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: Industrial Demand Response
    sub title: Methods, Best Practices, Case Studies, and Applications
  name:
    name part: Alhelou, Hassan Haes.
    role:
      role term: creator
  name:
    name part: Moreno-Muñoz, Antonio.
  name:
    name part: Siano, Pierluigi.
  type of resource: text
  genre: elektronické knihy
  genre: electronic books
  origin info:
    place:
      place term: xx
    date issued: 2022
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (426 pages).
  abstract: Demand response (DR) describes controlled changes in the power consumption whose role is to better match the power demand with the supply. This reference, written by an international team of experts from academia and industry, covers the principles, implementation and applications of DR.
  note: Description based upon print version of record.
  note: Chapter 1: A comprehensive review on industrial demand response strategies and applicationsChapter 2: Demand response cybersecurity for power systems with high renewable power shareChapter 3: Recurrent neural networks for electrical load forecasting to use in demand responseChapter 4: Optimal demand response strategy of an industrial customerChapter 5: Price-based demand response for thermostatically controlled loadsChapter 6: Electric vehicle massive resources mining and demand response applicationChapter 7: Demand response measurement and verification approaches: analyses and guidelinesChapter 8: Transactive energy industry demand response management marketChapter 9: Industrial demand response opportunities with residential appliances in smart gridsChapter 10: Modelling and optimal scheduling of flexibility in energy-intensive industryChapter 11: Industrial demand response: coordination with asset managementChapter 12: A machine learning-based approach for industrial demand responseChapter 13: Feasibility assessment of industrial demand responseChapter 14: Measurement and verification of demand response: the customer load baselineChapter 15: Modeling and optimizing the value of flexible industrial processes in the UK electricity marketChapter 16: Case study of Aran Islands: optimal demand response control of heat pumps and appliancesChapter 17: Use case of artificial intelligence, and neural networks in energy consumption markets, and industrial demand response.
  note: Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty
  subject:
    topic: Electric power consumption
    topic: Forecasting
  related item:
    title info:
      title: Industrial Demand Response
    name:
      name part: Alhelou, Hassan Haes
    origin info:
      publisher: Stevenage : Institution of Engineering & Technology,c2022
  related item:
    title info:
      title: Energy Engineering
  identifier: 1523153482
  identifier: 9781523153480
  identifier: 1523146745
  identifier: 9781523146741
  identifier: 1839535628
  identifier: 9781839535628
  identifier: https://proxy.k.utb.cz/login
  access condition: Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty
  record info:
    record content source: SFB
    record creation date: 220619
    record change date: 20240717213016.0
    record identifier: kn-on1381725329
    language of cataloging:
      language term: eng