A hybrid robust system considering outliers for electric load series forecasting
Journal article
Yang, Yang, Tao, Zhenghang, Qian, Chen, Gao, Yuchao, Zhou, Hu, Ding, Zhe and Wu, Jinran. (2022). A hybrid robust system considering outliers for electric load series forecasting. Applied Intelligence. 52(2), pp. 1630-1652. https://doi.org/10.1007/s10489-021-02473-5
Authors | Yang, Yang, Tao, Zhenghang, Qian, Chen, Gao, Yuchao, Zhou, Hu, Ding, Zhe and Wu, Jinran |
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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. |
Keywords | outilers; whale optimization algorithm; cellular automata; load forecasting; robust regression |
Year | 2022 |
Journal | Applied Intelligence |
Journal citation | 52 (2), pp. 1630-1652 |
Publisher | Springer |
ISSN | 0924-669X |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s10489-021-02473-5 |
Scopus EID | 2-s2.0-85106403524 |
Open access | Published as green open access |
Page range | 1630-1652 |
Funder | National Natural Science Foundation of China (NSFC) |
Natural Science Foundation of Jiangsu Province | |
Nanjing University of Posts and Telecommunications (NUPTSF), China | |
Australian Research Council (ARC) | |
Author's accepted manuscript | License All rights reserved File Access Level Open |
Publisher's version | License All rights reserved File Access Level Controlled |
Output status | Published |
Publication dates | |
Online | 25 May 2021 |
Publication process dates | |
Accepted | 22 Apr 2021 |
Deposited | 07 Jul 2023 |
ARC Funded Research | This output has been funded, wholly or partially, under the Australian Research Council Act 2001 |
Grant ID | 61873130 |
61833011 | |
61833008 | |
BK20191377 | |
BK20191376 | |
NY220102 | |
NY220194 | |
2020XZZ11 | |
CE140100049 |
https://acuresearchbank.acu.edu.au/item/8z3yx/a-hybrid-robust-system-considering-outliers-for-electric-load-series-forecasting
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Author's accepted manuscript
AM_Yang_2022_A_hybrid_robust_system_considering_outliers.pdf | |
License: All rights reserved | |
File access level: Open |
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