Optimizing Smart Grid Load Forecasting via a Hybrid Long Short-Term Memory-XGBoost Framework: Enhancing Accuracy, Robustness, and Energy Management

Optimizing Smart Grid Load Forecasting via a Hybrid Long Short-Term Memory-XGBoost Framework: Enhancing Accuracy, Robustness, and Energy Management

As renewable energy sources and distributed generation become more integrated into modern power systems, accurate short-term electricity load forecasting is increasingly critical for effective smart grid management. Most statistical techniques used in the analysis of time series models, conventional...

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Bibliographic Details
Main Authors:	Falah Dakheel, Mesut Çevik
Format:	Article
Language:	English
Published:	MDPI AG 2025-05-01
Series:	Energies
Subjects:	electricity load forecasting hybrid deep learning models LSTM Networks XGBoost smart grids ensemble learning
Online Access:	https://www.mdpi.com/1996-1073/18/11/2842
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