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A Fuzzy Group Forecasting Model Based on Least Squares Support Vector Machine (LS-SVM) for Short-Term Wind Power

Author

Listed:
  • Qian Zhang

    (School of Economics and Management, North China Electric Power University, Baoding 071003, China)

  • Kin Keung Lai

    (Department of Management Science, City University of Hong Kong, Kowloon, Hong Kong
    School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Dongxiao Niu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Qiang Wang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Xuebin Zhang

    (School of Economics and Management, North China Electric Power University, Baoding 071003, China)

Abstract

Many models have been developed to forecast wind farm power output. It is generally difficult to determine whether the performance of one model is consistently better than that of another model under all circumstances. Motivated by this finding, we aimed to integrate groups of models into an aggregated model using fuzzy theory to obtain further performance improvements. First, three groups of least squares support vector machine (LS-SVM) forecasting models were developed: univariate LS-SVM models, hybrid models using auto-regressive moving average (ARIMA) and LS-SVM and multivariate LS-SVM models. Each group of models is selected by a decorrelation maximisation method, and the remaining models can be regarded as experts in forecasting. Next, fuzzy aggregation and a defuzzification procedure are used to combine all of these forecasting results into the final forecast. For sample randomization, we statistically compare models. Results show that this group-forecasting model performs well in terms of accuracy and consistency.

Suggested Citation

  • Qian Zhang & Kin Keung Lai & Dongxiao Niu & Qiang Wang & Xuebin Zhang, 2012. "A Fuzzy Group Forecasting Model Based on Least Squares Support Vector Machine (LS-SVM) for Short-Term Wind Power," Energies, MDPI, vol. 5(9), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:5:y:2012:i:9:p:3329-3346:d:19867
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    References listed on IDEAS

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    2. Chia-Sheng Tu & Chih-Ming Hong & Hsi-Shan Huang & Chiung-Hsing Chen, 2020. "Short Term Wind Power Prediction Based on Data Regression and Enhanced Support Vector Machine," Energies, MDPI, vol. 13(23), pages 1-18, November.
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    6. Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
    7. 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.
    8. Xiaoya Shang & Zhigang Li & Tianyao Ji & P. Z. Wu & Qinghua Wu, 2017. "Online Area Load Modeling in Power Systems Using Enhanced Reinforcement Learning," Energies, MDPI, vol. 10(11), pages 1-17, November.
    9. Li Wang & Jiguang Yue & Yongqing Su & Feng Lu & Qiang Sun, 2017. "A Novel Remaining Useful Life Prediction Approach for Superbuck Converter Circuits Based on Modified Grey Wolf Optimizer-Support Vector Regression," Energies, MDPI, vol. 10(4), pages 1-22, April.
    10. Xiaomin Xu & Dongxiao Niu & Ming Fu & Huicong Xia & Han Wu, 2015. "A Multi Time Scale Wind Power Forecasting Model of a Chaotic Echo State Network Based on a Hybrid Algorithm of Particle Swarm Optimization and Tabu Search," Energies, MDPI, vol. 8(11), pages 1-21, November.
    11. Jian Yang & Yu Liu & Shangguang Jiang & Yazhou Luo & Nianzhang Liu & Deping Ke, 2022. "A Method of Probability Distribution Modeling of Multi-Dimensional Conditions for Wind Power Forecast Error Based on MNSGA-II-Kmeans," Energies, MDPI, vol. 15(7), pages 1-21, March.
    12. Luis Hernández & Carlos Baladrón & Javier M. Aguiar & Lorena Calavia & Belén Carro & Antonio Sánchez-Esguevillas & Pablo García & Jaime Lloret, 2013. "Experimental Analysis of the Input Variables’ Relevance to Forecast Next Day’s Aggregated Electric Demand Using Neural Networks," Energies, MDPI, vol. 6(6), pages 1-22, June.
    13. Maria Grazia De Giorgi & Stefano Campilongo & Antonio Ficarella & Paolo Maria Congedo, 2014. "Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)," Energies, MDPI, vol. 7(8), pages 1-22, August.
    14. Emanuele Ogliari & Francesco Grimaccia & Sonia Leva & Marco Mussetta, 2013. "Hybrid Predictive Models for Accurate Forecasting in PV Systems," Energies, MDPI, vol. 6(4), pages 1-12, April.

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