What question did this study set out to answer?

To develop and evaluate an adaptive machine learning technique, the Mountain Gazelle optimizer-driven Malleable Random Forest, for accurately forecasting variable household electricity consumption.

November 1, 2024INTI JOURNAL.Open Access

Precision Prediction of Household Electricity Consumption Through Data-Driven Model

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Authors

SJS. JebarajJain University BBBhumika BhumikaIndian Institute of Technology Jodhpur

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Implication

Machine learning study demonstrates accurate electricity forecasting in households using an optimized random forest, highlighting improved energy management strategies.

Key Points

To develop and evaluate an adaptive machine learning technique, the Mountain Gazelle optimizer-driven Malleable Random Forest, for accurately forecasting variable household electricity consumption.
Engineered the Mountain Gazelle optimizer-driven Malleable Random Forest (MG-MRF) model to adapt to varied energy usage patterns.
Gathered and preprocessed extensive electricity consumption datasets from multiple households to ensure data integrity.
The MG-MRF framework achieved a prediction accuracy of 98.2% and an F1-score of 92%, outperforming conventional forecasting models.
The model demonstrated high predictive efficacy with an evaluated precision of 94% and a recall of 90%.

Cite This Study

Jebaraj et al. (2024) studied this question.

synapsesocial.com/papers/6a86637097640e45c18333ec https://doi.org/10.61453/jods.v2024no41

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Implication

Machine learning study demonstrates accurate electricity forecasting in households using an optimized random forest, highlighting improved energy management strategies.

Precision Prediction of Household Electricity Consumption Through Data-Driven Model

Discussion

Member takes

Implication

Key Points

Cite This Study

Precision Prediction of Household Electricity Consumption Through Data-Driven Model

Discussion

Member takes

Implication

Key Points

Cite This Study

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