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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.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract
Energy efficiency in the building sector accounts for around 30–40% of the total energy consumption of human activities as per diverse sources (Pérez-Lombard et al. 2008; UNEP 2012). 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 2013), being responsible for 38% of the greenhouse gas emissions (UNEP 2012). Around the world, this sector currently represents 13% of the GDP and it is expected that it increases to 15% in 2020 (Global Construction Perspectives and Oxford Economics 2013). Its total budget sat at 8.2 trillion dollars in 2013 (IHS Economics 2013) and it is foreseen that this will grow to 15 trillion dollars in 2025. As such, those strategies that focused on energy efficiency, consumption and emission reduction are one of the main challenges of the construction sector. Thus, the need of predicting these factors has forced official entities, like the European Union since 2002 (European Commission 2002), to obligatorily establish the measuring of buildings’ energy efficiency.
Carlos Rubio-Bellido, Alexis Pérez-Fargallo, Jesús Pulido-Arcas

Chapter 2. Research Method

Abstract
To evaluate the energy behavior of the buildings it is necessary to know numerous data related with its geometry, internal and external loads, construction 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.
Carlos Rubio-Bellido, Alexis Pérez-Fargallo, Jesús Pulido-Arcas

Chapter 3. Energy Demand Analysis

Abstract
The energy demand analysis is performed following a methodology that comprises three main stages (Fig. 3.1). In the first stage, the input data for the 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 in the country and have been called “climate scenarios”. For each of these 9 zones, files containing the current climate data have been compiled. These files have been “morphed” according to the predicted climate scenarios for 2020, 2050 and 2080, producing a new set of climate files for these future years. These files will be used as the external conditions for the calculation of the energy demand. On the other side, test models have been defined following the parameters of the TDRe standard; some variables have been fixed while those related to the building shape and the enclosure will be set as free and studied in this research.
Carlos Rubio-Bellido, Alexis Pérez-Fargallo, Jesús Pulido-Arcas

Chapter 4. Multiple Linear Regressions

Abstract
This chapter intends to develop a mathematical model that allows predicting, with an acceptable degree of uncertainty, the energy consumption and CO2 emissions 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 the energy consumption and CO2 emissions. This research has an eminently practical nature and is susceptible to being applied in the future design and construction of buildings.
Carlos Rubio-Bellido, Alexis Pérez-Fargallo, Jesús Pulido-Arcas

Chapter 5. Artificial Neural Networks

Abstract
This chapter intends to demonstrate the performance and reliability of ANN in predicting large scale data not only for a single parameter, but for three of them (energy consumption, energy demand and CO2 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.
Carlos Rubio-Bellido, Alexis Pérez-Fargallo, Jesús Pulido-Arcas
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