IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v143y2019icp1172-1192.html
   My bibliography  Save this article

A novel non-linear combination system for short-term wind speed forecast

Author

Listed:
  • Wang, Jianzhou
  • Wang, Shiqi
  • Yang, Wendong

Abstract

As an important clean energy source, wind power has strong influence on the safe operation of the power grid. Because its output depends directly on the wind speed, it is of great significance to accurately forecast the short-term wind speed. However, many previous studies have two shortcomings: they only focus on the original data without extracting the information of the error sequence; most of them are based on single optimization and neglected the importance of increasing the accuracy and stability simultaneously. Therefore, a novel hybrid model based on data feature extraction and multi-objective optimization combining the original wind speed series with the error sequence non-linearly is proposed for short-term forecasting. It consists of two steps: (a) obtain error sequence by the preliminary prediction, and (b) Combining the error sequence with the preliminary results to obtain the final predicted value. In addition, a hybrid Elman neural network (ENN) optimized by multi-objective grey wolf optimization (MOGWO) is proposed to achieve high accuracy and stability at the same time. To analyze the performance, four datasets collected from different wind fields are utilized as an example. The results demonstrate the proposed forecasting model reaches superior accuracy and stability when compared with other contrast models.

Suggested Citation

  • Wang, Jianzhou & Wang, Shiqi & Yang, Wendong, 2019. "A novel non-linear combination system for short-term wind speed forecast," Renewable Energy, Elsevier, vol. 143(C), pages 1172-1192.
    • Handle: RePEc:eee:renene:v:143:y:2019:i:c:p:1172-1192
    DOI: 10.1016/j.renene.2019.04.154
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096014811930638X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2019.04.154?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. Dong, Lei & Wang, Lijie & Khahro, Shahnawaz Farhan & Gao, Shuang & Liao, Xiaozhong, 2016. "Wind power day-ahead prediction with cluster analysis of NWP," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1206-1212.
    2. Niu, Xinsong & Wang, Jiyang, 2019. "A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 241(C), pages 519-539.
    3. Chengshi Tian & Yan Hao, 2018. "A Novel Nonlinear Combined Forecasting System for Short-Term Load Forecasting," Energies, MDPI, vol. 11(4), pages 1-34, March.
    4. Tian, Chengshi & Hao, Yan & Hu, Jianming, 2018. "A novel wind speed forecasting system based on hybrid data preprocessing and multi-objective optimization," Applied Energy, Elsevier, vol. 231(C), pages 301-319.
    5. 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.
    6. Jiyang Wang & Yuyang Gao & Xuejun Chen, 2018. "A Novel Hybrid Interval Prediction Approach Based on Modified Lower Upper Bound Estimation in Combination with Multi-Objective Salp Swarm Algorithm for Short-Term Load Forecasting," Energies, MDPI, vol. 11(6), pages 1-30, June.
    7. Yang, Wendong & Wang, Jianzhou & Niu, Tong & Du, Pei, 2019. "A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting," Applied Energy, Elsevier, vol. 235(C), pages 1205-1225.
    8. Jiang, Ping & Yang, Hufang & Heng, Jiani, 2019. "A hybrid forecasting system based on fuzzy time series and multi-objective optimization for wind speed forecasting," Applied Energy, Elsevier, vol. 235(C), pages 786-801.
    9. Wang, Yun & Wang, Jianzhou & Wei, Xiang, 2015. "A hybrid wind speed forecasting model based on phase space reconstruction theory and Markov model: A case study of wind farms in northwest China," Energy, Elsevier, vol. 91(C), pages 556-572.
    10. Wang, Jian-Zhou & Wang, Yun & Jiang, Ping, 2015. "The study and application of a novel hybrid forecasting model – A case study of wind speed forecasting in China," Applied Energy, Elsevier, vol. 143(C), pages 472-488.
    11. Paulo S G de Mattos Neto & George D C Cavalcanti & Francisco Madeiro & Tiago A E Ferreira, 2015. "An Approach to Improve the Performance of PM Forecasters," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-23, September.
    12. Wang, Jianzhou & Hu, Jianming & Ma, Kailiang & Zhang, Yixin, 2015. "A self-adaptive hybrid approach for wind speed forecasting," Renewable Energy, Elsevier, vol. 78(C), pages 374-385.
    13. Cadenas, E. & Jaramillo, O.A. & Rivera, W., 2010. "Analysis and forecasting of wind velocity in chetumal, quintana roo, using the single exponential smoothing method," Renewable Energy, Elsevier, vol. 35(5), pages 925-930.
    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. Naik, Jyotirmayee & Dash, Sujit & Dash, P.K. & Bisoi, Ranjeeta, 2018. "Short term wind power forecasting using hybrid variational mode decomposition and multi-kernel regularized pseudo inverse neural network," Renewable Energy, Elsevier, vol. 118(C), pages 180-212.
