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Pinball-Huber boosted extreme learning machine regression : A multiobjective approach to accurate power load forecasting
Yang, Yang Lou, Hao Wang, Zijin Wu, Jinran
Yang, Yang
Lou, Hao
Wang, Zijin
Wu, Jinran
Author
Abstract
Power load data frequently display outliers and an uneven distribution of noise. To tackle this issue, we present a forecasting model based on an improved extreme learning machine (ELM). Specifically, we introduce the novel Pinball-Huber robust loss function as the objective function in training. The loss function enhances the precision by assigning distinct penalties to errors based on their directions. We employ a genetic algorithm, combined with a swift nondominated sorting technique, for multiobjective optimization in the ELM-Pinball-Huber context. This method simultaneously reduces training errors while streamlining model structure. We practically apply the integrated model to forecast power load data in Taixing City, which is situated in the southern part of Jiangsu Province. The empirical findings confirm the method’s effectiveness.
Keywords
load forecasting, robust loss function, multi-objective optimization, neural networks, extreme learning machine
Date
2024
Type
Journal article
Journal
Book
Volume
54
Issue
17-18
Page Range
8745-8760
Article Number
ACU Department
Institute for Positive Psychology and Education
Faculty of Education and Arts
Faculty of Education and Arts
Relation URI
Event URL
Open Access Status
Published as ‘gold’ (paid) open access
License
CC BY 4.0
File Access
Open
Notes
© The Author(s) 2024
The work is supported by the Australian Research Council project (grant number DP160104292), the National Natural Science Foundation of China under Grant 61873130 and Grant 61833011, the Natural Science Foundation of Jiangsu Province under Grant BK20191377, the 1311 Talent Project of Nanjing University of Posts and Telecommunications, and “Chunhui Program” Collaborative Scientific Research Project (202202004).
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Open Access funding enabled and organized by CAUL and its Member Institutions.
The work is supported by the Australian Research Council project (grant number DP160104292), the National Natural Science Foundation of China under Grant 61873130 and Grant 61833011, the Natural Science Foundation of Jiangsu Province under Grant BK20191377, the 1311 Talent Project of Nanjing University of Posts and Telecommunications, and “Chunhui Program” Collaborative Scientific Research Project (202202004).
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Open Access funding enabled and organized by CAUL and its Member Institutions.