May 7, 2024EnergiesOpen Access

Energy Load Forecasting Techniques in Smart Grids: A Cross-Country Comparative Analysis

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RHRachida HachacheSidi Mohamed Ben Abdellah University MLMourad LabrahmiInstitut National des Postes et Télécommunications AGAntónio GriloInstituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento

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Implication

Comparative analysis demonstrates superior accuracy of hybrid deep learning for short-term load forecasts across three national grids, indicating data augmentation enhances grid management.

Key Points

Hybrid deep learning models achieve superior predictive accuracy for short-term load forecasting compared to conventional machine learning and classical statistical baselines.
Comparative evaluation across three real-world consumption datasets indicates that incorporating weather variables significantly boosts forecasting performance in smart grids.
Data augmentation based on generative adversarial networks substantially enhances forecasting precision, supporting optimized demand-side management across smart grids infrastructure.

Cite This Study

Hachache et al. (2024) studied this question.

synapsesocial.com/papers/68e6b291b6db643587633ce8 https://doi.org/10.3390/en17102251

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Implication

Comparative analysis demonstrates superior accuracy of hybrid deep learning for short-term load forecasts across three national grids, indicating data augmentation enhances grid management.

Energy Load Forecasting Techniques in Smart Grids: A Cross-Country Comparative Analysis

Discussion

Member takes

Implication

Key Points

Cite This Study

Energy Load Forecasting Techniques in Smart Grids: A Cross-Country Comparative Analysis

Discussion

Member takes

Implication

Key Points

Cite This Study

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