rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Energy Time Series Forecasting
    sub title: Efficient and Accurate Forecasting of Evolving Time Series from the Energy Domain
  name:
    name part: Dannecker, Lars.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2015
    edition: 1st ed. 2015.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIX, 231 p. 92 illus., 19 illus. in color. online resource.
  abstract: 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.
  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.
  note: by Lars Dannecker.
  subject:
    topic: Computer science
  subject:
    topic: Data structures (Computer science)
  subject:
    topic: Computers
  subject:
    topic: Computer Science
  subject:
    topic: Data Structures, Cryptology and Information Theory
  subject:
    topic: Theory of Computation
  subject:
    topic: Information Systems and Communication Service
  classification: QA76.9.D35
  classification: 005.74
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783658110390
  identifier: http://dx.doi.org/10.1007/978-3-658-11039-0
  record info:
    record creation date: 150806
    record change date: 20151204190711.0
    record identifier: 978-3-658-11039-0