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Research and application of a combined model based on multi-objective optimization for electrical load forecasting

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  • Xiao, Liye
  • Shao, Wei
  • Yu, Mengxia
  • Ma, Jing
  • Jin, Congjun

Abstract

Short-term load forecasting (STLF) plays an irreplaceable role in the efficient management of electrical systems but remains an extremely challenging task. To achieve the goal of load forecasting with both accuracy and stability, a combined model based on a multi-objective optimization algorithm, the multi-objective flower pollination algorithm (MOFPA), is developed in this study. In this combined model, MOPFA is used to optimize the weights of single models to simultaneously obtain high accuracy and great stability, which are two mostly independent objectives and are equally important to the model effectiveness. Data preprocessing techniques, such as the fast ensemble empirical mode decomposition and multiple seasonal patterns, are also incorporated in this model. Case studies of half-hourly electrical load data from the State of Victoria, the State of Queensland, and New South Wales, Australia, are considered as illustrative examples to evaluate the effectiveness and efficiency of the developed combined model. The experimental results clearly show that both the accuracy and stability of the combined model are superior to those of the single models.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:119:y:2017:i:c:p:1057-1074
    DOI: 10.1016/j.energy.2016.11.035
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    1. Hernández, Luis & Baladrón, Carlos & Aguiar, Javier M. & Carro, Belén & Sánchez-Esguevillas, Antonio & Lloret, Jaime, 2014. "Artificial neural networks for short-term load forecasting in microgrids environment," Energy, Elsevier, vol. 75(C), pages 252-264.
    2. Bahrami, Saadat & Hooshmand, Rahmat-Allah & Parastegari, Moein, 2014. "Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm," Energy, Elsevier, vol. 72(C), pages 434-442.
    3. Pei Du & Yu Jin & Kequan Zhang, 2016. "A Hybrid Multi-Step Rolling Forecasting Model Based on SSA and Simulated Annealing—Adaptive Particle Swarm Optimization for Wind Speed," Sustainability, MDPI, vol. 8(8), pages 1-25, August.
    4. 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.
    5. Luis Hernández & Carlos Baladrón & Javier M. Aguiar & Lorena Calavia & Belén Carro & Antonio Sánchez-Esguevillas & Francisco Pérez & Ángel Fernández & Jaime Lloret, 2014. "Artificial Neural Network for Short-Term Load Forecasting in Distribution Systems," Energies, MDPI, vol. 7(3), pages 1-23, March.
    6. Xiao, Ling & Wang, Jianzhou & Dong, Yao & Wu, Jie, 2015. "Combined forecasting models for wind energy forecasting: A case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 271-288.
    7. Wang, Yung-Hung & Yeh, Chien-Hung & Young, Hsu-Wen Vincent & Hu, Kun & Lo, Men-Tzung, 2014. "On the computational complexity of the empirical mode decomposition algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 400(C), pages 159-167.
    8. Amjady, N. & Keynia, F., 2009. "Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm," Energy, Elsevier, vol. 34(1), pages 46-57.
    9. 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.
    10. Che, JinXing & Wang, JianZhou, 2014. "Short-term load forecasting using a kernel-based support vector regression combination model," Applied Energy, Elsevier, vol. 132(C), pages 602-609.
    11. Che, Jinxing & Wang, Jianzhou & Wang, Guangfu, 2012. "An adaptive fuzzy combination model based on self-organizing map and support vector regression for electric load forecasting," Energy, Elsevier, vol. 37(1), pages 657-664.
    12. 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.
    13. Richard L. Daniels & Robert J. Chambers, 1990. "Multiobjective flow‐shop scheduling," Naval Research Logistics (NRL), John Wiley & Sons, vol. 37(6), pages 981-995, December.
    14. Wang, Jianzhou & Zhu, Suling & Zhang, Wenyu & Lu, Haiyan, 2010. "Combined modeling for electric load forecasting with adaptive particle swarm optimization," Energy, Elsevier, vol. 35(4), pages 1671-1678.
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