IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i15p5810-d1210786.html
   My bibliography  Save this article

Comparison and Enhancement of Machine Learning Algorithms for Wind Turbine Output Prediction with Insufficient Data

Author

Listed:
  • Subin Im

    (Department of Electrical Engineering, Kwangwoon University, Seoul 01897, Republic of Korea)

  • Hojun Lee

    (Department of Electrical Engineering, Kwangwoon University, Seoul 01897, Republic of Korea)

  • Don Hur

    (Department of Electrical Engineering, Kwangwoon University, Seoul 01897, Republic of Korea)

  • Minhan Yoon

    (Department of Electrical Engineering, Kwangwoon University, Seoul 01897, Republic of Korea)

Abstract

As the penetration of renewable energy sources into a power system increases, the significance of precise short-term forecasts for wind power generation becomes paramount. However, the erratic and non-periodic nature of wind poses challenges in accurately predicting the output. This paper presents a comprehensive investigation into forecasting wind power generation for the following day, using three machine learning models: long short-term memory (LSTM), convolutional neural network-bidirectional LSTM (CNN-biLSTM), and light gradient boosting machine (LGBM). In addition, this paper proposes a method to improve the prediction performance of LGBM by separating data according to the distribution of features, and training and testing each separated dataset with a distinct model. This study includes a comparative analysis of the performance of the proposed models in predicting wind turbine output, offering valuable insights into their respective efficiencies. The results of this investigation were analyzed for two geographically distinct wind farms (Korea and the UK). The findings of this study are expected to facilitate the selection of efficient prediction models within the forecast accuracy auxiliary service market and assist grid operators in ensuring reliable power supply for the grid.

Suggested Citation

  • Subin Im & Hojun Lee & Don Hur & Minhan Yoon, 2023. "Comparison and Enhancement of Machine Learning Algorithms for Wind Turbine Output Prediction with Insufficient Data," Energies, MDPI, vol. 16(15), pages 1-16, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5810-:d:1210786
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/15/5810/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/15/5810/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    2. A-Hyun Jung & Dong-Hyun Lee & Jin-Young Kim & Chang Ki Kim & Hyun-Goo Kim & Yung-Seop Lee, 2022. "Regional Photovoltaic Power Forecasting Using Vector Autoregression Model in South Korea," Energies, MDPI, vol. 15(21), pages 1-13, October.
    3. Kavasseri, Rajesh G. & Seetharaman, Krithika, 2009. "Day-ahead wind speed forecasting using f-ARIMA models," Renewable Energy, Elsevier, vol. 34(5), pages 1388-1393.
    4. Wei, Jingdong & Zhang, Yao & Wang, Jianxue & Cao, Xiaoyu & Khan, Muhammad Armoghan, 2020. "Multi-period planning of multi-energy microgrid with multi-type uncertainties using chance constrained information gap decision method," Applied Energy, Elsevier, vol. 260(C).
    5. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.
    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. Yao Zhang & Fan Lin & Ke Wang, 2020. "Robustness of Short-Term Wind Power Forecasting against False Data Injection Attacks," Energies, MDPI, vol. 13(15), pages 1-21, July.
    2. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
    3. 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).
    4. 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).
    5. Hu, Jianming & Wang, Jianzhou & Zeng, Guowei, 2013. "A hybrid forecasting approach applied to wind speed time series," Renewable Energy, Elsevier, vol. 60(C), pages 185-194.
    6. Rana Muhammad Adnan & Zhongmin Liang & Xiaohui Yuan & Ozgur Kisi & Muhammad Akhlaq & Binquan Li, 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation," Energies, MDPI, vol. 12(2), pages 1-22, January.
    7. Abubakar Ahmad Musa & Adamu Hussaini & Weixian Liao & Fan Liang & Wei Yu, 2023. "Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey," Future Internet, MDPI, vol. 15(6), pages 1-24, May.
    8. Lan, Puzhe & Han, Dong & Xu, Xiaoyuan & Yan, Zheng & Ren, Xijun & Xia, Shiwei, 2022. "Data-driven state estimation of integrated electric-gas energy system," Energy, Elsevier, vol. 252(C).
    9. Ijaz Ul Haq & Amin Ullah & Samee Ullah Khan & Noman Khan & Mi Young Lee & Seungmin Rho & Sung Wook Baik, 2021. "Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors," Mathematics, MDPI, vol. 9(6), pages 1-17, March.
    10. Ke Yan & Xudong Wang & Yang Du & Ning Jin & Haichao Huang & Hangxia Zhou, 2018. "Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy," Energies, MDPI, vol. 11(11), pages 1-15, November.
    11. Zonggui Yao & Chen Wang, 2018. "A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting," Sustainability, MDPI, vol. 10(5), pages 1-33, May.
    12. Lu, Yakai & Tian, Zhe & Zhou, Ruoyu & Liu, Wenjing, 2021. "A general transfer learning-based framework for thermal load prediction in regional energy system," Energy, Elsevier, vol. 217(C).
    13. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
    14. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    15. Santamaría-Bonfil, G. & Reyes-Ballesteros, A. & Gershenson, C., 2016. "Wind speed forecasting for wind farms: A method based on support vector regression," Renewable Energy, Elsevier, vol. 85(C), pages 790-809.
    16. Sen Guo & Haoran Zhao & Huiru Zhao, 2017. "A New Hybrid Wind Power Forecaster Using the Beveridge-Nelson Decomposition Method and a Relevance Vector Machine Optimized by the Ant Lion Optimizer," Energies, MDPI, vol. 10(7), pages 1-20, July.
    17. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda & Song, Jiakang, 2018. "Deep belief network based k-means cluster approach for short-term wind power forecasting," Energy, Elsevier, vol. 165(PA), pages 840-852.
    18. 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).
    19. Zhang, Lifang & Wang, Jianzhou & Niu, Xinsong & Liu, Zhenkun, 2021. "Ensemble wind speed forecasting with multi-objective Archimedes optimization algorithm and sub-model selection," Applied Energy, Elsevier, vol. 301(C).
    20. Namrye Son, 2021. "Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting," Sustainability, MDPI, vol. 13(22), pages 1-25, November.

    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:15:p:5810-:d:1210786. 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.