Staff View: Precision Prediction of Household Electricity Consumption Through Data- Driven Model

Precision Prediction of Household Electricity Consumption Through Data- Driven Model

An effective strategy for managing energy and sustainability is the accurate forecasting of household electricity consumption. A new challenge arises in consumption patterns for traditional models, which face difficulties in variability and data variety. This study aims to bridge the gap by propo...

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Main Authors:	Sadaf, Hashmi, Solomon, Jebaraj, Utpalkumar B, Patel, Bhumika, .
Format:	Article
Language:	English English
Published:	INTI International University 2024
Subjects:	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.intimal.edu.my/2022/1/jods2024_41.pdf http://eprints.intimal.edu.my/2022/2/563 http://eprints.intimal.edu.my/2022/ http://ipublishing.intimal.edu.my/jods.html
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spelling	my-inti-eprints.20222024-11-08T03:46:30Z http://eprints.intimal.edu.my/2022/ Precision Prediction of Household Electricity Consumption Through Data- Driven Model Sadaf, Hashmi Solomon, Jebaraj Utpalkumar B, Patel Bhumika, . QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TK Electrical engineering. Electronics Nuclear engineering An effective strategy for managing energy and sustainability is the accurate forecasting of household electricity consumption. A new challenge arises in consumption patterns for traditional models, which face difficulties in variability and data variety. This study aims to bridge the gap by proposing a novel technique called the Mountain Gazelle optimizer-driven Malleable Random Forest technique (MG-MRF), for improving electricity consumption prediction. This has enabled MG-MRF to model different consumption patterns as well as manage variability in the data. The study collected extensive datasets from different households, and those datasets had to undergo preprocessing to ensure integrity. Evaluation results of the approach further underscore the potential of MG-MRF to give accurate and dependable predictions, consequently allowing informed decision-making for the consumption of energy. The proposed method outperformed the traditional models with a prediction accuracy of 98.2%, precision of 94%, recall of 90%, and an f1-score of 92%. This study emphasizes the importance of adaptive modeling techniques in understanding and predicting household electricity usage, enabling the development of more effective energy management strategies. The experimental results advocate and contribute to sustainable energy practices by raising consumer awareness regarding their electrical consumption. INTI International University 2024-11 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2022/1/jods2024_41.pdf text en cc_by_4 http://eprints.intimal.edu.my/2022/2/563 Sadaf, Hashmi and Solomon, Jebaraj and Utpalkumar B, Patel and Bhumika, . (2024) Precision Prediction of Household Electricity Consumption Through Data- Driven Model. Journal of Data Science, 2024 (41). pp. 1-10. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html
institution	INTI International University
building	INTI Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	INTI International University
content_source	INTI Institutional Repository
url_provider	http://eprints.intimal.edu.my
language	English English
topic	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Sadaf, Hashmi Solomon, Jebaraj Utpalkumar B, Patel Bhumika, . Precision Prediction of Household Electricity Consumption Through Data- Driven Model
description	An effective strategy for managing energy and sustainability is the accurate forecasting of household electricity consumption. A new challenge arises in consumption patterns for traditional models, which face difficulties in variability and data variety. This study aims to bridge the gap by proposing a novel technique called the Mountain Gazelle optimizer-driven Malleable Random Forest technique (MG-MRF), for improving electricity consumption prediction. This has enabled MG-MRF to model different consumption patterns as well as manage variability in the data. The study collected extensive datasets from different households, and those datasets had to undergo preprocessing to ensure integrity. Evaluation results of the approach further underscore the potential of MG-MRF to give accurate and dependable predictions, consequently allowing informed decision-making for the consumption of energy. The proposed method outperformed the traditional models with a prediction accuracy of 98.2%, precision of 94%, recall of 90%, and an f1-score of 92%. This study emphasizes the importance of adaptive modeling techniques in understanding and predicting household electricity usage, enabling the development of more effective energy management strategies. The experimental results advocate and contribute to sustainable energy practices by raising consumer awareness regarding their electrical consumption.
format	Article
author	Sadaf, Hashmi Solomon, Jebaraj Utpalkumar B, Patel Bhumika, .
author_facet	Sadaf, Hashmi Solomon, Jebaraj Utpalkumar B, Patel Bhumika, .
author_sort	Sadaf, Hashmi
title	Precision Prediction of Household Electricity Consumption Through Data- Driven Model
title_short	Precision Prediction of Household Electricity Consumption Through Data- Driven Model
title_full	Precision Prediction of Household Electricity Consumption Through Data- Driven Model
title_fullStr	Precision Prediction of Household Electricity Consumption Through Data- Driven Model
title_full_unstemmed	Precision Prediction of Household Electricity Consumption Through Data- Driven Model
title_sort	precision prediction of household electricity consumption through data- driven model
publisher	INTI International University
publishDate	2024
url	http://eprints.intimal.edu.my/2022/1/jods2024_41.pdf http://eprints.intimal.edu.my/2022/2/563 http://eprints.intimal.edu.my/2022/ http://ipublishing.intimal.edu.my/jods.html
_version_	1817849521577656320
score	13.223943

Precision Prediction of Household Electricity Consumption Through Data- Driven Model

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