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Energy Time Series Forecasting = Efficient and Accurate Forecasting of Evolving Time Series from the Energy Domain /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Energy Time Series Forecasting/ by Lars Dannecker.
Reminder of title:	Efficient and Accurate Forecasting of Evolving Time Series from the Energy Domain /
Author:	Dannecker, Lars.
Description:	XIX, 231 p. 92 illus., 19 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Data structures (Computer science). -
Online resource:	https://doi.org/10.1007/978-3-658-11039-0
ISBN:	9783658110390

Energy Time Series Forecasting = 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. - 1st ed. 2015. - XIX, 231 p. 92 illus., 19 illus. in color.online resource.

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 Universität 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 Universität 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:

680370
Data structures (Computer science).

LC Class. No.: QA76.9.D35

Dewey Class. No.: 005.73

Energy Time Series Forecasting = Efficient and Accurate Forecasting of Evolving Time Series from the Energy Domain /
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