IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v238y2022ipcs0360544221022684.html
   My bibliography  Save this article

A high-accuracy hybrid method for short-term wind power forecasting

Author

Listed:
  • Khazaei, Sahra
  • Ehsan, Mehdi
  • Soleymani, Soodabeh
  • Mohammadnezhad-Shourkaei, Hosein

Abstract

In this article, a high-accuracy hybrid approach for short-term wind power forecasting is proposed using historical data of wind farm and Numerical Weather Prediction (NWP) data. The power forecasting is carried out in three stages: wind direction forecasting, wind speed forecasting, and wind power forecasting. In all three phases, the same hybrid method is used, and the only difference is in the input data set. The main steps of the proposed method are constituted of outlier detection, decomposition of time series using wavelet transform, effective feature selection and prediction of each time series decomposed using Multilayer Perceptron (MLP) neural network. The combination of automatic clustering and T2 statistic is employed for outlier detection. Effective feature selection is also carried out with the assistance of the Non-dominated Sorting Genetic Algorithm II (NSGA- ӀӀ) and the Radial Basis Function (RBF) Neural network. The evaluation of the proposed method using the data of Sotavento wind farm located in Spain demonstrates the very high accuracy of the proposed approach.

Suggested Citation

  • Khazaei, Sahra & Ehsan, Mehdi & Soleymani, Soodabeh & Mohammadnezhad-Shourkaei, Hosein, 2022. "A high-accuracy hybrid method for short-term wind power forecasting," Energy, Elsevier, vol. 238(PC).
    • Handle: RePEc:eee:energy:v:238:y:2022:i:pc:s0360544221022684
    DOI: 10.1016/j.energy.2021.122020
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221022684
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.122020?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Catalão, J.P.S. & Pousinho, H.M.I. & Mendes, V.M.F., 2011. "Short-term wind power forecasting in Portugal by neural networks and wavelet transform," Renewable Energy, Elsevier, vol. 36(4), pages 1245-1251.
    2. 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.
    3. 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).
    4. 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).
    5. 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).
    6. Zucatelli, P.J. & Nascimento, E.G.S. & Santos, A.Á.B. & Arce, A.M.G. & Moreira, D.M., 2021. "An investigation on deep learning and wavelet transform to nowcast wind power and wind power ramp: A case study in Brazil and Uruguay," Energy, Elsevier, vol. 230(C).
    7. Sun, Gaiping & Jiang, Chuanwen & Cheng, Pan & Liu, Yangyang & Wang, Xu & Fu, Yang & He, Yang, 2018. "Short-term wind power forecasts by a synthetical similar time series data mining method," Renewable Energy, Elsevier, vol. 115(C), pages 575-584.
    8. Shahid, Farah & Zameer, Aneela & Muneeb, Muhammad, 2021. "A novel genetic LSTM model for wind power forecast," Energy, Elsevier, vol. 223(C).
    9. Deo, Ravinesh C. & Ghorbani, Mohammad Ali & Samadianfard, Saeed & Maraseni, Tek & Bilgili, Mehmet & Biazar, Mustafa, 2018. "Multi-layer perceptron hybrid model integrated with the firefly optimizer algorithm for windspeed prediction of target site using a limited set of neighboring reference station data," Renewable Energy, Elsevier, vol. 116(PA), pages 309-323.
    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. 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).
    2. Dai, Xiaoran & Liu, Guo-Ping & Hu, Wenshan, 2023. "An online-learning-enabled self-attention-based model for ultra-short-term wind power forecasting," Energy, Elsevier, vol. 272(C).
    3. Han Peng & Songyin Li & Linjian Shangguan & Yisa Fan & Hai Zhang, 2023. "Analysis of Wind Turbine Equipment Failure and Intelligent Operation and Maintenance Research," Sustainability, MDPI, vol. 15(10), pages 1-35, May.
    4. Qu, Zhijian & Li, Jian & Hou, Xinxing & Gui, Jianglin, 2023. "A D-stacking dual-fusion, spatio-temporal graph deep neural network based on a multi-integrated overlay for short-term wind-farm cluster power multi-step prediction," Energy, Elsevier, vol. 281(C).
    5. Hou, Guolian & Wang, Junjie & Fan, Yuzhen & Zhang, Jianhua & Huang, Congzhi, 2024. "A novel wind power deterministic and interval prediction framework based on the critic weight method, improved northern goshawk optimization, and kernel density estimation," Renewable Energy, Elsevier, vol. 226(C).
    6. Mehmood, Faiza & Ghani, Muhammad Usman & Ghafoor, Hina & Shahzadi, Rehab & Asim, Muhammad Nabeel & Mahmood, Waqar, 2022. "EGD-SNet: A computational search engine for predicting an end-to-end machine learning pipeline for Energy Generation & Demand Forecasting," Applied Energy, Elsevier, vol. 324(C).
