Machado, L. & Nunes, R.O., 2018. "Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil," Applied Energy, Elsevier, vol. 224(C), pages 550-566.
    3. Shahram Hanifi & Xiaolei Liu & Zi Lin & Saeid Lotfian, 2020. "A Critical Review of Wind Power Forecasting Methods—Past, Present and Future," Energies, MDPI, vol. 13(15), pages 1-24, July.
    4. Long Cai & Jie Gu & Jinghuan Ma & Zhijian Jin, 2019. "Probabilistic Wind Power Forecasting Approach via Instance-Based Transfer Learning Embedded Gradient Boosting Decision Trees," Energies, MDPI, vol. 12(1), pages 1-19, January.
    5. Baskut, Omer & Ozgener, Onder & Ozgener, Leyla, 2010. "Effects of meteorological variables on exergetic efficiency of wind turbine power plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 3237-3241, December.
    6. Hong, Ying-Yi & Rioflorido, Christian Lian Paulo P., 2019. "A hybrid deep learning-based neural network for 24-h ahead wind power forecasting," Applied Energy, Elsevier, vol. 250(C), pages 530-539.
    7. Imre Delgado & Muhammad Fahim, 2020. "Wind Turbine Data Analysis and LSTM-Based Prediction in SCADA System," Energies, MDPI, vol. 14(1), pages 1-21, December.
    8. Zhang, Jinhua & Yan, Jie & Infield, David & Liu, Yongqian & Lien, Fue-sang, 2019. "Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model," Applied Energy, Elsevier, vol. 241(C), pages 229-244.
    9. Lahouar, A. & Ben Hadj Slama, J., 2017. "Hour-ahead wind power forecast based on random forests," Renewable Energy, Elsevier, vol. 109(C), pages 529-541.
    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. Feng Xing & Xiaoyu Song & Yubo Wang & Caiyan Qin, 2023. "A New Combined Prediction Model for Ultra-Short-Term Wind Power Based on Variational Mode Decomposition and Gradient Boosting Regression Tree," Sustainability, MDPI, vol. 15(14), pages 1-18, July.
    2. Cui, Xiwen & Yu, Xiaoyu & Niu, Dongxiao, 2024. "The ultra-short-term wind power point-interval forecasting model based on improved variational mode decomposition and bidirectional gated recurrent unit improved by improved sparrow search algorithm a," Energy, Elsevier, vol. 288(C).

    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. Yang, Mao & Wang, Da & Xu, Chuanyu & Dai, Bozhi & Ma, Miaomiao & Su, Xin, 2023. "Power transfer characteristics in fluctuation partition algorithm for wind speed and its application to wind power forecasting," Renewable Energy, Elsevier, vol. 211(C), pages 582-594.
    2. Kim, Daeyoung & Ryu, Geonhwa & Moon, Chaejoo & Kim, Bumsuk, 2024. "Accuracy of a short-term wind power forecasting model based on deep learning using LiDAR-SCADA integration: A case study of the 400-MW Anholt offshore wind farm," Applied Energy, Elsevier, vol. 373(C).
    3. Hanifi, Shahram & Cammarono, Andrea & Zare-Behtash, Hossein, 2024. "Advanced hyperparameter optimization of deep learning models for wind power prediction," Renewable Energy, Elsevier, vol. 221(C).
    4. Hanifi, Shahram & Zare-Behtash, Hossein & Cammarano, Andrea & Lotfian, Saeid, 2023. "Offshore wind power forecasting based on WPD and optimised deep learning methods," Renewable Energy, Elsevier, vol. 218(C).
    5. Hui Huang & Qiliang Zhu & Xueling Zhu & Jinhua Zhang, 2023. "An Adaptive, Data-Driven Stacking Ensemble Learning Framework for the Short-Term Forecasting of Renewable Energy Generation," Energies, MDPI, vol. 16(4), pages 1-20, February.
    6. Paweł Piotrowski & Inajara Rutyna & Dariusz Baczyński & Marcin Kopyt, 2022. "Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors," Energies, MDPI, vol. 15(24), pages 1-38, December.
    7. Liu, Yanli & Wang, Junyi, 2022. "Transfer learning based multi-layer extreme learning machine for probabilistic wind power forecasting," Applied Energy, Elsevier, vol. 312(C).
    8. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
    9. 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).
    10. Cheng-Yu Ho & Ke-Sheng Cheng & Chi-Hang Ang, 2023. "Utilizing the Random Forest Method for Short-Term Wind Speed Forecasting in the Coastal Area of Central Taiwan," Energies, MDPI, vol. 16(3), pages 1-18, January.
    11. Manisha Sawant & Rupali Patil & Tanmay Shikhare & Shreyas Nagle & Sakshi Chavan & Shivang Negi & Neeraj Dhanraj Bokde, 2022. "A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction," Energies, MDPI, vol. 15(21), pages 1-24, October.
    12. Lu, Hongfang & Ma, Xin & Huang, Kun & Azimi, Mohammadamin, 2020. "Prediction of offshore wind farm power using a novel two-stage model combining kernel-based nonlinear extension of the Arps decline model with a multi-objective grey wolf optimizer," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    13. Kisvari, Adam & Lin, Zi & Liu, Xiaolei, 2021. "Wind power forecasting – A data-driven method along with gated recurrent neural network," Renewable Energy, Elsevier, vol. 163(C), pages 1895-1909.
    14. Lin, Zi & Liu, Xiaolei, 2020. "Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network," Energy, Elsevier, vol. 201(C).
    15. Shahram Hanifi & Saeid Lotfian & Hossein Zare-Behtash & Andrea Cammarano, 2022. "Offshore Wind Power Forecasting—A New Hyperparameter Optimisation Algorithm for Deep Learning Models," Energies, MDPI, vol. 15(19), pages 1-21, September.
    16. Upma Singh & Mohammad Rizwan & Muhannad Alaraj & Ibrahim Alsaidan, 2021. "A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments," Energies, MDPI, vol. 14(16), pages 1-21, August.
    17. Li, Tenghui & Liu, Xiaolei & Lin, Zi & Morrison, Rory, 2022. "Ensemble offshore Wind Turbine Power Curve modelling – An integration of Isolation Forest, fast Radial Basis Function Neural Network, and metaheuristic algorithm," Energy, Elsevier, vol. 239(PD).
    18. Lv, Jiaqing & Zheng, Xiaodong & Pawlak, Mirosław & Mo, Weike & Miśkowicz, Marek, 2021. "Very short-term probabilistic wind power prediction using sparse machine learning and nonparametric density estimation algorithms," Renewable Energy, Elsevier, vol. 177(C), pages 181-192.
    19. Gong, Zhipeng & Wan, Anping & Ji, Yunsong & AL-Bukhaiti, Khalil & Yao, Zhehe, 2024. "Improving short-term offshore wind speed forecast accuracy using a VMD-PE-FCGRU hybrid model," Energy, Elsevier, vol. 295(C).
    20. Ana Lagos & Joaquín E. Caicedo & Gustavo Coria & Andrés Romero Quete & Maximiliano Martínez & Gastón Suvire & Jesús Riquelme, 2022. "State-of-the-Art Using Bibliometric Analysis of Wind-Speed and -Power Forecasting Methods Applied in Power Systems," Energies, MDPI, vol. 15(18), pages 1-40, September.

    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:16:y:2023:i:5:p:2359-:d:1084685. 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.