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A hybrid forecasting model with parameter optimization for short-term load forecasting of micro-grids

Author

Listed:
  • Liu, Nian
  • Tang, Qingfeng
  • Zhang, Jianhua
  • Fan, Wei
  • Liu, Jie

Abstract

Short-term load forecasting is an important part in the energy management of micro-grid. The forecasting errors directly affect the economic efficiency of operation. Compared to larger-scale power grid, micro-grid is more difficult to realize the short-term load forecasting for its smaller capacity and higher randomness. A hybrid load forecasting model with parameter optimization is proposed for short-term load forecasting of micro-grids, being composed of Empirical Mode Decomposition (EMD), Extended Kalman Filter (EKF), Extreme Learning Machine with Kernel (KELM) and Particle Swarm Optimization (PSO). Firstly, the time-series load data are decomposed into a number of Intrinsic Mode Function (IMF) components through EMD. Two typical different forecasting algorithms (EKF and KELM) are adopted to predict different kinds of IMF components. Particle Swarm Optimization (PSO) is used to optimize the parameters in the model. Considering the limited computation resources, an implementation mode based on off-line parameter optimization, period parameters updating and on-line load forecasting is proposed. Finally, four typical micro-grids with different users and capacities are used to test the accuracy and efficiency of the forecasting model.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:129:y:2014:i:c:p:336-345
    DOI: 10.1016/j.apenergy.2014.05.023
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    References listed on IDEAS

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    1. Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Narimani, Mohammad Rasoul, 2012. "An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation," Applied Energy, Elsevier, vol. 99(C), pages 455-470.
    2. AlRashidi, M.R. & EL-Naggar, K.M., 2010. "Long term electric load forecasting based on particle swarm optimization," Applied Energy, Elsevier, vol. 87(1), pages 320-326, January.
    3. An, Xueli & Jiang, Dongxiang & Li, Shaohua & Zhao, Minghao, 2011. "Application of the ensemble empirical mode decomposition and Hilbert transform to pedestal looseness study of direct-drive wind turbine," Energy, Elsevier, vol. 36(9), pages 5508-5520.
    4. 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.
    5. Chen, Yen-Haw & Lu, Su-Ying & Chang, Yung-Ruei & Lee, Ta-Tung & Hu, Ming-Che, 2013. "Economic analysis and optimal energy management models for microgrid systems: A case study in Taiwan," Applied Energy, Elsevier, vol. 103(C), pages 145-154.
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