Predicting the Future : Big Data and Machine Learning

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ISBN 10: 303936619X / ISBN 13: 9783039366194

Published by MDPI AG, 2020

About this Item

Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | 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 # 36767916/2

Bibliographic Details

• Title: Predicting the Future : Big Data and Machine Learning
• Publisher: MDPI AG
• Publication Date: 2020
• Language: English
• ISBN 10: 303936619X
• ISBN 13: 9783039366194
• Binding: Hardcover
• Condition: Sehr gut
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.