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: | , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2024
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| Subjects: | |
| 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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| Summary: | 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. |
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