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

<p>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...

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Bibliographic Details
Main Author: Dabeeruddin Syed (16864260) (author)
Other Authors: Haitham Abu-Rub (16855500) (author), Ali Ghrayeb (16864266) (author), Shady S. Refaat (16864269) (author), Mahdi Houchati (16891560) (author), Othmane Bouhali (8252544) (author), Santiago Banales (16891563) (author)
Published: 2021
Subjects:
Engineering
Electrical engineering
Information and computing sciences
Data management and data science
Machine learning
Load modeling
Predictive models
Load forecasting
Forecasting
Energy consumption
Data models
Clustering algorithms
Deep neural networks
Distribution transformers
k-medoids clustering
Short-term load forecasting
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