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A hybrid forecasting model based on date-framework strategy and improved feature selection technology for short-term load forecasting

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  • Jiang, Ping
  • Liu, Feng
  • Song, Yiliao

Abstract

The ultimate issue in electricity loads modelling is to improve forecasting accuracy as well as guarantee a robust prediction result, which will save considerable manual labor material resources and economic consumption. For addressing this challenge, many researchers are committed to investigating highly accurate forecasting models, and feature selection (FS) technologies are considered as a powerful tool to improve performance of models in many literature. However, common FS technologies applied for Short-term load forecasting (STLF) ignore to select date information of the observed series as feature candidates and pay less attention to reduction rates of feature candidates, which will result in loss of date information and redundancy of features. Both drawbacks provide a significant roadblock for improving forecasting accuracy. Aiming to overcome both drawbacks and develop an effective model for STLF, this paper successfully investigates the date-framework strategy (DFS) to construct the pool of features and develops an FS technology, genetic algorithm binary improved cuckoo search (GABICS), to search a solution with the lowest reduction rate. Assigning the extreme learning machine (ELM) to be the forecast, GABICS-DFS-ELM not only obtains a minimum and effective subset of features but also has a satisfactory forecasting result with high accuracy and robustness.

Suggested Citation

  • Jiang, Ping & Liu, Feng & Song, Yiliao, 2017. "A hybrid forecasting model based on date-framework strategy and improved feature selection technology for short-term load forecasting," Energy, Elsevier, vol. 119(C), pages 694-709.
    • Handle: RePEc:eee:energy:v:119:y:2017:i:c:p:694-709
    DOI: 10.1016/j.energy.2016.11.034
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    References listed on IDEAS

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    1. Wang, Jianzhou & Zhu, Wenjin & Zhang, Wenyu & Sun, Donghuai, 2009. "A trend fixed on firstly and seasonal adjustment model combined with the [epsilon]-SVR for short-term forecasting of electricity demand," Energy Policy, Elsevier, vol. 37(11), pages 4901-4909, November.
    2. Charlton, Nathaniel & Singleton, Colin, 2014. "A refined parametric model for short term load forecasting," International Journal of Forecasting, Elsevier, vol. 30(2), pages 364-368.
    3. Zhu, Suling & Wang, Jianzhou & Zhao, Weigang & Wang, Jujie, 2011. "A seasonal hybrid procedure for electricity demand forecasting in China," Applied Energy, Elsevier, vol. 88(11), pages 3807-3815.
    4. Jain, Rishee K. & Smith, Kevin M. & Culligan, Patricia J. & Taylor, John E., 2014. "Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy," Applied Energy, Elsevier, vol. 123(C), pages 168-178.
    5. Wang, Jianjun & Li, Li & Niu, Dongxiao & Tan, Zhongfu, 2012. "An annual load forecasting model based on support vector regression with differential evolution algorithm," Applied Energy, Elsevier, vol. 94(C), pages 65-70.
    6. Moazzami, M. & Khodabakhshian, A. & Hooshmand, R., 2013. "A new hybrid day-ahead peak load forecasting method for Iran’s National Grid," Applied Energy, Elsevier, vol. 101(C), pages 489-501.
    7. Javed, Fahad & Arshad, Naveed & Wallin, Fredrik & Vassileva, Iana & Dahlquist, Erik, 2012. "Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting," Applied Energy, Elsevier, vol. 96(C), pages 150-160.
    8. Masa-Bote, D. & Castillo-Cagigal, M. & Matallanas, E. & Caamaño-Martín, E. & Gutiérrez, A. & Monasterio-Huelín, F. & Jiménez-Leube, J., 2014. "Improving photovoltaics grid integration through short time forecasting and self-consumption," Applied Energy, Elsevier, vol. 125(C), pages 103-113.
    9. Walton, S. & Hassan, O. & Morgan, K. & Brown, M.R., 2011. "Modified cuckoo search: A new gradient free optimisation algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 44(9), pages 710-718.
    10. Mamlook, Rustum & Badran, Omar & Abdulhadi, Emad, 2009. "A fuzzy inference model for short-term load forecasting," Energy Policy, Elsevier, vol. 37(4), pages 1239-1248, April.
    11. 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.
    12. Liu, Nian & Tang, Qingfeng & Zhang, Jianhua & Fan, Wei & Liu, Jie, 2014. "A hybrid forecasting model with parameter optimization for short-term load forecasting of micro-grids," Applied Energy, Elsevier, vol. 129(C), pages 336-345.
    13. Lin, Whei-Min & Gow, Hong-Jey & Tsai, Ming-Tang, 2010. "An enhanced radial basis function network for short-term electricity price forecasting," Applied Energy, Elsevier, vol. 87(10), pages 3226-3234, October.
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