    7. Ban, Guihua & Chen, Yan & Xiong, Zhenhua & Zhuo, Yixin & Huang, Kui, 2024. "The univariate model for long-term wind speed forecasting based on wavelet soft threshold denoising and improved Autoformer," Energy, Elsevier, vol. 290(C).
    8. Ma, Long & Huang, Ling & Shi, Huifeng, 2023. "Multi-node wind speed forecasting based on a novel dynamic spatial–temporal graph network," Energy, Elsevier, vol. 285(C).
    9. Lee, Keunmin & Park, Bongjoon & Kim, Jeongwon & Hong, Jinkyu, 2024. "Day-ahead wind power forecasting based on feature extraction integrating vertical layer wind characteristics in complex terrain," Energy, Elsevier, vol. 288(C).
    10. Wang, Xiaodi & Hao, Yan & Yang, Wendong, 2024. "Novel wind power ensemble forecasting system based on mixed-frequency modeling and interpretable base model selection strategy," Energy, Elsevier, vol. 297(C).
    11. Lv, Sheng-Xiang & Wang, Lin, 2023. "Multivariate wind speed forecasting based on multi-objective feature selection approach and hybrid deep learning model," Energy, Elsevier, vol. 263(PE).
    12. Al-qaness, Mohammed A.A. & Ewees, Ahmed A. & Fan, Hong & Abualigah, Laith & Elaziz, Mohamed Abd, 2022. "Boosted ANFIS model using augmented marine predator algorithm with mutation operators for wind power forecasting," Applied Energy, Elsevier, vol. 314(C).
    13. Li, Yanting & Wu, Zhenyu & Su, Yan, 2023. "Adaptive short-term wind power forecasting with concept drifts," Renewable Energy, Elsevier, vol. 217(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. 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.
    2. Xiaohan Huang & Aihua Jiang, 2022. "Wind Power Generation Forecast Based on Multi-Step Informer Network," Energies, MDPI, vol. 15(18), pages 1-17, September.
    3. 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.
    4. 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.
    5. 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).
    6. Ding, Song & Cai, Zhijian & Qin, Xinghuan & Shen, Xingao, 2024. "Comparative assessment and policy analysis of forecasting quarterly renewable energy demand: Fresh evidence from an innovative seasonal approach with superior matching algorithms," Applied Energy, Elsevier, vol. 367(C).
    7. 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).
    8. Liu, Shuwei & Tian, Jianyan & Ji, Zhengxiong & Dai, Yuanyuan & Guo, Hengkuan & Yang, Shengqiang, 2024. "Research on multi-digital twin and its application in wind power forecasting," Energy, Elsevier, vol. 292(C).
    9. Wang, Pengfei & Zhang, Jiaxuan & Wan, Jiashuang & Wu, Shifa, 2022. "A fault diagnosis method for small pressurized water reactors based on long short-term memory networks," Energy, Elsevier, vol. 239(PC).
    10. Zhang, Yagang & Zhang, Jinghui & Yu, Leyi & Pan, Zhiya & Feng, Changyou & Sun, Yiqian & Wang, Fei, 2022. "A short-term wind energy hybrid optimal prediction system with denoising and novel error correction technique," Energy, Elsevier, vol. 254(PC).
    11. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
    12. 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).
    13. Zhengxuan Xiao & Fei Tang & Mengyuan Wang, 2023. "Wind Power Short-Term Forecasting Method Based on LSTM and Multiple Error Correction," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    14. 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.
    15. Shi, Qiao & Lin, Yanwen & Hao, Yongchao & Song, Zixuan & Zhou, Ziyue & Fu, Yuequn & Zhang, Zhisen & Wu, Jianyang, 2023. "Unconventional growth of methane hydrates: A molecular dynamics and machine learning study," Energy, Elsevier, vol. 282(C).
    16. Huang, Zhiwen & Li, Tong & Huang, Kexin & Ke, Hanbing & Lin, Mei & Wang, Qiuwang, 2022. "Predictions of flow and temperature fields in a T-junction based on dynamic mode decomposition and deep learning," Energy, Elsevier, vol. 261(PA).
    17. 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.
    18. Huang, Hao-Hsuan & Huang, Yun-Hsun, 2024. "Applying green learning to regional wind power prediction and fluctuation risk assessment," Energy, Elsevier, vol. 295(C).
    19. Nathan Oaks Farrar & Mohd Hasan Ali & Dipankar Dasgupta, 2023. "Artificial Intelligence and Machine Learning in Grid Connected Wind Turbine Control Systems: A Comprehensive Review," Energies, MDPI, vol. 16(3), pages 1-25, February.
    20. Liu, Zhi-Feng & Liu, You-Yuan & Chen, Xiao-Rui & Zhang, Shu-Rui & Luo, Xing-Fu & Li, Ling-Ling & Yang, Yi-Zhou & You, Guo-Dong, 2024. "A novel deep learning-based evolutionary model with potential attention and memory decay-enhancement strategy for short-term wind power point-interval forecasting," Applied Energy, Elsevier, vol. 360(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:eee:energy:v:238:y:2022:i:pc:s0360544221022684. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.