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1. |
Record Nr. |
UNINA990004602870403321 |
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Autore |
Hupe, Conradus |
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Titolo |
<De genere dicendi C. Valerio Catulli Veronensis : Pars I / scripsit Conradus Hupe> |
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Descrizione fisica |
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Locazione |
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Collocazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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2. |
Record Nr. |
UNISA996214876603316 |
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Titolo |
Fish Pathology [[ギョビョウケンキュウ]] |
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Pubbl/distr/stampa |
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Tōkyō, : Nihon Gyobyō Gakkai |
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ISSN |
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Descrizione fisica |
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Soggetti |
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Fishes - Diseases |
Veterinary pathology |
Poissons - Maladies |
Pathologie vétérinaire |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Periodico |
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Note generali |
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3. |
Record Nr. |
UNINA9910731475303321 |
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Autore |
Xie Le |
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Titolo |
Data Science and Applications for Modern Power Systems / / by Le Xie, Yang Weng, Ram Rajagopal |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
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ISBN |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (446 pages) |
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Collana |
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Power Electronics and Power Systems, , 2196-3193 |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Electric power production |
Big data |
Business information services |
Electrical Power Engineering |
Big Data |
IT in Business |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Big Data Challenges in Power Systems -- Challenges and Opportunities in Utility Data -- Wholesale Markets Data Deluge -- Distribution System Data Operation -- Synchrophasor Data Analytics -- Smart Meter and its Implications -- Deep Learning in Power Markets -- Data-driven Planning in Electric Energy Systems -- Common Information Model for Unifying Data Sets -- Inference and Business for Aggregators Non-intrusive Load Monitoring -- Utility Business Model in the Era of Big Data -- Data Security Services for Utilities. |
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Sommario/riassunto |
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This book offers a comprehensive collection of research articles that utilize data—in particular large data sets—in modern power systems operation and planning. As the power industry moves towards actively utilizing distributed resources with advanced technologies and incentives, it is becoming increasingly important to benefit from the available heterogeneous data sets for improved decision-making. The authors present a first-of-its-kind comprehensive review of big data opportunities and challenges in the smart grid industry. This book |
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provides succinct and useful theory, practical algorithms, and case studies to improve power grid operations and planning utilizing big data, making it a useful graduate-level reference for students, faculty, and practitioners on the future grid. Presents a comprehensive review of data sciences for the power industry; Contains state-of-the-art research articles; Provides practical algorithms and case studies. |
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