Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast
Language: English
Published by MDPI AG, 2021
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 41.86
Quantity: 2 available
Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - The use of data collectors in energy systems is growing more and more. For example, smart sensors are now widely used in energy production and energy consumption systems.This implies that huge amounts of data are generated and need to be analyzed in order to extract useful insights from them. Such big data give rise to a number of opportunities and challenges for informed decision making.In recent years, researchers have been working very actively in order to come up with effective and powerful techniques in order to deal with the huge amount of data available. Such approaches can be used in the context of energy production and consumption considering the amount of data produced by all samples and measurements, as well as including many additional features. With them, automated machine learning methods for extracting relevant patterns, high-performance computing, or data visualization are being successfully applied to energy demand forecasting.In light of the above, this Special Issue collects the latest research on relevant topics, in particular in energy demand forecasts, and the use of advanced optimization methods and big data techniques. Here, by energy, we mean any kind of energy, e.g., electrical, solar, microwave, or wind.…
Seller Inventory # 9783036508627
- Title
- Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast
- Publisher
- MDPI AG
- Publication year
- 2021
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 3036508627
- ISBN 13
- 9783036508627
- Item weight
- 450 grams
- Dimensions
- 250x175x11 mm
The use of data collectors in energy systems is growing more and more. For example, smart sensors are now widely used in energy production and energy consumption systems.
This implies that huge amounts of data are generated and need to be analyzed in order to extract useful insights from them. Such big data give rise to a number of opportunities and challenges for informed decision making.
In recent years, researchers have been working very actively in order to come up with effective and powerful techniques in order to deal with the huge amount of data available. Such approaches can be used in the context of energy production and consumption considering the amount of data produced by all samples and measurements, as well as including many additional features. With them, automated machine learning methods for extracting relevant patterns, high-performance computing, or data visualization are being successfully applied to energy demand forecasting.
In light of the above, this Special Issue collects the latest research on relevant topics, in particular in energy demand forecasts, and the use of advanced optimization methods and big data techniques. Here, by energy, we mean any kind of energy, e.g., electrical, solar, microwave, or wind
"Synopsis" may belong to another edition of this title.
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