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Titlebook: Energy Optimization and Prediction in Office Buildings; A Case Study of Offi Carlos Rubio-Bellido,Alexis Pérez-Fargallo,Jesús P Book 2018 T

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發(fā)表于 2025-3-21 20:06:31 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Energy Optimization and Prediction in Office Buildings
副標(biāo)題A Case Study of Offi
編輯Carlos Rubio-Bellido,Alexis Pérez-Fargallo,Jesús P
視頻videohttp://file.papertrans.cn/311/310355/310355.mp4
概述Provides essential tools for reducing energy consumption and CO2 emissions in modern office buildings.Highlights computer-aided optimization tools.Demonstrates building energy optimization techniques
叢書(shū)名稱SpringerBriefs in Energy
圖書(shū)封面Titlebook: Energy Optimization and Prediction in Office Buildings; A Case Study of Offi Carlos Rubio-Bellido,Alexis Pérez-Fargallo,Jesús P Book 2018 T
描述.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..
出版日期Book 2018
關(guān)鍵詞Energy Efficiency; Energy Demand in Buildings; Energy Consumption; Regression Models; Artificial Neural
版次1
doihttps://doi.org/10.1007/978-3-319-90146-6
isbn_softcover978-3-319-90145-9
isbn_ebook978-3-319-90146-6Series ISSN 2191-5520 Series E-ISSN 2191-5539
issn_series 2191-5520
copyrightThe Author(s) 2018
The information of publication is updating

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發(fā)表于 2025-3-21 20:13:58 | 只看該作者
Springer Lexikon Kosmetik und K?rperpflegeLombard et al. .; UNEP .). In 2010, its absolute consumption was 23.7?PWh and the International Energy Agency indicates that it can reach 38.4?PWh in 2040 (IEA .), being responsible for 38% of the greenhouse gas emissions (UNEP .). Around the world, this sector currently represents 13% of the GDP an
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地板
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https://doi.org/10.1007/978-3-642-34730-6e simulation process is set up, making a distinction between two groups: Climate data and what has been called “test models”. On one side, the 9 different climate zones in which Chile is divided into by the Chilean building standard are considered; these zones cover all the existing climate contexts
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發(fā)表于 2025-3-22 08:53:41 | 只看該作者
Springer Tracts in Modern Physicsemissions for the office buildings in Chile. Through the multivariable regression method, diverse equations will be produced that will bear in mind the parameters mentioned for the different locations. In this way, the designers will be able to know the consequences that their decisions will have on
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發(fā)表于 2025-3-22 20:33:37 | 只看該作者
K. A. Anderson,L. E. Anderson,Ute Villwockction systems, air-conditioning systems and user profiles. Selecting and quantifying the parameters needed is a complex task which requires the designer’s experience and knowledge, as well as an in-depth understanding of the calculation process.
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發(fā)表于 2025-3-22 23:10:27 | 只看該作者
Müfit Bahadir,Harun Parlar,Michael Spitelleree of them (energy consumption, energy demand and CO. emissions) in relation to a large-scale sample of buildings, with all the issues associated to them, such as the nonlinearity of problems related to building design and performance.
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