Exploring the potential of deep learning in smart grid: Addressing power load prediction and system fault diagnosis challenges

Modern societies and development depend heavily on electric power, so dependence on the electric grid is increasing. At the same time, patterns of electricity production and consumption are changing and becoming more complex day by day. Predicting future loads and diagnosing faults, especially in sm...

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Bibliographic Details
Main Authors: Falih, Mohanaed, Fadhil, Ammar, Shakir, Mohammed, Atiyah, Baqer Turki
Format: Conference Proceeding
Language:English
Subjects:
Algorithms
Artificial intelligence
Artificial neural networks
Deep learning
Electric power grids
Electrical loads
Fault diagnosis
Feedback
Machine learning
Smart grid
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Summary:Modern societies and development depend heavily on electric power, so dependence on the electric grid is increasing. At the same time, patterns of electricity production and consumption are changing and becoming more complex day by day. Predicting future loads and diagnosing faults, especially in smart grid that need good management, so Artificial Intelligence (AI) and its algorithms are used in this field. The deep learning algorithm is considered one of the best current algorithms and is widely used because of its efficiency in prediction based on previous data and feedback. A method based on weighing the variables and calculating the feedback data was used in the deep neural network to find the best structure for it. Through the results, the efficiency of the proposed method has been proven in terms of predicting loads in various conditions.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0200012