    16. Zhao, Xuejing & Wang, Chen & Su, Jinxia & Wang, Jianzhou, 2019. "Research and application based on the swarm intelligence algorithm and artificial intelligence for wind farm decision system," Renewable Energy, Elsevier, vol. 134(C), pages 681-697.
    17. Hufang Yang & Zaiping Jiang & Haiyan Lu, 2017. "A Hybrid Wind Speed Forecasting System Based on a ‘Decomposition and Ensemble’ Strategy and Fuzzy Time Series," Energies, MDPI, vol. 10(9), pages 1-30, September.
    18. Nourani Esfetang, Naser & Kazemzadeh, Rasool, 2018. "A novel hybrid technique for prediction of electric power generation in wind farms based on WIPSO, neural network and wavelet transform," Energy, Elsevier, vol. 149(C), pages 662-674.
    19. Wang, Jianzhou & Yang, Wendong & Du, Pei & Li, Yifan, 2018. "Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system," Energy, Elsevier, vol. 148(C), pages 59-78.
    20. Salcedo-Sanz, S. & Pastor-Sánchez, A. & Del Ser, J. & Prieto, L. & Geem, Z.W., 2015. "A Coral Reefs Optimization algorithm with Harmony Search operators for accurate wind speed prediction," Renewable Energy, Elsevier, vol. 75(C), pages 93-101.
    21. Hao, Yan & Tian, Chengshi, 2019. "A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting," Applied Energy, Elsevier, vol. 238(C), pages 368-383.
    22. 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.
    23. Yin, Peng-Yeng & Wu, Tsai-Hung & Hsu, Ping-Yi, 2017. "Simulation based risk management for multi-objective optimal wind turbine placement using MOEA/D," Energy, Elsevier, vol. 141(C), pages 579-597.
    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. Lu, Peng & Ye, Lin & Zhao, Yongning & Dai, Binhua & Pei, Ming & Tang, Yong, 2021. "Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges," Applied Energy, Elsevier, vol. 301(C).
    2. Wu, Binrong & Wang, Lin & Zeng, Yu-Rong, 2022. "Interpretable wind speed prediction with multivariate time series and temporal fusion transformers," Energy, Elsevier, vol. 252(C).
    3. Liu, Hui & Duan, Zhu & Chen, Chao, 2020. "Wind speed big data forecasting using time-variant multi-resolution ensemble model with clustering auto-encoder," Applied Energy, Elsevier, vol. 280(C).
    4. Liu, Chenyu & Zhang, Xuemin & Mei, Shengwei & Zhen, Zhao & Jia, Mengshuo & Li, Zheng & Tang, Haiyan, 2022. "Numerical weather prediction enhanced wind power forecasting: Rank ensemble and probabilistic fluctuation awareness," Applied Energy, Elsevier, vol. 313(C).
    5. Luca Di Persio & Nicola Fraccarolo & Andrea Veronese, 2024. "Wind Energy Production in Italy: A Forecasting Approach Based on Fractional Brownian Motion and Generative Adversarial Networks," Mathematics, MDPI, vol. 12(13), pages 1-16, July.
    6. Hu, Jianming & Heng, Jiani & Wen, Jiemei & Zhao, Weigang, 2020. "Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm," Renewable Energy, Elsevier, vol. 162(C), pages 1208-1226.
    7. Rita Teixeira & Adelaide Cerveira & Eduardo J. Solteiro Pires & José Baptista, 2024. "Advancing Renewable Energy Forecasting: A Comprehensive Review of Renewable Energy Forecasting Methods," Energies, MDPI, vol. 17(14), pages 1-30, July.
    8. Xu, Li & Ou, Yanxia & Cai, Jingjing & Wang, Jin & Fu, Yang & Bian, Xiaoyan, 2023. "Offshore wind speed assessment with statistical and attention-based neural network methods based on STL decomposition," Renewable Energy, Elsevier, vol. 216(C).
    9. 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).
    10. Yulong Bai & Lihong Tang & Manhong Fan & Xiaoyan Ma & Yang Yang, 2020. "Fuzzy First-Order Transition-Rules-Trained Hybrid Forecasting System for Short-Term Wind Speed Forecasts," Energies, MDPI, vol. 13(13), pages 1-21, June.
    11. Li, Ranran & Hu, Yucai & Heng, Jiani & Chen, Xueli, 2021. "A novel multiscale forecasting model for crude oil price time series," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    12. Sun, Zexian & Zhao, Mingyu & Zhao, Guohong, 2022. "Hybrid model based on VMD decomposition, clustering analysis, long short memory network, ensemble learning and error complementation for short-term wind speed forecasting assisted by Flink platform," Energy, Elsevier, vol. 261(PB).
    13. Yu Jin & Honggang Guo & Jianzhou Wang & Aiyi Song, 2020. "A Hybrid System Based on LSTM for Short-Term Power Load Forecasting," Energies, MDPI, vol. 13(23), pages 1-32, November.
    14. Yidi Ren & Hua Li & Hsiung-Cheng Lin, 2019. "Optimization of Feedforward Neural Networks Using an Improved Flower Pollination Algorithm for Short-Term Wind Speed Prediction," Energies, MDPI, vol. 12(21), pages 1-17, October.
    15. Shang, Zhihao & He, Zhaoshuang & Chen, Yao & Chen, Yanhua & Xu, MingLiang, 2022. "Short-term wind speed forecasting system based on multivariate time series and multi-objective optimization," Energy, Elsevier, vol. 238(PC).
    16. Guo, Nai-Zhi & Shi, Ke-Zhong & Li, Bo & Qi, Liang-Wen & Wu, Hong-Hui & Zhang, Zi-Liang & Xu, Jian-Zhong, 2022. "A physics-inspired neural network model for short-term wind power prediction considering wake effects," Energy, Elsevier, vol. 261(PA).

