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Computational Assessment of the Impact of Occupant-Related Assumptions on the Thermal Performance of an Office Building

Federica Soncini , Sara Torabi Moghadam

Computational Assessment of the Impact of Occupant-Related Assumptions on the Thermal Performance of an Office Building.

Rel. Stefano Paolo Corgnati, Valentina Fabi, Ardeshir Mahdavi , Farhang Tahmasebi . Politecnico di Torino, Corso di laurea magistrale in Architettura Per Il Progetto Sostenibile, 2014


With the decline of fossil energy resources, the necessity to change our way of energy usage is apparent. Building sector, indeed, account for 40% of the total energy consumption in EU member states (EC, 2004). Consequently, in recent years, attention has focused increasingly towards the realization of sustainable buildings with the aim of reducing thè global energy consumption and environmental impacts of thè construction sector. Therefore, building performance simulation tools have been used to help designers achieve their goals in designing energy-efficient buildings. However, buildings' actual energy performance frequently does not meet thè expectations at thè design phase. One of the potential reasons for thè discrepancy between expected and actual energy performance are the uncertainties associated with building occupants' presence and behavior, as shown in previous literatures.

Existing dynamic energy simulation tools, developed in thè last decade, exceed thè static size of thè simplified methods through a better and more accurate prediction of energy use; however, they are stili unable to replicate the actual dynamics that govern energy uses within buildings. Energy performance is dramatically affected by thè behavior of users inside buildings: each architectural project is proposed on thè base of a singular spatial organization and occupier’s usability, but once thè building-plant system has been realized, this could be used differently from the assumptions made in the design phase. Moreover, the use of imprecise and standardized occupant behavior can blur the effect of other energy saving solutions.

The inappropriateness of deterministic assumptions about occupants’ interactions with building's Controls is proved by the field evidence: despite on-going improvements in the building envelope thermal performances, energy demand has not decreased in the expected way. Moreover, recent studies compare deterministic simulation results to the simulation results where stochastic models are implemented, showing a significant energy use increase when switching from one approach to another [(Fabi et ai, 2012), (D’Oca, 2012), (Buso, 2012)]. For these reasons, a stochastic simulation of the designed edifice, in which statistical methods can be used to generate individual occupancy patterns, indeed of fixed schedules, is needed.

Several investigations have been conducted inliterature in order to comprehend thè most influencing drivers related to occupant behavior in an office building. Accordingly to previous researches (Annex 53, ECBCS), is possible to highlight that users principally restare their comfort condition by opening and closing windows. It is therefore important to take window opening behavior into account, when designing energy efficient buildings.

Given this background, the main purpose of thè presented research is to investigate the implications of different assumptions with regard to window operation in a mechanically and naturally ventilated office building for thè energy use and located in different locations, i.e. Turin and Athens, with the aim to evaluate the influence of climate on the predicted energy consumption. Toward this end, a dynamic numeric simulation application, which necessarily takes into account thè possibility that an action is carried out through statistical input parameters, was deployed to simulate the building thermal performance. This new method defines behavioral models by using an algorithm based on a statistical data set built on monitoring occupants’ real interaction within the building-plant system, published by Haldi and Robinson (2009). Then, an individual behavioral model approach, taken from Parys’ (2013) PhD thesis, was developed by implementing the logistic regression models for ac live and passive users with the purpose to compare an aggregated behavioral model to a single one, where the differences between users are displayed.

The method of the research developed in this master thesis is based on the assumption that only switching from an automated approach to a manual one, it will be feasible to obtain energy consumption prediction closer to reality, already in the design phase.

Relators: Stefano Paolo Corgnati, Valentina Fabi, Ardeshir Mahdavi , Farhang Tahmasebi
Publication type: Printed
Subjects: A Architettura > AJ Buildings and equipment for administration, commerce and defense
S Scienze e Scienze Applicate > SG Fisica
SS Scienze Sociali ed economiche > SSF Scienze sociali
Corso di laurea: Corso di laurea magistrale in Architettura Per Il Progetto Sostenibile
Classe di laurea: UNSPECIFIED
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/3630








CHAPTER loverview & Problem Statement

1.1 Introduction

1.1.1 Sustainability in Energy and Building

1.1.2 Energy Efficiency Policies in thè EU

1.2 Motivation

1.3 Background

1.3.1 Occupant Behavior

1.4 Research Objectives

1.5 Thesis Outline


CHAPTER 2_Reference Office Building

2.1 Introduction

2.2 Model Description

2.2.1 Envelope’s Characteristic

2.2.2 Thermal Zoning

2.3 Internal Boundary Conditions

2.4 External Boundary Conditions: Weather

CHAPTER 3 Modelling Tools

3.1 Introduction

3.2 Building Performance Simulation

3.3 Dynamic Simulation Software IDA Ice

3.4 Uncertainty Analysis in Building Simulation

CHAPTER 4_Modelling Approach

4.1 Introduction

4.2 Methodology

4.3 Occupant Control of Windows

4.3.1 Literature Review

4.3.2 Implemented Model

4.5 Building Model

4.5.1 Fully-automatic VS Manual Approach


CHAPTER 5_Uncertainty Analysis of Occupant Behavior: thè Climate of Turin

5.1 Simulations Performed for thè Climate of Turin

5.2 Building Level-Based Approaches

5.2.1 Annual Analyses in Turin’s Climate

5.2.2 Monthly Analysis in Turin’s Climate

5.3 Zone Level-Based Approaches

5.3.1 Annual Analysis in Turin’s Climate

5.4 Discussion

CHAPTER 6_EvaIuating thè Influence of Occupant Behavior in Different Climates: Comparison Turin and Athens’s Results

6.1 The Reference Office Building in Different Climates

6.2 Building Level-Based Approaches

6.2.1 Annual Analysis in Athens’ Climate

6.2.2 Monthly Analysis in Athens’ Climate

6.3 Zone Level-Based Approaches

6.3.1 Annual Analysis in Athens’ Climate

6.4 Discussion

CHAPTER 7_Active & Passive Users for Dynamic Building Simulation

7.1 Development of Individual Behavioral Model

7.2 Building Level-Based Approach

7.2.1 Annual Analyses in Turin’s Climate

7.3 Discussion


CHAPTER 8_Conclusions & Recommendations...

8.1 Introduction

8.2 Comparison with Literature

8.3 Recommendation

8.3.1. Prospective for Future Research

8.4 Conclusions







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