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 Optimization and Prediction in Office Buildings
    sub title: A Case Study of Office Building Design in Chile
  name:
    name part: Rubio-Bellido, Carlos.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Pérez-Fargallo, Alexis.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Pulido-Arcas, Jesús.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2018
    edition: 1st ed. 2018.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: X, 78 p. 22 illus., 20 illus. in color. online resource.
  abstract: This book explains how energy demand and energy consumption in new buildings can be predicted and how these aspects and the resulting CO2 emissions can be reduced. It is based upon the authors' extensive research into the design and energy optimization of office buildings in Chile. The authors first introduce a calculation procedure that can be used for the optimization of energy parameters in office buildings, and to predict how a changing climate may affect energy demand. The prediction of energy demand, consumption and CO2 emissions is demonstrated by solving simple equations using the example of Chilean buildings, and the findings are subsequently applied to buildings around the globe. An optimization process based on Artificial Neural Networks is discussed in detail, which predicts heating and cooling energy demands, energy consumption and CO2 emissions. Taken together, these processes will show readers how to reduce energy demand, consumption and CO2 emissions associated with office buildings in the future. Readers will gain an advanced understanding of energy use in buildings and how it can be reduced.
  table of contents: Introduction -- Research Method -- Energy Demand Analysis -- Multiple Linear Regressions -- Artificial Neural Networks -- Conclusions.
  note: by Carlos Rubio-Bellido, Alexis Pérez-Fargallo, Jesús Pulido-Arcas.
  subject:
    topic: Sustainable architecture
  subject:
    topic: Energy efficiency
  subject:
    topic: Buildings-Design and construction
  subject:
    topic: Building
  subject:
    topic: Construction
  subject:
    topic: Engineering, Architectural
  subject:
    topic: Neural networks (Computer science)
  subject:
    topic: Mathematical optimization
  subject:
    topic: Sustainable Architecture/Green Buildings
  subject:
    topic: Energy Efficiency
  subject:
    topic: Building Construction and Design
  subject:
    topic: Mathematical Models of Cognitive Processes and Neural Networks
  subject:
    topic: Optimization
  classification: NA2542.36
  classification: 720.47
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: SpringerBriefs in Energy
  identifier: 9783319901466
  identifier: https://doi.org/10.1007/978-3-319-90146-6
  record info:
    record creation date: 180420
    record change date: 20191028131259.0
    record identifier: 978-3-319-90146-6