IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i3p1374-d1050595.html
   My bibliography  Save this article

Utilizing the Random Forest Method for Short-Term Wind Speed Forecasting in the Coastal Area of Central Taiwan

Author

Listed:
  • Cheng-Yu Ho

    (Hydrotech Research Institute, National Taiwan University, Taipei 10617, Taiwan)

  • Ke-Sheng Cheng

    (Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan)

  • Chi-Hang Ang

    (Department of Civil Engineering, National Taiwan University, Taipei 10617, Taiwan)

Abstract

The Taiwan Strait contains a vast potential for wind energy. However, the power grid balance is challenging due to wind energy’s uncertainty and intermittent nature. Wind speed forecasting reduces this risk, increasing the penetration rate. Machine learning (ML) models are adopted in this study for the short-term prediction of wind speed based on the complex nonlinear relationships among wind speed, terrain, air pressure, air temperature, and other weather conditions. Feature selection is crucial for ML modeling. Finding more valuable features in observations is the key to improving the accuracy of prediction models. The random forest method was selected because of its stability, interpretability, low computational cost, and immunity to noise, which helps maintain focus on investigating the essential features from vast data. In this study, several new exogenous features were found on the basis of physics and the spatiotemporal correlation of surrounding data. Apart from the conventional input features used for wind speed prediction, such as wind speed, wind direction, air pressure, and air temperature, new features were identified through the feature importance of the random forest method, including wave height, air pressure difference, air-sea temperature difference, and hours and months, representing the periodic components of time series analysis. The air–sea temperature difference is proposed to replace the wind speed difference to represent atmosphere stability due to the availability and adequate accuracy of the data. A random forest and an artificial neural network model were created to investigate the effectiveness and generality of these new features. Both models are superior to persistence models and models using only conventional features. The random forest model outperformed all models. We believe that time-consuming and tune-required sophisticated models may also benefit from these new features.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1374-:d:1050595
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/3/1374/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/3/1374/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cadenas, Erasmo & Rivera, Wilfrido, 2009. "Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks," Renewable Energy, Elsevier, vol. 34(1), pages 274-278.
    2. Jung, Jaesung & Broadwater, Robert P., 2014. "Current status and future advances for wind speed and power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 762-777.
    3. Wang, Yamin & Wu, Lei, 2016. "On practical challenges of decomposition-based hybrid forecasting algorithms for wind speed and solar irradiation," Energy, Elsevier, vol. 112(C), pages 208-220.
    4. Ismail Shah & Faheem Jan & Sajid Ali & Tahir Mehmood, 2022. "Functional Data Approach for Short-Term Electricity Demand Forecasting," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-14, June.
    5. Cadenas, Erasmo & Rivera, Wilfrido, 2010. "Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model," Renewable Energy, Elsevier, vol. 35(12), pages 2732-2738.
    6. Mohandes, Mohamed A. & Rehman, Shafiqur & Halawani, Talal O., 1998. "A neural networks approach for wind speed prediction," Renewable Energy, Elsevier, vol. 13(3), pages 345-354.
    7. 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.
    8. Ke-Sheng Cheng & Cheng-Yu Ho & Jen-Hsin Teng, 2020. "Wind Characteristics in the Taiwan Strait: A Case Study of the First Offshore Wind Farm in Taiwan," Energies, MDPI, vol. 13(24), pages 1-21, December.
    9. Ke-Sheng Cheng & Cheng-Yu Ho & Jen-Hsin Teng, 2022. "Wind and Sea Breeze Characteristics for the Offshore Wind Farms in the Central Coastal Area of Taiwan," Energies, MDPI, vol. 15(3), pages 1-23, January.
    10. 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.
