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Interpretable AI Hybrid Model for Electricity Demand Forecasting:Combining TFT and XGBoost in Smart Grid Data

Sobhan Manjili Saeid Jafarzadeh Ghoushchi Mohammad Reza Maghami Mazlan Mohamed

Computer Modeling in Engineering & Sciences2026，Vol.147Issue(4)：P.682-703,22.

Computer Modeling in Engineering & Sciences2026，Vol.147Issue(4)：P.682-703,22.DOI:10.32604/cmes.2026.076217

Interpretable AI Hybrid Model for Electricity Demand Forecasting:Combining TFT and XGBoost in Smart Grid Data

Sobhan Manjili ¹Saeid Jafarzadeh Ghoushchi ¹Mohammad Reza Maghami ²Mazlan Mohamed³

作者信息

1. Faculty of Industrial Engineering,Urmia University of Technology,Urmia,Iran
2. Strategic Research Institute(SRI),Asia Pacific University of Technology&Innovation(APU),Technology Park Malaysia,Bukit Jalil,Kuala Lumpur,Malaysia
3. Faculty of Artificial Intelligence and Cyber Security(FAIX),University Technical Malaysia Melaka,Melaka,Malaysia
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摘要

关键词

Electricity load forecasting/hybrid AI models/Temporal Fusion Transformer(TFT)/XGBoost/interpretable machine learning/smart grid analytics/urban energy management/attention mechanisms

分类

信息技术与安全科学

引用本文复制引用

Sobhan Manjili,Saeid Jafarzadeh Ghoushchi,Mohammad Reza Maghami,Mazlan Mohamed..Interpretable AI Hybrid Model for Electricity Demand Forecasting:Combining TFT and XGBoost in Smart Grid Data[J].Computer Modeling in Engineering & Sciences,2026,147(4):P.682-703,22.

Computer Modeling in Engineering & Sciences

ISSN：1526-1492

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