IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i4p1252-d744968.html
   My bibliography  Save this article

Advanced Ensemble Methods Using Machine Learning and Deep Learning for One-Day-Ahead Forecasts of Electric Energy Production in Wind Farms

Author

Listed:
  • Paweł Piotrowski

    (Electrical Power Engineering Institute, Warsaw University of Technology, Koszykowa 75 Street, 00-662 Warsaw, Poland)

  • Dariusz Baczyński

    (Electrical Power Engineering Institute, Warsaw University of Technology, Koszykowa 75 Street, 00-662 Warsaw, Poland)

  • Marcin Kopyt

    (Electrical Power Engineering Institute, Warsaw University of Technology, Koszykowa 75 Street, 00-662 Warsaw, Poland)

  • Tomasz Gulczyński

    (Globema Sp. z o. o., Wita Stwosza 22 Street, 02-661 Warsaw, Poland)

Abstract

The ability to precisely forecast power generation for large wind farms is very important, since such generation is highly unstable and creates problems for Distribution and Transmission System Operators to properly prepare the power system for operation. Forecasts for the next 24 h play an important role in this process. They are also used in energy market transactions. Even a small improvement in the quality of these forecasts translates into more security of the system and savings for the economy. Using two wind farms for statistical analyses and forecasting considerably increases credibility of newly created effective prediction methods and formulated conclusions. In the first part of our study, we have analysed the available data to identify potentially useful explanatory variables for forecasting models with additional development of new input data based on the basic data set. We demonstrate that it is better to use Numerical Weather Prediction (NWP) point forecasts for hourly lags: −3, 2, −1, 0, 1, 2, 3 (original contribution) as input data than lags 0, 1 that are typically used. Also, we prove that it is better to use forecasts from two NWP models as input data. Ensemble, hybrid and single methods are used for predictions, including machine learning (ML) solutions like Gradient-Boosted Trees (GBT), Random Forest (RF), Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), K-Nearest Neighbours Regression (KNNR) and Support Vector Regression (SVR). Original ensemble methods, developed for researching specific implementations, have reduced errors of forecast energy generation for both wind farms as compared to single methods. Predictions by the original ensemble forecasting method, called “Ensemble Averaging Without Extremes” have the lowest normalized mean absolute error (nMAE) among all tested methods. A new, original “Additional Expert Correction” additionally reduces errors of energy generation forecasts for both wind farms. The proposed ensemble methods are also applicable to short-time generation forecasting for other renewable energy sources (RES), e.g., hydropower or photovoltaic (PV) systems.

