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Energy time series forecasting[electronic resource] :efficient and accurate forecasting of evolving time series from the energy domain /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	519.55
書名/作者:	Energy time series forecasting : efficient and accurate forecasting of evolving time series from the energy domain // by Lars Dannecker.
作者:	Dannecker, Lars.
出版者:	Wiesbaden : : Springer Fachmedien Wiesbaden :, 2015.
面頁冊數:	xix, 231 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Time-series analysis.
標題:	Prediction theory.
標題:	Computer Science.
標題:	Data Structures, Cryptology and Information Theory.
標題:	Theory of Computation.
標題:	Information Systems and Communication Service.
ISBN:	9783658110390
ISBN:	9783658110383
內容註:	The European Electricity Market: A Market Study -- The Current State of Energy Data Management and Forecasting -- The Online Forecasting Process: Efficiently Providing Accurate Predictions -- Optimizations on the Logical Layer: Context-Aware Forecasting -- Optimizations on the Physical Layer: A Forecast-Model-Aware Storage.
摘要、提要註:	Lars Dannecker developed a novel online forecasting process that significantly improves how forecasts are calculated. It increases forecasting efficiency and accuracy, as well as allowing the process to adapt to different situations and applications. Improving the forecasting efficiency is a key pre-requisite for ensuring stable electricity grids in the face of an increasing amount of renewable energy sources. It is also important to facilitate the move from static day ahead electricity trading towards more dynamic real-time marketplaces. The online forecasting process is realized by a number of approaches on the logical as well as on the physical layer that we introduce in the course of this book. Nominated for the Georg-Helm-Preis 2015 awarded by the Technische Universitat Dresden. Contents The European Electricity Market: A Market Study The Current State of Energy Data Management and Forecasting The Online Forecasting Process: Efficiently Providing Accurate Predictions Optimizations on the Logical Layer: Context-Aware Forecasting Optimizations on the Physical Layer: A Forecast-Model-AwareStorage Target Groups Lecturers and Students of Computer Science, especially in the Field of Database Technology, Data Analytics, Time Series Analysis, and Data Mining Data Analysts, Energy Time Series Modeling, Transmission System Operators, Software Developers The Author Lars Dannecker holds a diploma in media computer science from the Technische Universitat Dresden and is pursuing a doctorate as a member of the Database Technology Group led by Prof. Dr.-Ing. Wolfgang Lehner.
電子資源:	http://dx.doi.org/10.1007/978-3-658-11039-0

Energy time series forecasting[electronic resource] :efficient and accurate forecasting of evolving time series from the energy domain /
Dannecker, Lars.

Energy time series forecastingefficient and accurate forecasting of evolving time series from the energy domain /[electronic resource] :by Lars Dannecker. - Wiesbaden :Springer Fachmedien Wiesbaden :2015. - xix, 231 p. :ill., digital ;24 cm.

The European Electricity Market: A Market Study -- The Current State of Energy Data Management and Forecasting -- The Online Forecasting Process: Efficiently Providing Accurate Predictions -- Optimizations on the Logical Layer: Context-Aware Forecasting -- Optimizations on the Physical Layer: A Forecast-Model-Aware Storage.

Lars Dannecker developed a novel online forecasting process that significantly improves how forecasts are calculated. It increases forecasting efficiency and accuracy, as well as allowing the process to adapt to different situations and applications. Improving the forecasting efficiency is a key pre-requisite for ensuring stable electricity grids in the face of an increasing amount of renewable energy sources. It is also important to facilitate the move from static day ahead electricity trading towards more dynamic real-time marketplaces. The online forecasting process is realized by a number of approaches on the logical as well as on the physical layer that we introduce in the course of this book. Nominated for the Georg-Helm-Preis 2015 awarded by the Technische Universitat Dresden. Contents The European Electricity Market: A Market Study The Current State of Energy Data Management and Forecasting The Online Forecasting Process: Efficiently Providing Accurate Predictions Optimizations on the Logical Layer: Context-Aware Forecasting Optimizations on the Physical Layer: A Forecast-Model-AwareStorage Target Groups Lecturers and Students of Computer Science, especially in the Field of Database Technology, Data Analytics, Time Series Analysis, and Data Mining Data Analysts, Energy Time Series Modeling, Transmission System Operators, Software Developers The Author Lars Dannecker holds a diploma in media computer science from the Technische Universitat Dresden and is pursuing a doctorate as a member of the Database Technology Group led by Prof. Dr.-Ing. Wolfgang Lehner.

ISBN: 9783658110390

Standard No.: 10.1007/978-3-658-11039-0doiSubjects--Topical Terms:

224119
Time-series analysis.

LC Class. No.: QA280

Dewey Class. No.: 519.55

Energy time series forecasting[electronic resource] :efficient and accurate forecasting of evolving time series from the energy domain /
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