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September 5, 2025Open Access

Development of a Machine Learning Algorithm for Predicting Electrical Consumption

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Authors

DKDimitrios KazolisDemocritus University of Thrace JFJacob FantidisDemocritus University of Thrace CFChristos Dionyshs FotakisDemocritus University of Thrace

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Implication

This analysis demonstrates the efficacy of machine learning approaches in predicting electricity consumption, indicating enhanced optimization techniques can improve model performance.

Key Points

The study shows significant improvements in predicting electricity consumption using machine learning models.
XGBoost and Scikit were key models utilized, revealing their strengths in handling complex datasets effectively.
Data preprocessing and optimization techniques were critical in achieving optimal performance for prediction tasks.
Findings highlight the potential for informed decision-making in urban planning and resource allocation.

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Kazolis et al. (2025) studied this question.

synapsesocial.com/papers/68bb420d2b87ece8dc957ef9 https://doi.org/10.3390/engproc2025104055

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Implication

This analysis demonstrates the efficacy of machine learning approaches in predicting electricity consumption, indicating enhanced optimization techniques can improve model performance.

Development of a Machine Learning Algorithm for Predicting Electrical Consumption

Discussion

Member takes

Implication

Key Points

Cite This Study

Development of a Machine Learning Algorithm for Predicting Electrical Consumption

Discussion

Member takes

Implication

Key Points

Cite This Study

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