Suggested Citation

  • Paweł Piotrowski & Dariusz Baczyński & Marcin Kopyt & Tomasz Gulczyński, 2022. "Advanced Ensemble Methods Using Machine Learning and Deep Learning for One-Day-Ahead Forecasts of Electric Energy Production in Wind Farms," Energies, MDPI, vol. 15(4), pages 1-30, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1252-:d:744968
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/4/1252/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/4/1252/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mirosław Parol & Paweł Piotrowski & Piotr Kapler & Mariusz Piotrowski, 2021. "Forecasting of 10-Second Power Demand of Highly Variable Loads for Microgrid Operation Control," Energies, MDPI, vol. 14(5), pages 1-29, February.
    2. Niu, Zhewen & Yu, Zeyuan & Tang, Wenhu & Wu, Qinghua & Reformat, Marek, 2020. "Wind power forecasting using attention-based gated recurrent unit network," Energy, Elsevier, vol. 196(C).
    3. 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.
    4. Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
    5. Ouyang, Tinghui & Huang, Heming & He, Yusen & Tang, Zhenhao, 2020. "Chaotic wind power time series prediction via switching data-driven modes," Renewable Energy, Elsevier, vol. 145(C), pages 270-281.
    6. Shahid, Farah & Zameer, Aneela & Mehmood, Ammara & Raja, Muhammad Asif Zahoor, 2020. "A novel wavenets long short term memory paradigm for wind power prediction," Applied Energy, Elsevier, vol. 269(C).
    7. Duan, Jiandong & Wang, Peng & Ma, Wentao & Tian, Xuan & Fang, Shuai & Cheng, Yulin & Chang, Ying & Liu, Haofan, 2021. "Short-term wind power forecasting using the hybrid model of improved variational mode decomposition and Correntropy Long Short -term memory neural network," Energy, Elsevier, vol. 214(C).
    8. Hu, Huanling & Wang, Lin & Lv, Sheng-Xiang, 2020. "Forecasting energy consumption and wind power generation using deep echo state network," Renewable Energy, Elsevier, vol. 154(C), pages 598-613.
    9. Bingchun Liu & Shijie Zhao & Xiaogang Yu & Lei Zhang & Qingshan Wang, 2020. "A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model," Energies, MDPI, vol. 13(18), pages 1-17, September.
    10. 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).
    11. Paweł Piotrowski & Marcin Kopyt & Dariusz Baczyński & Sylwester Robak & Tomasz Gulczyński, 2021. "Hybrid and Ensemble Methods of Two Days Ahead Forecasts of Electric Energy Production in a Small Wind Turbine," Energies, MDPI, vol. 14(5), pages 1-25, February.
    12. Sun, Mucun & Feng, Cong & Zhang, Jie, 2020. "Multi-distribution ensemble probabilistic wind power forecasting," Renewable Energy, Elsevier, vol. 148(C), pages 135-149.
    13. Yu, Ruiguo & Liu, Zhiqiang & Li, Xuewei & Lu, Wenhuan & Ma, Degang & Yu, Mei & Wang, Jianrong & Li, Bin, 2019. "Scene learning: Deep convolutional networks for wind power prediction by embedding turbines into grid space," Applied Energy, Elsevier, vol. 238(C), pages 249-257.
    14. Wang, Cong & Zhang, Hongli & Ma, Ping, 2020. "Wind power forecasting based on singular spectrum analysis and a new hybrid Laguerre neural network," Applied Energy, Elsevier, vol. 259(C).
    15. Yin, Hao & Ou, Zuhong & Huang, Shengquan & Meng, Anbo, 2019. "A cascaded deep learning wind power prediction approach based on a two-layer of mode decomposition," Energy, Elsevier, vol. 189(C).
    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. 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.
    2. Paweł Piotrowski & Mirosław Parol & Piotr Kapler & Bartosz Fetliński, 2022. "Advanced Forecasting Methods of 5-Minute Power Generation in a PV System for Microgrid Operation Control," Energies, MDPI, vol. 15(7), pages 1-23, April.
    3. Grzegorz Dudek, 2022. "A Comprehensive Study of Random Forest for Short-Term Load Forecasting," Energies, MDPI, vol. 15(20), pages 1-19, October.
    4. Hafiz Muhammad Abdullah & Sanghyoun Park & Kwanjae Seong & Sangyong Lee, 2023. "Hybrid Renewable Energy System Design: A Machine Learning Approach for Optimal Sizing with Net-Metering Costs," Sustainability, MDPI, vol. 15(11), pages 1-37, May.
    5. Wen-Chang Tsai & Chih-Ming Hong & Chia-Sheng Tu & Whei-Min Lin & Chiung-Hsing Chen, 2023. "A Review of Modern Wind Power Generation Forecasting Technologies," Sustainability, MDPI, vol. 15(14), pages 1-40, July.
    6. Grzegorz Dudek & Paweł Piotrowski & Dariusz Baczyński, 2023. "Intelligent Forecasting and Optimization in Electrical Power Systems: Advances in Models and Applications," Energies, MDPI, vol. 16(7), pages 1-11, March.

