Energy optimization and prediction in office buildings : a case study of office building design in Chile / Carlos Rubio-Bellido, Alexis Pérez-Fargallo, Jesús Pulido-Arcas.
Rubio-Bellido, Carlos, author.; Pérez-Fargallo, Alexis, author.; Pulido-Arcas, Jesús, author.
2018
NA6234.C5
Available Online

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Title Energy optimization and prediction in office buildings : a case study of office building design in Chile / Carlos Rubio-Bellido, Alexis Pérez-Fargallo, Jesús Pulido-Arcas.
Author Rubio-Bellido, Carlos, author.
ISBN 9783319901466 (electronic book)
331990146X (electronic book)
9783319901459
3319901451
DOI https://doi.org/10.1007/978-3-319-90146-6
Published Cham, Switzerland : Springer, 2018.
Language English
Description 1 online resource.
Other Standard Identifiers 10.1007/978-3-319-90146-6 doi
Call Number NA6234.C5
Dewey Decimal Classification 725/.230983
Summary 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.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Digital File Characteristics text file PDF
Source of Description Online resource; title from PDF title page (SpringerLink, viewed April 26, 2018).
Added Author Pérez-Fargallo, Alexis, author.
Pulido-Arcas, Jesús, author.
Series SpringerBriefs in energy.
Available in Other Form Energy optimization and prediction in office buildings.
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Intro; Contents; Symbols and Abbreviations; 1 Introduction; 1.1 Energy in Buildings; 1.2 Office Sector in Chile; 1.3 Legal Framework and Energy Services; References; 2 Research Method; 2.1 Introduction; 2.2 Calculation Procedure; 2.2.1 Internal and External Loads; 2.2.2 Heat Balance; 2.2.3 Heat Gains; 2.2.4 Energy Demand; 2.2.5 Energy Consumption and CO2 Emissions; 2.3 Test Models; 2.3.1 Location; 2.3.2 Geometry; 2.3.3 Constructive Systems; 2.4 Climate Context; 2.4.1 Current Climate Zones; 2.4.2 Climate Change Simulation; 2.5 Optimization and Prediction Methods; 2.5.1 Minimal Energy Demand.

2.5.2 Multiple Linear Regressions2.5.3 Multilayer Perceptron; References; 3 Energy Demand Analysis; 3.1 Introduction; 3.2 Climate Variation; 3.3 Effects on Annual Energy Demand; 3.3.1 WWR and FR Influence; 3.3.2 Annual Energy Demand for Different Climate Scenarios; 3.3.3 Heating and Cooling Energy Demand for Different Climate Scenarios; 3.4 Effects on Design Strategies; 3.4.1 Evolution on Annual Energy Demand; 3.4.2 Evolution on WWR and FR; 3.5 Discussions; 4 Multiple Linear Regressions; 4.1 Introduction; 4.2 Energy Consumption; 4.3 CO2 Emissions; 4.4 Regression Models Validation.

4.5 Discussions5 Artificial Neural Networks; 5.1 Introduction; 5.2 Data Description; 5.3 Data Pre-processing; 5.4 Comparison with Linear Regressions; 5.5 Discussions.

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