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Jurnal SINTA 2

Energy Consumption Prediction Using Data Reduction and Ensemble Learning Techniques

Journal of ICT Research and Applications (JICTRA) · Vol. 16 No. 3 (2022) · pp. 197–209

DOI
10.5614/itbj.ict.res.appl.2022.16.3.1
ISSN
2338-5499, 2337-5787
Published
2022-12-27
Publisher
DRI - ITB
Language
eng

Abstract

Building energy problems have various kinds of aspects, one of which is the difficulty of measuring energy efficiency. With current data development, energy efficiency measurements can be made by developing predictive models to estimate future building needs. However, with the massive amount of data, several problems arise regarding data quality and the lack of scalability in terms of computation memory and time in modeling. In this study, we used data reduction and ensemble learning techniques to overcome these problems. We used numerosity reduction, dimension reduction, and a LightGBM model based on boosting added with a bagging technique, which we compared with incremental learning. Our experimental results showed that the numerosity reduction and dimension reduction techniques could speed up the training process and model prediction without reducing the accuracy. Testing the ensemble learning model also revealed that bagging had the best performance in terms of RMSE and speed, with an RMSE of 262.304 and 1.67 times faster than the model with incremental learning.

Keywords

Collaboration network of the authors

7 authors · 8 co-authorship links · 2 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Bandung Institute of Technology
  • Universitas Pendidikan Ganesha
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Naufal Dzaki HafizhanAffiliation unknown1
Gede IndrawanUniversitas Pendidikan Ganesha1
B. SitohangBandung Institute of Technology1
Suhono Harso SupangkatBandung Institute of Technology1
Susetyo Bagas BhaskoroBandung Institute of Technology1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Energy Consumption Prediction Using Data Reduction and Ensemble Learning Techniques</dc:title>
	<dc:creator>Ahmada, Marsa Thoriq</dc:creator>
	<dc:creator>Akbar, Saiful</dc:creator>
	<dc:subject xml:lang="en-US">bagging</dc:subject>
	<dc:subject xml:lang="en-US">boosting</dc:subject>
	<dc:subject xml:lang="en-US">data reduction</dc:subject>
	<dc:subject xml:lang="en-US">dimensionality reduction</dc:subject>
	<dc:subject xml:lang="en-US">energy efficiency</dc:subject>
	<dc:subject xml:lang="en-US">ensemble learning</dc:subject>
	<dc:subject xml:lang="en-US">LightGBM</dc:subject>
	<dc:subject xml:lang="en-US">numerosity reduction</dc:subject>
	<dc:description xml:lang="en-US">Building energy problems have various kinds of aspects, one of which is the difficulty of measuring energy efficiency. With current data development, energy efficiency measurements can be made by developing predictive models to estimate future building needs. However, with the massive amount of data, several problems arise regarding data quality and the lack of scalability in terms of computation memory and time in modeling. In this study, we used data reduction and ensemble learning techniques to overcome these problems. We used numerosity reduction, dimension reduction, and a LightGBM model based on boosting added with a bagging technique, which we compared with incremental learning. Our experimental results showed that the numerosity reduction and dimension reduction techniques could speed up the training process and model prediction without reducing the accuracy. Testing the ensemble learning model also revealed that bagging had the best performance in terms of RMSE and speed, with an RMSE of 262.304 and 1.67 times faster than the model with incremental learning.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2022-12-27</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
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	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/17580</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2022.16.3.1</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 16 No. 3 (2022); 197-209</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2022.16.3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/17580/6026</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Journal of ICT Research and Applications</dc:rights>
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