| Autor: |
S. Sofana Reka, Tomislav Dragicevic, Prakash Venugopal, V. Ravi, Manoj Kumar Rajagopal |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Heliyon, Vol 10, Iss 15, Pp e35683- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2405-8440 |
| DOI: |
10.1016/j.heliyon.2024.e35683 |
| Popis: |
Next generation electrical grid considered as Smart Grid has completely embarked a journey in the present electricity era. This creates a dominant need of machine learning approaches for security aspects at the larger scale for the electrical grid. The need of connectivity and complete communication in the system uses a large amount of data where the involvement of machine learning models with proper frameworks are required. This massive amount of data can be handled by various process of machine learning models by selecting appropriate set of consumers to respond in accordance with demand response modelling, learning the different attributes of the consumers, dynamic pricing schemes, various load forecasting and also data acquisition process with more cost effectiveness. In connected to this process, considering complex smart grid security and privacy based methods becomes a major aspect and there can be potential cyber threats for the consumers and also utility data. The security concerns related to machine learning model exhibits a key factor based on different machine learning algorithms used and needed for the energy application at a future perspective. This work exhibits as a detailed analysis with machine learning models which are considered as cyber physical system model with smart grid. This work also gives a clear understanding towards the potential advantages, limitations of the algorithms in a security aspect and outlines future direction in this very important area and fast-growing approach. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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