Data Mining in Smart Grids
Effective smart grid operation requires rapid decisions in a data-rich, but information-limited, environment. In this context, grid sensor data-streaming cannot provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbanc...
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| Formato: | Online |
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| Idioma: | inglés |
| Publicado: |
MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Subjects: | |
| Acceso en liña: | ONIX_20210501_9783039433261_955 |
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| _version_ | 1869531356320497664 |
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| collection | Directory of Open Access Books |
| description | Effective smart grid operation requires rapid decisions in a data-rich, but information-limited, environment. In this context, grid sensor data-streaming cannot provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbances. Even if there are fast models that can convert the data into information, the smart grid operator must deal with the challenge of not having a full understanding of the context of the information, and, therefore, the information content cannot be used with any high degree of confidence. To address this issue, data mining has been recognized as the most promising enabling technology for improving decision-making processes, providing the right information at the right moment to the right decision-maker. This Special Issue is focused on emerging methodologies for data mining in smart grids. In this area, it addresses many relevant topics, ranging from methods for uncertainty management, to advanced dispatching. This Special Issue not only focuses on methodological breakthroughs and roadmaps in implementing the methodology, but also presents the much-needed sharing of the best practices. Topics include, but are not limited to, the following: Fuzziness in smart grids computing Emerging techniques for renewable energy forecasting Robust and proactive solution of optimal smart grids operation Fuzzy-based smart grids monitoring and control frameworks Granular computing for uncertainty management in smart grids Self-organizing and decentralized paradigms for information processing |
| format | Online |
| id | doab-20.500.12854ir-69209 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-692092024-03-30T12:51:19Z Data Mining in Smart Grids Vaccaro, Alfredo voltage regulation smart grid decentralized control architecture multi-agent systems t-SNE algorithm numerical weather prediction data preprocessing data visualization wind power generation partial discharge gas insulated switchgear case-based reasoning data matching variational autoencoder DSHW TBATS NN-AR time-series clustering decentral smart grid control (DSGC) interpretable and accurate DSGC-stability prediction data mining computational intelligence fuzzy rule-based classifiers multi-objective evolutionary optimization power systems resilience dynamic Bayesian network Markov model probabilistic modeling resilience models thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries Effective smart grid operation requires rapid decisions in a data-rich, but information-limited, environment. In this context, grid sensor data-streaming cannot provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbances. Even if there are fast models that can convert the data into information, the smart grid operator must deal with the challenge of not having a full understanding of the context of the information, and, therefore, the information content cannot be used with any high degree of confidence. To address this issue, data mining has been recognized as the most promising enabling technology for improving decision-making processes, providing the right information at the right moment to the right decision-maker. This Special Issue is focused on emerging methodologies for data mining in smart grids. In this area, it addresses many relevant topics, ranging from methods for uncertainty management, to advanced dispatching. This Special Issue not only focuses on methodological breakthroughs and roadmaps in implementing the methodology, but also presents the much-needed sharing of the best practices. Topics include, but are not limited to, the following: Fuzziness in smart grids computing Emerging techniques for renewable energy forecasting Robust and proactive solution of optimal smart grids operation Fuzzy-based smart grids monitoring and control frameworks Granular computing for uncertainty management in smart grids Self-organizing and decentralized paradigms for information processing 2021-05-01T15:43:48Z 2021-05-01T15:43:48Z 2020 book ONIX_20210501_9783039433261_955 9783039433261 9783039433278 https://directory.doabooks.org/handle/20.500.12854/69209 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/2981 https://mdpi.com/books/pdfview/book/2981 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03943-327-8 10.3390/books978-3-03943-327-8 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783039433261 9783039433278 116 Basel, Switzerland open access |
| spellingShingle | voltage regulation smart grid decentralized control architecture multi-agent systems t-SNE algorithm numerical weather prediction data preprocessing data visualization wind power generation partial discharge gas insulated switchgear case-based reasoning data matching variational autoencoder DSHW TBATS NN-AR time-series clustering decentral smart grid control (DSGC) interpretable and accurate DSGC-stability prediction data mining computational intelligence fuzzy rule-based classifiers multi-objective evolutionary optimization power systems resilience dynamic Bayesian network Markov model probabilistic modeling resilience models thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries Data Mining in Smart Grids |
| title | Data Mining in Smart Grids |
| title_full | Data Mining in Smart Grids |
| title_fullStr | Data Mining in Smart Grids |
| title_full_unstemmed | Data Mining in Smart Grids |
| title_short | Data Mining in Smart Grids |
| title_sort | data mining in smart grids |
| topic | voltage regulation smart grid decentralized control architecture multi-agent systems t-SNE algorithm numerical weather prediction data preprocessing data visualization wind power generation partial discharge gas insulated switchgear case-based reasoning data matching variational autoencoder DSHW TBATS NN-AR time-series clustering decentral smart grid control (DSGC) interpretable and accurate DSGC-stability prediction data mining computational intelligence fuzzy rule-based classifiers multi-objective evolutionary optimization power systems resilience dynamic Bayesian network Markov model probabilistic modeling resilience models thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| topic_facet | voltage regulation smart grid decentralized control architecture multi-agent systems t-SNE algorithm numerical weather prediction data preprocessing data visualization wind power generation partial discharge gas insulated switchgear case-based reasoning data matching variational autoencoder DSHW TBATS NN-AR time-series clustering decentral smart grid control (DSGC) interpretable and accurate DSGC-stability prediction data mining computational intelligence fuzzy rule-based classifiers multi-objective evolutionary optimization power systems resilience dynamic Bayesian network Markov model probabilistic modeling resilience models thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| url | ONIX_20210501_9783039433261_955 |