Predicting the Future : Big Data and Machine Learning
Language: English
Published by MDPI AG, 2020
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 47.37
Quantity: 2 available
Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - Due to the increased capabilities of microprocessors and the advent of graphics processing units (GPUs) in recent decades, the use of machine learning methodologies has become popular in many fields of science and technology. This fact, together with the availability of large amounts of information, has meant that machine learning and Big Data have an important presence in the field of Energy. This Special Issue entitled 'Predicting the Future-Big Data and Machine Learning' is focused on applications of machine learning methodologies in the field of energy. Topics include but are not limited to the following: big data architectures of power supply systems, energy-saving and efficiency models, environmental effects of energy consumption, prediction of occupational health and safety outcomes in the energy industry, price forecast prediction of raw materials, and energy management of smart buildings.
Seller Inventory # 9783039366194
- Title
- Predicting the Future : Big Data and Machine Learning
- Publisher
- MDPI AG
- Publication year
- 2020
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 303936619X
- ISBN 13
- 9783039366194
- Item weight
- 570 grams
- Dimensions
- 250x175x14 mm
Due to the increased capabilities of microprocessors and the advent of graphics processing units (GPUs) in recent decades, the use of machine learning methodologies has become popular in many fields of science and technology. This fact, together with the availability of large amounts of information, has meant that machine learning and Big Data have an important presence in the field of Energy. This Special Issue entitled "Predicting the Future-Big Data and Machine Learning" is focused on applications of machine learning methodologies in the field of energy. Topics include but are not limited to the following: big data architectures of power supply systems, energy-saving and efficiency models, environmental effects of energy consumption, prediction of occupational health and safety outcomes in the energy industry, price forecast prediction of raw materials, and energy management of smart buildings.
"Synopsis" may belong to another edition of this title.
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