Atnaujinkite slapukų nuostatas

El. knyga: Energy Optimization and Prediction in Office Buildings: A Case Study of Office Building Design in Chile

  • Formatas: EPUB+DRM
  • Serija: SpringerBriefs in Energy
  • Išleidimo metai: 20-Apr-2018
  • Leidėjas: Springer International Publishing AG
  • Kalba: eng
  • ISBN-13: 9783319901466
  • Formatas: EPUB+DRM
  • Serija: SpringerBriefs in Energy
  • Išleidimo metai: 20-Apr-2018
  • Leidėjas: Springer International Publishing AG
  • Kalba: eng
  • ISBN-13: 9783319901466

DRM apribojimai

  • Kopijuoti:

    neleidžiama

  • Spausdinti:

    neleidžiama

  • El. knygos naudojimas:

    Skaitmeninių teisių valdymas (DRM)
    Leidykla pateikė šią knygą šifruota forma, o tai reiškia, kad norint ją atrakinti ir perskaityti reikia įdiegti nemokamą programinę įrangą. Norint skaityti šią el. knygą, turite susikurti Adobe ID . Daugiau informacijos  čia. El. knygą galima atsisiųsti į 6 įrenginius (vienas vartotojas su tuo pačiu Adobe ID).

    Reikalinga programinė įranga
    Norint skaityti šią el. knygą mobiliajame įrenginyje (telefone ar planšetiniame kompiuteryje), turite įdiegti šią nemokamą programėlę: PocketBook Reader (iOS / Android)

    Norint skaityti šią el. knygą asmeniniame arba „Mac“ kompiuteryje, Jums reikalinga  Adobe Digital Editions “ (tai nemokama programa, specialiai sukurta el. knygoms. Tai nėra tas pats, kas „Adobe Reader“, kurią tikriausiai jau turite savo kompiuteryje.)

    Negalite skaityti šios el. knygos naudodami „Amazon Kindle“.

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.
Introduction.- Research Method.- Energy Demand Analysis.- Multiple Linear Regressions.- Artificial Neural Networks.- Conclusions.
Carlos Rubio-Bellido is an assistant professor at the Department of Building Construction II at the University of Sevilla. His research focuses largely on energy efficiency, climate adaption and climate change in the building sector. 





Alexis Pérez-Fargallo is an assistant professor at the Department of Building Science at the University of Bķo-Bķo and is a specialist for energy demand, energy consumption and CO2 emissions in buildings that are in use. 





Jesśs A. Pulido-Arcas is an assistant professor at the Department of Building Science at the University of Bķo-Bķo. He has published extensive research works concerning radiative transfer, statistical and environmental software in architecture.