Search

The present work focuses on the development of a novel application that aims to better understand energy-related indoor occupant behaviour (OB). The thesis briefly introduces the key features of OB, presents the current state of the art and highlights the present challenges related to this field. The goal of the thesis is to present how potential challenges can be addressed with the use of a novel monitoring technique for in-situ occupant tracking. It is a depth registration technique that has practical implementations in various applications focusing on surface scanning. The main goal of the use of this technique in OB studies is to increase the quality of the measurement resolution to better understand the reasons for occupant activities regarding indoor energy use. It is expected that with an increase in the measurement resolution, it will be possible to observe and collect new information about occupants and their energy utilization. Therefore, it will be possible to detect avoidable waste of energy. The proposed monitoring technique has not previously been commercially implemented in this research domain. Thus, there were no guidelines allowing for quick implementation. The methodology presented in the thesis describes step-by-step all the basic information required to understand the ideas and proposals for device utilization. Extended explanations are included in the appendices, and each subchapter refers to relevant appendices. Beyond the direct monitoring of occupants, the thesis explains the use of the collected data and shows how the data can be implemented in an agent-based model (ABM). The proposed model focuses on direct simulation of an occupant’s activities by recreating these activities through the portrayal of the occupant as an agent. The main functionalities that are provided by the model enable simulation of occupant movement, sensation of the thermal environment, interaction with indoor appliances and decision-making processes. The results obtained from the simulation show that a novel approach for the simulation of occupants is achievable. The OB represented by the ABM can be implemented as a calculation engine, which considers the occupant’s actions as activity triggers. The model is designed to connect various scientific specialities that focus on OB aspects, and combines each of the aspects into one common platform. The communication between scientific specialities that can be initialised by the proposed platform will bring a greater understanding of energy-related OB. Consequently, it may lead to a greater reduction in the use of energy resources without compromising occupants’ indoor comfort.

Human behaviour is a multidisciplinary subject that is being investigated by numerous scientists around the world. The ability to understand and forecast reactions can be beneficial for all scientific branches that are related to this subject. With the increase in the accessibility of personal monitoring systems, a new era of human behaviour research has begun. Currently, in the market, there are many inexpensive and reliable solutions that can grant extra insights into the everyday lives of human beings. Regardless of whether the monitoring solutions are stationary or wearable, they can provide very detailed information with high operational and temporal resolution. Access to these data has advanced our understanding of human routines and habits, but it does not provide insights into the “soft” data that define human beings. Once the quantitative data has started to enrich scientific databases, the community has started to question whether such information is suitable to detect or record qualitative (“soft”) output. Typed text is not included, but it is possible to extract existing data and to obtain “soft” data. This manuscript will try to address this issue. It proposes a straightforward solution that can have great potential for implementation purposes. It investigates the existing literature and tries to evaluate its applicability for numerical implementation. One of the highlights of the manuscript is the proposal of a novel modelling solution that can cooperate with other occupant behaviour-related simulation models. Finally, the manuscript tries to outline future steps to enable the possibility of translating or modelling quantitative input into qualitative output. © The authors. Published by SINTEF Academic Press 2020 This is an open access publication under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Energy consumption in households stand for approximately 30% [1] of the total energy consumption in Norway, this is related to a number of factors, implying that energy use in buildings can also be optimized in various fields. Occupants behavior is considered to be one of the factors influencing the energy consumption as well as the indoor environment. However, the influence of occupants behavior is complex and hard to predict, thus under-recognized or over simplified in most cases. This thesis focus on the interaction between occupants and their surroundings in residential buildings, mainly on the issue of furniture settings. Increasing our knowledge about how the space and the equipment will be used by occupants, what their preferred device and furniture setting will influence the room condition in relation to temperature and air velocity. The study takes place in an apartment located in Trondheim, Norway, more specifically the living room. Using a ventilation meter and thermal camera to perform a series of tests and measurements to gain information about the indoor environment, size of furniture and the rooms dimensions. Thereby adopting software such as Solidworks and ANSYS to recreate a geometrical representation of that selected room and preform a series of numerical CFD simulation based on these measurements. Various setting of the furniture is simulated (four to be exact), each one is different from the other but has the same boundary conditions. Introducing Microsoft Kinect for occupant migration monitoring in the selected living room, conduct investigation regarding the thermal comfort sensation. This device was placed in the living rooms corner for almost a week, recording occupants movement throughout all four different room scenarios. Recorded data is obtained in form of clock data (Y/M/D/h/min/s) and point location (25 points in 3D [X, Y, Z]) distance from device [m]. [2] Processed data from the Microsoft Kinect device demonstrated how the furniture setting had a noticeable influenced the occupant behavior (pattern). As it successful managed to detect occupant preferred furniture and place accordingly. ANSYS numerical CFD simulation achieved 88-98% result accuracy, according to the set boundary conditions and method used. Several figures, charts, and tables are introduced in chapter 6, describing temperature, air velocity and air volume across all four selected room scenarios. Most of the results gathered from the CFD simulations support the idea that furniture settings could impact conviction flows in living room, even if its marginal. The person living in that apartment also mentioned that some room scenarios where more convenient than others, in terms of his mobility, access to other room furniture and air quality. His idea of a better room scenario also matched the one predicted by the CFD study.

Energy-related occupant behaviour has a major impact on the energy use in buildings, and occupant behaviour is also a key parameter for building design optimization, energy diagnosis, performance evaluation and building energy performance simulation. Using today's models for occupant behaviour, there is major differences between energy use simulated and real measurements for the same building. One of the main reasons for this difference is the lack of proper occupant behaviour models. The work of this thesis will support the development of knowledge regarding occupant behaviour related to energy, which can lead to development of more accurate occupant behaviour models. The work accomplished concerns mainly the kitchen and kitchen appliances. To get more information of household appliances, a knowledge base of typical devices in a residential has been prepared. The knowledge base contains device performance and characteristics of electrical energy consumption in time for 21 different household devices. A survey concerning use and placement of kitchen appliances have been conducted to get more information on use of devices. The results of the part of survey which dealt with placement of devices in the kitchen have been used as base for development of heat maps of device placement in the kitchen. Field measurements from the ZEB - Living Lab have been used for decoding of electricity consumption behaviour in residential buildings. Based on this, and the developed heat maps, a simulator for household usage have been developed. The simulator has to be further developed, and suggestions for this work is given.