IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i5p1225-d504743.html
   My bibliography  Save this article

Hybrid and Ensemble Methods of Two Days Ahead Forecasts of Electric Energy Production in a Small Wind Turbine

Author

Listed:
  • Paweł Piotrowski

    (Institute of Electrical Power Engineering, Warsaw University of Technology, Koszykowa 75 Street, 00-661 Warszawa, Poland)

  • Marcin Kopyt

    (Institute of Electrical Power Engineering, Warsaw University of Technology, Koszykowa 75 Street, 00-661 Warszawa, Poland)

  • Dariusz Baczyński

    (Institute of Electrical Power Engineering, Warsaw University of Technology, Koszykowa 75 Street, 00-661 Warszawa, Poland)

  • Sylwester Robak

    (Institute of Electrical Power Engineering, Warsaw University of Technology, Koszykowa 75 Street, 00-661 Warszawa, Poland)

  • Tomasz Gulczyński

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

Abstract

The ability to forecast electricity generation for a small wind turbine is important both on a larger scale where there are many such turbines (because it creates problems for networks managed by distribution system operators) and for prosumers to allow current energy consumption planning. It is also important for owners of small energy systems in order to optimize the use of various energy sources and facilitate energy storage. The research presented here addresses an original, rarely predicted 48 h forecasting horizon for small wind turbines. This topic has been rather underrepresented in research, especially in comparison with forecasts for large wind farms. Wind speed forecasts with a 48 h horizon are also rarely used as input data. We have analyzed the available data to identify potentially useful explanatory variables for forecasting models. Eight sets with increasing data amounts were created to analyze the influence of the types and amounts of data on forecast quality. Hybrid, ensemble and single methods are used for predictions, including machine learning (ML) solutions like long short-term memory (LSTM), multi-layer perceptron (MLP), support vector regression (SVR) and K-nearest neighbours regression (KNNR). Original hybrid methods, developed for research of specific implementations and ensemble methods based on hybrid methods’ decreased errors of energy generation forecasts for small wind turbines in comparison with single methods. The “artificial neural network (ANN) type MLP as an integrator of ensemble based on hybrid methods” ensemble forecasting method incorporates an original combination of predictors. Predictions by this method have the lowest mean absolute error (MAE). In addition, this paper presents an original ensemble forecasting method, called “averaging ensemble based on hybrid methods without extreme forecasts”. Predictions by this method have the lowest root mean square error (RMSE) error among all tested methods. LSTM, a deep neural network, is the best single method, MLP is the second best one, while SVR, KNNR and, especially, linear regression (LR) perform less well. We prove that lagged values of forecasted time series slightly increase the accuracy of predictions. The same applies to seasonal and daily variability markers. Our studies have also demonstrated that using the full set of available input data and the best proposed hybrid and ensemble methods yield the lowest error. The proposed hybrid and ensemble methods are also applicable to other short-time generation forecasting in renewable energy sources (RES), e.g., in photovoltaic (PV) systems or hydropower.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1225-:d:504743
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/5/1225/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/5/1225/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Baobin Zhou & Che Liu & Jianjing Li & Bo Sun & Jun Yang, 2020. "A Hybrid Method for Ultrashort-Term Wind Power Prediction considering Meteorological Features and Seasonal Information," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-12, September.
    2. Hu, Jianming & Tang, Jingwei & Lin, Yingying, 2020. "A novel wind power probabilistic forecasting approach based on joint quantile regression and multi-objective optimization," Renewable Energy, Elsevier, vol. 149(C), pages 141-164.
    3. Kuen-Suan Chen & Kuo-Ping Lin & Jun-Xiang Yan & Wan-Lin Hsieh, 2019. "Renewable Power Output Forecasting Using Least-Squares Support Vector Regression and Google Data," Sustainability, MDPI, vol. 11(11), pages 1-13, May.
    4. Nahid-Al-Masood, & Yan, Ruifeng & Saha, Tapan Kumar, 2015. "A new tool to estimate maximum wind power penetration level: In perspective of frequency response adequacy," Applied Energy, Elsevier, vol. 154(C), pages 209-220.
    5. Jarosław Brodny & Magdalena Tutak & Saqib Ahmad Saki, 2020. "Forecasting the Structure of Energy Production from Renewable Energy Sources and Biofuels in Poland," Energies, MDPI, vol. 13(10), pages 1-31, May.
    6. Cai, Haoshu & Jia, Xiaodong & Feng, Jianshe & Li, Wenzhe & Hsu, Yuan-Ming & Lee, Jay, 2020. "Gaussian Process Regression for numerical wind speed prediction enhancement," Renewable Energy, Elsevier, vol. 146(C), pages 2112-2123.
    7. Sun, Mucun & Feng, Cong & Zhang, Jie, 2020. "Multi-distribution ensemble probabilistic wind power forecasting," Renewable Energy, Elsevier, vol. 148(C), pages 135-149.
    8. Sewdien, V.N. & Preece, R. & Torres, J.L. Rueda & Rakhshani, E. & van der Meijden, M., 2020. "Assessment of critical parameters for artificial neural networks based short-term wind generation forecasting," Renewable Energy, Elsevier, vol. 161(C), pages 878-892.
    9. Hao Zhen & Dongxiao Niu & Min Yu & Keke Wang & Yi Liang & Xiaomin Xu, 2020. "A Hybrid Deep Learning Model and Comparison for Wind Power Forecasting Considering Temporal-Spatial Feature Extraction," Sustainability, MDPI, vol. 12(22), pages 1-24, November.
    10. 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.
    11. 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).
    12. Evangelos Spiliotis & Fotios Petropoulos & Konstantinos Nikolopoulos, 2020. "The Impact of Imperfect Weather Forecasts on Wind Power Forecasting Performance: Evidence from Two Wind Farms in Greece," Energies, MDPI, vol. 13(8), pages 1-18, April.
    13. Pei Zhang & Chunping Li & Chunhua Peng & Jiangang Tian, 2020. "Ultra-Short-Term Prediction of Wind Power Based on Error Following Forget Gate-Based Long Short-Term Memory," Energies, MDPI, vol. 13(20), pages 1-13, October.
    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 & 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.
    2. Altaf Hussain Rajpar & Imran Ali & Ahmad E. Eladwi & Mohamed Bashir Ali Bashir, 2021. "Recent Development in the Design of Wind Deflectors for Vertical Axis Wind Turbine: A Review," Energies, MDPI, vol. 14(16), pages 1-23, August.
    3. 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.

