Energy Time Series Forecasting: Efficient and Accurate Forecasting of Evolving Time Series from the Energy Domain by Lars DanneckerEnergy Time Series Forecasting: Efficient and Accurate Forecasting of Evolving Time Series from the Energy Domain by Lars Dannecker

Energy Time Series Forecasting: Efficient and Accurate Forecasting of Evolving Time Series from the…

byLars Dannecker

Paperback | August 14, 2015

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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.

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.
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Title:Energy Time Series Forecasting: Efficient and Accurate Forecasting of Evolving Time Series from the…Format:PaperbackDimensions:231 pages, 21 × 14.8 × 0.02 inPublished:August 14, 2015Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3658110384

ISBN - 13:9783658110383

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Table of 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-Aware Storage.