    11. Piramuthu, Selwyn, 2004. "Evaluating feature selection methods for learning in data mining applications," European Journal of Operational Research, Elsevier, vol. 156(2), pages 483-494, July.
    12. Erasmo Cadenas & Wilfrido Rivera & Rafael Campos-Amezcua & Christopher Heard, 2016. "Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model," Energies, MDPI, vol. 9(2), pages 1-15, February.
    13. Optis, Mike & Perr-Sauer, Jordan, 2019. "The importance of atmospheric turbulence and stability in machine-learning models of wind farm power production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 27-41.
    14. Jursa, René & Rohrig, Kurt, 2008. "Short-term wind power forecasting using evolutionary algorithms for the automated specification of artificial intelligence models," International Journal of Forecasting, Elsevier, vol. 24(4), pages 694-709.
    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. Wen, Songkang & Li, Yanting & Su, Yan, 2022. "A new hybrid model for power forecasting of a wind farm using spatial–temporal correlations," Renewable Energy, Elsevier, vol. 198(C), pages 155-168.
    2. Ahmed, Adil & Khalid, Muhammad, 2018. "An intelligent framework for short-term multi-step wind speed forecasting based on Functional Networks," Applied Energy, Elsevier, vol. 225(C), pages 902-911.
    3. Tascikaraoglu, A. & Uzunoglu, M., 2014. "A review of combined approaches for prediction of short-term wind speed and power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 243-254.
    4. Optis, Mike & Perr-Sauer, Jordan, 2019. "The importance of atmospheric turbulence and stability in machine-learning models of wind farm power production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 27-41.
    5. Ramasamy, P. & Chandel, S.S. & Yadav, Amit Kumar, 2015. "Wind speed prediction in the mountainous region of India using an artificial neural network model," Renewable Energy, Elsevier, vol. 80(C), pages 338-347.
    6. Wang, Jianzhou & Xiong, Shenghua, 2014. "A hybrid forecasting model based on outlier detection and fuzzy time series – A case study on Hainan wind farm of China," Energy, Elsevier, vol. 76(C), pages 526-541.
    7. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    8. 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.
    9. Wang, Jianzhou & Qin, Shanshan & Zhou, Qingping & Jiang, Haiyan, 2015. "Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China," Renewable Energy, Elsevier, vol. 76(C), pages 91-101.
    10. Qian, Zheng & Pei, Yan & Zareipour, Hamidreza & Chen, Niya, 2019. "A review and discussion of decomposition-based hybrid models for wind energy forecasting applications," Applied Energy, Elsevier, vol. 235(C), pages 939-953.
    11. Wang, Jianzhou & Niu, Tong & Lu, Haiyan & Guo, Zhenhai & Yang, Wendong & Du, Pei, 2018. "An analysis-forecast system for uncertainty modeling of wind speed: A case study of large-scale wind farms," Applied Energy, Elsevier, vol. 211(C), pages 492-512.
    12. Li, Hongmin & Wang, Jianzhou & Lu, Haiyan & Guo, Zhenhai, 2018. "Research and application of a combined model based on variable weight for short term wind speed forecasting," Renewable Energy, Elsevier, vol. 116(PA), pages 669-684.
    13. Liu, Da & Niu, Dongxiao & Wang, Hui & Fan, Leilei, 2014. "Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm," Renewable Energy, Elsevier, vol. 62(C), pages 592-597.
    14. Jiang, Ping & Wang, Yun & Wang, Jianzhou, 2017. "Short-term wind speed forecasting using a hybrid model," Energy, Elsevier, vol. 119(C), pages 561-577.
    15. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    16. 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).
    17. 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.
    18. 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.
    19. Philippopoulos, Kostas & Deligiorgi, Despina, 2012. "Application of artificial neural networks for the spatial estimation of wind speed in a coastal region with complex topography," Renewable Energy, Elsevier, vol. 38(1), pages 75-82.
    20. Yiqi Chu & Chengcai Li & Yefang Wang & Jing Li & Jian Li, 2016. "A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction," Energies, MDPI, vol. 9(11), pages 1-20, October.

    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:3:p:1374-:d:1050595. 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.