    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 & 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.
    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. 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.
    4. Jin, Huaiping & Shi, Lixian & Chen, Xiangguang & Qian, Bin & Yang, Biao & Jin, Huaikang, 2021. "Probabilistic wind power forecasting using selective ensemble of finite mixture Gaussian process regression models," Renewable Energy, Elsevier, vol. 174(C), pages 1-18.
    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.
    6. Wang, Yun & Xu, Houhua & Song, Mengmeng & Zhang, Fan & Li, Yifen & Zhou, Shengchao & Zhang, Lingjun, 2023. "A convolutional Transformer-based truncated Gaussian density network with data denoising for wind speed forecasting," Applied Energy, Elsevier, vol. 333(C).
    7. Liu, Xin & Yang, Luoxiao & Zhang, Zijun, 2022. "The attention-assisted ordinary differential equation networks for short-term probabilistic wind power predictions," Applied Energy, Elsevier, vol. 324(C).
    8. Guangyu Qin & Qingyou Yan & Jingyao Zhu & Chuanbo Xu & Daniel M. Kammen, 2021. "Day-Ahead Wind Power Forecasting Based on Wind Load Data Using Hybrid Optimization Algorithm," Sustainability, MDPI, vol. 13(3), pages 1-17, January.
    9. Wang, Lei & He, Yigang, 2022. "M2STAN: Multi-modal multi-task spatiotemporal attention network for multi-location ultra-short-term wind power multi-step predictions," Applied Energy, Elsevier, vol. 324(C).
    10. Honghai Niu & Yu Yang & Lingchao Zeng & Yiguo Li, 2021. "ELM-QR-Based Nonparametric Probabilistic Prediction Method for Wind Power," Energies, MDPI, vol. 14(3), pages 1-15, January.
    11. 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).
    12. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
    13. 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.
    14. Mohamed CHIKHI & Claude DIEBOLT, 2022. "Testing the weak form efficiency of the French ETF market with the LSTAR-ANLSTGARCH approach using a semiparametric estimation," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 13, pages 228-253, June.
    15. Lim, Juin Yau & Safder, Usman & How, Bing Shen & Ifaei, Pouya & Yoo, Chang Kyoo, 2021. "Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model," Applied Energy, Elsevier, vol. 283(C).
    16. 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).
    17. Katarzyna Chudy-Laskowska & Tomasz Pisula, 2022. "An Analysis of the Use of Energy from Conventional Fossil Fuels and Green Renewable Energy in the Context of the European Union’s Planned Energy Transformation," Energies, MDPI, vol. 15(19), pages 1-23, October.
    18. Anna Duczkowska & Ewa Kulińska & Zbigniew Plutecki & Joanna Rut, 2022. "Sustainable Agro-Biomass Market for Urban Heating Using Centralized District Heating System," Energies, MDPI, vol. 15(12), pages 1-23, June.
    19. Elianne Mora & Jenny Cifuentes & Geovanny Marulanda, 2021. "Short-Term Forecasting of Wind Energy: A Comparison of Deep Learning Frameworks," Energies, MDPI, vol. 14(23), pages 1-26, November.
    20. 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).

    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:14:y:2021:i:5:p:1225-:d:504743. 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.