    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. Liu, Hui & Chen, Chao, 2019. "Data processing strategies in wind energy forecasting models and applications: A comprehensive review," Applied Energy, Elsevier, vol. 249(C), pages 392-408.
    2. Jianzhou Wang & Chunying Wu & Tong Niu, 2019. "A Novel System for Wind Speed Forecasting Based on Multi-Objective Optimization and Echo State Network," Sustainability, MDPI, vol. 11(2), pages 1-34, January.
    3. Lu, Peng & Ye, Lin & Zhao, Yongning & Dai, Binhua & Pei, Ming & Tang, Yong, 2021. "Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges," Applied Energy, Elsevier, vol. 301(C).
    4. Yang, Wendong & Sun, Shaolong & Hao, Yan & Wang, Shouyang, 2022. "A novel machine learning-based electricity price forecasting model based on optimal model selection strategy," Energy, Elsevier, vol. 238(PC).
    5. Wu, Chunying & Wang, Jianzhou & Chen, Xuejun & Du, Pei & Yang, Wendong, 2020. "A novel hybrid system based on multi-objective optimization for wind speed forecasting," Renewable Energy, Elsevier, vol. 146(C), pages 149-165.
    6. Niu, Xinsong & Wang, Jiyang, 2019. "A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 241(C), pages 519-539.
    7. Hao, Yan & Tian, Chengshi, 2019. "A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting," Applied Energy, Elsevier, vol. 238(C), pages 368-383.
    8. Liu, Zhenkun & Jiang, Ping & Zhang, Lifang & Niu, Xinsong, 2020. "A combined forecasting model for time series: Application to short-term wind speed forecasting," Applied Energy, Elsevier, vol. 259(C).
    9. 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).
    10. Zhou, Qingguo & Wang, Chen & Zhang, Gaofeng, 2019. "Hybrid forecasting system based on an optimal model selection strategy for different wind speed forecasting problems," Applied Energy, Elsevier, vol. 250(C), pages 1559-1580.
    11. Liu, Hui & Mi, Xiwei & Li, Yanfei & Duan, Zhu & Xu, Yinan, 2019. "Smart wind speed deep learning based multi-step forecasting model using singular spectrum analysis, convolutional Gated Recurrent Unit network and Support Vector Regression," Renewable Energy, Elsevier, vol. 143(C), pages 842-854.
    12. Yechi Zhang & Jianzhou Wang & Haiyan Lu, 2019. "Research and Application of a Novel Combined Model Based on Multiobjective Optimization for Multistep-Ahead Electric Load Forecasting," Energies, MDPI, vol. 12(10), pages 1-27, May.
    13. Kong, Xiangyu & Li, Chuang & Wang, Chengshan & Zhang, Yusen & Zhang, Jian, 2020. "Short-term electrical load forecasting based on error correction using dynamic mode decomposition," Applied Energy, Elsevier, vol. 261(C).
    14. Yang, Wendong & Wang, Jianzhou & Niu, Tong & Du, Pei, 2019. "A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting," Applied Energy, Elsevier, vol. 235(C), pages 1205-1225.
    15. Tian, Chengshi & Hao, Yan & Hu, Jianming, 2018. "A novel wind speed forecasting system based on hybrid data preprocessing and multi-objective optimization," Applied Energy, Elsevier, vol. 231(C), pages 301-319.
    16. Yukun Wang & Aiying Zhao & Xiaoxue Wei & Ranran Li, 2023. "A Novel Ensemble Model Based on an Advanced Optimization Algorithm for Wind Speed Forecasting," Energies, MDPI, vol. 16(14), pages 1-19, July.
    17. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
    18. Mohammed A. A. Al-qaness & Ahmed A. Ewees & Mohamed Abd Elaziz & Ahmed H. Samak, 2022. "Wind Power Forecasting Using Optimized Dendritic Neural Model Based on Seagull Optimization Algorithm and Aquila Optimizer," Energies, MDPI, vol. 15(24), pages 1-14, December.
    19. Lu, Hongfang & Ma, Xin & Huang, Kun & Azimi, Mohammadamin, 2020. "Prediction of offshore wind farm power using a novel two-stage model combining kernel-based nonlinear extension of the Arps decline model with a multi-objective grey wolf optimizer," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    20. Nie, Ying & Liang, Ni & Wang, Jianzhou, 2021. "Ultra-short-term wind-speed bi-forecasting system via artificial intelligence and a double-forecasting scheme," Applied Energy, Elsevier, vol. 301(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:renene:v:143:y:2019:i:c:p:1172-1192. 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/renewable-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.