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
AuthorsYang, Yang, Tao, Zhenghang, Qian, Chen, Gao, Yuchao, Zhou, Hu, Ding, Zhe and Wu, Jinran
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.

Keywordsoutilers; whale optimization algorithm; cellular automata; load forecasting; robust regression
Year2022
JournalApplied Intelligence
Journal citation52 (2), pp. 1630-1652
PublisherSpringer
ISSN0924-669X
Digital Object Identifier (DOI)https://doi.org/10.1007/s10489-021-02473-5
Scopus EID2-s2.0-85106403524
Open accessPublished as green open access
Page range1630-1652
FunderNational 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
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All rights reserved
File Access Level
Open
Publisher's version
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All rights reserved
File Access Level
Controlled
Output statusPublished
Publication dates
Online25 May 2021
Publication process dates
Accepted22 Apr 2021
Deposited07 Jul 2023
ARC Funded ResearchThis output has been funded, wholly or partially, under the Australian Research Council Act 2001
Grant ID61873130
61833011
61833008
BK20191377
BK20191376
NY220102
NY220194
2020XZZ11
CE140100049
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