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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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Dabeeruddin Syed (16864260) (author)
مؤلفون آخرون:	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)
منشور في:	2021
الموضوعات:	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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Deep Learning-Based Short-Term Load Forecasting Approach in Smart Grid With Clustering and Consumption Pattern Recognition

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