Deep Learning-Based Short-Term Load Forecasting Approach in Smart Grid With Clustering and Consumption Pattern Recognition

Deep Learning-Based Short-Term Load Forecasting Approach in Smart Grid With Clustering and Consumption Pattern Recognition

Different aggregation levels of the electric grid’s big data can be helpful to develop highly accurate deep learning models for Short-term Load Forecasting (STLF) in electrical networks. Whilst different models are proposed for STLF, they are based on small historical datasets and are not...

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Bibliographic Details
Main Authors: Dabeeruddin Syed, Haitham Abu-Rub, Ali Ghrayeb, Shady S. Refaat, Mahdi Houchati, Othmane Bouhali, Santiago Banales
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Deep neural networks
distribution transformers
k-medoids clustering
machine learning
short-term load forecasting
Online Access:https://ieeexplore.ieee.org/document/9399106/

Internet

https://ieeexplore.ieee.org/document/9399106/

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