Pinball-Huber boosted extreme learning machine regression : A multiobjective approach to accurate power load forecasting

Journal article

Yang, Yang, Lou, Hao, Wang, Zijin and Wu, Jinran. (2024). Pinball-Huber boosted extreme learning machine regression : A multiobjective approach to accurate power load forecasting. Applied Intelligence. 54(17-18), pp. 8745-8760. https://doi.org/10.1007/s10489-024-05651-3
AuthorsYang, Yang, Lou, Hao, Wang, Zijin and Wu, Jinran
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.

Keywordsload forecasting ; robust loss function; multi-objective optimization; neural networks; extreme learning machine
Year01 Jan 2024
JournalApplied Intelligence
Journal citation54 (17-18), pp. 8745-8760
PublisherSpringer
ISSN0924-669X
Digital Object Identifier (DOI)https://doi.org/10.1007/s10489-024-05651-3
Web address (URL)https://link.springer.com/article/10.1007/s10489-024-05651-3
Open accessPublished as ‘gold’ (paid) open access
Research or scholarlyResearch
Page range8745-8760
Publisher's version
License
File Access Level
Open
Output statusPublished
Publication dates
Online03 Jul 2024
Publication process dates
Accepted25 Jun 2024
Deposited20 Sep 2024
ARC Funded ResearchThis output has been funded, wholly or partially, under the Australian Research Council Act 2001
Grant IDDP160104292
Additional information

© 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).

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Place of publicationUnited States
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