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A hybrid robust system considering outliers for electric load series forecasting

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Abstract

Electric load forecasting has become crucial to the safe operation of power grids and cost reduction in the production of power. Although numerous electric load forecasting models have been proposed, most of them are still limited by poor effectiveness in the model training and a sensitivity to outliers. The limitations of current methods may lead to extra operational costs of a power system or even disrupt its power distribution and network safety. To this end, we propose a new hybrid load-forecasting model, which is based on a robust extreme-learning machine and an improved whale optimization algorithm. Specifically, Huber loss, which is insensitive to outliers, is proposed as the objective function in extreme learning machine (ELM) training. In addition, an improved whale optimization algorithm is designed for the robust ELM training, in which a cellular automaton mechanism is used to enhance the local search. To verify our improved whale optimization algorithm, some experiments were then conducted based on seven benchmark test functions. Due to the enhancement of the local search, the improved optimizer was around 7% superior to the basic. Finally, our proposed hybrid forecasting model was validated by two real electric load datasets (Nanjing and New South Wales), and the experimental results confirmed that the proposed hybrid load-forecasting model could achieve satisfying improvements in both datasets.

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Acknowledgements

This work was supported by National Natural Science Foundation of China under Grants 61873130, 61833011 and 61833008, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20191377 and BK20191376, in part by 1311 Talent Project of Nanjing University of Posts and Telecommunications, in part by Scientific Foundation of Nanjing University of Posts and Telecommunications (NUPTSF) under Grants NY220102, NY220194 and 2020XZZ11. Also, this work was supported by the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), grant number CE140100049.

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Authors and Affiliations

  1. College of Automation, College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, People’s Republic of China

    Yang Yang, Zhenghang Tao, Chen Qian, Yuchao Gao & Hu Zhou

  2. School of Computer Science, Queensland University of Technology, Queensland, Australia

    Zhe Ding

  3. School of Mathematical Science, Queensland University of Technology, Queensland, Australia

    Jinran Wu

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  1. Yang Yang
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Correspondence to Jinran Wu.

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Credit authorship contribution statement

Yang Yang: Project administration, Supervision, Investigation Zhenghang Tao: Software, Visualization, Writing - original draft. Yuchao Gao: Visualization, Writing - original draft. Chen Qian: Formal analysis, Writing - original draft Hu Zhou: Writing – review & editing. Zhe Ding: Writing- review & editing. Jinran Wu: Supervision, Project administration,Investigation, Writing – review & editing.

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Yang, Y., Tao, Z., Qian, C. et al. A hybrid robust system considering outliers for electric load series forecasting. Appl Intell 52, 1630–1652 (2022). https://doi.org/10.1007/s10489-021-02473-5

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