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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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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/43575/1/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
http://umpir.ump.edu.my/id/eprint/43575/
https://doi.org/10.11113/ijbes.v12.n1.1254
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spelling my.ump.umpir.435752025-01-15T06:24:04Z http://umpir.ump.edu.my/id/eprint/43575/ Enhancing electricity consumption forecasting in limited dataset: A simple stacked ensemble approach incorporating simple linear and support vector regression for Malaysia Chuan, Zun Liang Shao Jie, Ong Yim Hin, Tham Siti Nur Syamimi, Mat Zain Yunalis Amani, Abdul Rashid Ainur Naseiha, Kamarudin Q Science (General) QA Mathematics QD Chemistry T Technology (General) TP Chemical technology 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 linear regression (SLR) and Support Vector Regression (SVR), designed to forecast Malaysia’s annual electricity consumption, particularly in scenarios with limited datasets utilizing the Cross Industry Standard Process for Data Mining (CRISP-DM) data science methodology. Analysis revealed that this simple stacked ensemble SVR-based time-series algorithm, employing an ε -insensitive loss function with a third-degree polynomial kernel, outperformed 71 other SVR-based algorithms, including four time-series algorithms from the previous study. The algorithm’s forecasting insights from the formulated algorithm could guide policymakers in establishing more effective regulations aligned with Sustainable Development Goals (SDGs) such as affordable and clean energy (SDG7), decent work and economic growth (SDG8), industry, innovation and infrastructure (SDG9), sustainable cities and communities (SDG11), responsible consumption and production (SDG12), and climate action (SDG13), which benefit economic, environmental, human, and social. Penerbit UTM 2025 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/43575/1/IJBES%20%282025%29.pdf pdf en 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 Chuan, Zun Liang and Shao Jie, Ong and Yim Hin, Tham and Siti Nur Syamimi, Mat Zain and Yunalis Amani, Abdul Rashid and Ainur Naseiha, Kamarudin (2025) Enhancing electricity consumption forecasting in limited dataset: A simple stacked ensemble approach incorporating simple linear and support vector regression for Malaysia. International Journal of Built Environment and Sustainability, 12 (1). pp. 9-21. ISSN 2289–8948 (eISSN). (Published) https://doi.org/10.11113/ijbes.v12.n1.1254 10.11113/ijbes.v12.n1.1254
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic Q Science (General)
QA Mathematics
QD Chemistry
T Technology (General)
TP Chemical technology
spellingShingle Q Science (General)
QA Mathematics
QD Chemistry
T Technology (General)
TP Chemical technology
Chuan, Zun Liang
Shao Jie, Ong
Yim Hin, Tham
Siti Nur Syamimi, Mat Zain
Yunalis Amani, Abdul Rashid
Ainur Naseiha, Kamarudin
Enhancing electricity consumption forecasting in limited dataset: A simple stacked ensemble approach incorporating simple linear and support vector regression for Malaysia
description 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 linear regression (SLR) and Support Vector Regression (SVR), designed to forecast Malaysia’s annual electricity consumption, particularly in scenarios with limited datasets utilizing the Cross Industry Standard Process for Data Mining (CRISP-DM) data science methodology. Analysis revealed that this simple stacked ensemble SVR-based time-series algorithm, employing an ε -insensitive loss function with a third-degree polynomial kernel, outperformed 71 other SVR-based algorithms, including four time-series algorithms from the previous study. The algorithm’s forecasting insights from the formulated algorithm could guide policymakers in establishing more effective regulations aligned with Sustainable Development Goals (SDGs) such as affordable and clean energy (SDG7), decent work and economic growth (SDG8), industry, innovation and infrastructure (SDG9), sustainable cities and communities (SDG11), responsible consumption and production (SDG12), and climate action (SDG13), which benefit economic, environmental, human, and social.
format Article
author Chuan, Zun Liang
Shao Jie, Ong
Yim Hin, Tham
Siti Nur Syamimi, Mat Zain
Yunalis Amani, Abdul Rashid
Ainur Naseiha, Kamarudin
author_facet Chuan, Zun Liang
Shao Jie, Ong
Yim Hin, Tham
Siti Nur Syamimi, Mat Zain
Yunalis Amani, Abdul Rashid
Ainur Naseiha, Kamarudin
author_sort Chuan, Zun Liang
title Enhancing electricity consumption forecasting in limited dataset: A simple stacked ensemble approach incorporating simple linear and support vector regression for Malaysia
title_short Enhancing electricity consumption forecasting in limited dataset: A simple stacked ensemble approach incorporating simple linear and support vector regression for Malaysia
title_full Enhancing electricity consumption forecasting in limited dataset: A simple stacked ensemble approach incorporating simple linear and support vector regression for Malaysia
title_fullStr Enhancing electricity consumption forecasting in limited dataset: A simple stacked ensemble approach incorporating simple linear and support vector regression for Malaysia
title_full_unstemmed Enhancing electricity consumption forecasting in limited dataset: A simple stacked ensemble approach incorporating simple linear and support vector regression for Malaysia
title_sort enhancing electricity consumption forecasting in limited dataset: a simple stacked ensemble approach incorporating simple linear and support vector regression for malaysia
publisher Penerbit UTM
publishDate 2025
url 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
http://umpir.ump.edu.my/id/eprint/43575/
https://doi.org/10.11113/ijbes.v12.n1.1254
_version_ 1822924944716070912
score 13.236483

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