    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. 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.
    2. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
    3. 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).
    4. da Silva, Ramon Gomes & Ribeiro, Matheus Henrique Dal Molin & Moreno, Sinvaldo Rodrigues & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2021. "A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting," Energy, Elsevier, vol. 216(C).
    5. Meng, Anbo & Zhu, Zibin & Deng, Weisi & Ou, Zuhong & Lin, Shan & Wang, Chenen & Xu, Xuancong & Wang, Xiaolin & Yin, Hao & Luo, Jianqiang, 2022. "A novel wind power prediction approach using multivariate variational mode decomposition and multi-objective crisscross optimization based deep extreme learning machine," Energy, Elsevier, vol. 260(C).
    6. Farah, Shahid & David A, Wood & Humaira, Nisar & Aneela, Zameer & Steffen, Eger, 2022. "Short-term multi-hour ahead country-wide wind power prediction for Germany using gated recurrent unit deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    7. Ma, Yixiang & Yu, Lean & Zhang, Guoxing, 2022. "Short-term wind power forecasting with an intermittency-trait-driven methodology," Renewable Energy, Elsevier, vol. 198(C), pages 872-883.
    8. Jing Wan & Jiehui Huang & Zhiyuan Liao & Chunquan Li & Peter X. Liu, 2022. "A Multi-View Ensemble Width-Depth Neural Network for Short-Term Wind Power Forecasting," Mathematics, MDPI, vol. 10(11), pages 1-20, May.
    9. Meng, Anbo & Chen, Shun & Ou, Zuhong & Ding, Weifeng & Zhou, Huaming & Fan, Jingmin & Yin, Hao, 2022. "A hybrid deep learning architecture for wind power prediction based on bi-attention mechanism and crisscross optimization," Energy, Elsevier, vol. 238(PB).
    10. 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).
    11. 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.
    12. Yuzgec, Ugur & Dokur, Emrah & Balci, Mehmet, 2024. "A novel hybrid model based on Empirical Mode Decomposition and Echo State Network for wind power forecasting," Energy, Elsevier, vol. 300(C).
    13. 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.
    14. Tang, Yugui & Yang, Kuo & Zheng, Yichu & Ma, Li & Zhang, Shujing & Zhang, Zhen, 2024. "Wind power forecasting: A transfer learning approach incorporating temporal convolution and adversarial training," Renewable Energy, Elsevier, vol. 224(C).
    15. Xiao, Yulong & Zou, Chongzhe & Chi, Hetian & Fang, Rengcun, 2023. "Boosted GRU model for short-term forecasting of wind power with feature-weighted principal component analysis," Energy, Elsevier, vol. 267(C).
    16. Yang, Ting & Yang, Zhenning & Li, Fei & Wang, Hengyu, 2024. "A short-term wind power forecasting method based on multivariate signal decomposition and variable selection," Applied Energy, Elsevier, vol. 360(C).
    17. Guanjun Liu & Chao Wang & Hui Qin & Jialong Fu & Qin Shen, 2022. "A Novel Hybrid Machine Learning Model for Wind Speed Probabilistic Forecasting," Energies, MDPI, vol. 15(19), pages 1-16, September.
    18. Wang, Huaqing & Tan, Zhongfu & Liang, Yan & Li, Fanqi & Zhang, Zheyu & Ju, Liwei, 2024. "A novel multi-layer stacking ensemble wind power prediction model under Tensorflow deep learning framework considering feature enhancement and data hierarchy processing," Energy, Elsevier, vol. 286(C).
    19. Liu, Guanjun & Wang, Yun & Qin, Hui & Shen, Keyan & Liu, Shuai & Shen, Qin & Qu, Yuhua & Zhou, Jianzhong, 2023. "Probabilistic spatiotemporal forecasting of wind speed based on multi-network deep ensembles method," Renewable Energy, Elsevier, vol. 209(C), pages 231-247.
    20. Paweł Piotrowski & Marcin Kopyt & Dariusz Baczyński & Sylwester Robak & Tomasz Gulczyński, 2021. "Hybrid and Ensemble Methods of Two Days Ahead Forecasts of Electric Energy Production in a Small Wind Turbine," Energies, MDPI, vol. 14(5), pages 1-25, February.

    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:15:y:2022:i:4:p:1252-:d:744968. 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.