Enhancing electricity consumption forecasting in limited dataset: A simple stacked ensemble approach incorporating simple linear and support vector regression for Malaysia

Enhancing electricity consumption forecasting in limited dataset: A simple stacked ensemble approach incorporating simple linear and support vector regression for Malaysia

Rapid population growth and urbanization, coupled with technological advancements, have driven higher electricity demand, predominantly sourced from contributors to climate change. This article introduces a novel artificial intelligence (AI) time-series algorithm, a simple stacked ensemble of simple...

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Bibliographic Details
Main Authors: Chuan, Zun Liang, Shao Jie, Ong, Yim Hin, Tham, Siti Nur Syamimi, Mat Zain, Yunalis Amani, Abdul Rashid, Ainur Naseiha, Kamarudin
Format: Article
Language:English
English
Published: Penerbit UTM 2025
Subjects:
Q Science (General)
QA Mathematics
QD Chemistry
T Technology (General)
TP Chemical technology
Online Access:http://umpir.ump.edu.my/id/eprint/43575/
http://umpir.ump.edu.my/id/eprint/43575/1/IJBES%20%282025%29.pdf
http://umpir.ump.edu.my/id/eprint/43575/7/Enhancing%20electricity%20consumption%20forecasting%20in%20limited%20dataset_A%20simple%20stacked%20ensemble%20approach%20incorporating%20simple%20linear%20and%20support%20vector%20regression%20for%20Malaysia_abs.pdf

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