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A combined model based on data pre-analysis and weight coefficients optimization for electrical load forecasting


  • Xiao, Liye
  • Wang, Jianzhou
  • Hou, Ru
  • Wu, Jie


Electrical load forecasting has always played a key role in power system administration, planning for energy transfer scheduling and load dispatch. For electrical load forecasting, due to the fact that combined model has the capacity to effectively calculate the seasonality and nonlinearity shown in the electrical load data, absorb the merits and avoid the limitations of the individual models, a new combined model is presented. In this model, the data pre-analysis is used to reduce the interferences from the data, meanwhile cuckoo search is firstly used to optimize weight coefficients of the combined model. To evaluate the forecast performance of the proposed combined model, half-hourly electricity power data from February 2006 to 2009 for the State of New South Wales, August 2006 to 2008 for the State of Victoria and November 2006 to 2008 for the State of Queensland, Australia, were used in this paper as a case study. The experimental results show that the proposed combined model is superior to the individual forecasting models regarding forecast performance.

Suggested Citation

  • Xiao, Liye & Wang, Jianzhou & Hou, Ru & Wu, Jie, 2015. "A combined model based on data pre-analysis and weight coefficients optimization for electrical load forecasting," Energy, Elsevier, vol. 82(C), pages 524-549.
  • Handle: RePEc:eee:energy:v:82:y:2015:i:c:p:524-549
    DOI: 10.1016/

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    References listed on IDEAS

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    3. Xiao, Liye & Shao, Wei & Wang, Chen & Zhang, Kequan & Lu, Haiyan, 2016. "Research and application of a hybrid model based on multi-objective optimization for electrical load forecasting," Applied Energy, Elsevier, vol. 180(C), pages 213-233.
    4. Yuyang Gao & Chao Qu & Kequan Zhang, 2016. "A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting," Energies, MDPI, Open Access Journal, vol. 9(10), pages 1-28, September.
    5. Min-Liang Huang, 2016. "Hybridization of Chaotic Quantum Particle Swarm Optimization with SVR in Electric Demand Forecasting," Energies, MDPI, Open Access Journal, vol. 9(6), pages 1-16, May.
    6. Xiao, Liye & Shao, Wei & Yu, Mengxia & Ma, Jing & Jin, Congjun, 2017. "Research and application of a combined model based on multi-objective optimization for electrical load forecasting," Energy, Elsevier, vol. 119(C), pages 1057-1074.
    7. Xiao, Liye & Shao, Wei & Liang, Tulu & Wang, Chen, 2016. "A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting," Applied Energy, Elsevier, vol. 167(C), pages 135-153.
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    11. Ghasemi, A. & Shayeghi, H. & Moradzadeh, M. & Nooshyar, M., 2016. "A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management," Applied Energy, Elsevier, vol. 177(C), pages 40-59.
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    19. Hur, J. & Baldick, R., 2016. "A new merit function to accommodate high wind power penetration of WGRs (wind generating resources)," Energy, Elsevier, vol. 108(C), pages 34-40.
    20. Yang, YouLong & Che, JinXing & Li, YanYing & Zhao, YanJun & Zhu, SuLing, 2016. "An incremental electric load forecasting model based on support vector regression," Energy, Elsevier, vol. 113(C), pages 796-808.
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