To understand how to better manage resources in urban environment it is imperative that we develop predictive models related to the behaviour of building occupants. The energy demand and performance of a building depends on the behaviour of occupants engaged in various activities. To understand these energy demands it is important to understand how the building spaces are being used by individuals i.e. the occupancy pattern of individuals. At present there is a lot of research being done simulating building environment using sensors and artificial agents to predict energy usage and other building performance related factors that helps to promote understanding of more sustainable buildings. All these simulation tools try to incorporate models of interaction and most importantly models of occupancy. There has been previous work in detecting occupancy of building users using PIR sensors or ambient sensors detecting changes in carbon dioxide composition or sound. The major shortcoming of the previous methods is the sensors limitation in detecting presence and absence of individual occupants in the room only. It is not possible to know the transition of movement of a person e.g. from office room to meeting room.
An understanding of occupancy in the building environment requires a robust indoor positioning solution to be deployed. Although the positioning system might not require a very high degree of positioning accuracy, the application still requires correct identification of transitions between zones and rooms, and entry and exit from the building. These details together help to get a clear picture of the journey the occupant makes which has possible applications in built environment modelling. The research presents a novel application of indoor positioning technology proposing a prototype positioning system for occupancy detection in building environment and understanding its potential impact on building energy usage using an Agent based occupant model.
There are no existing work which makes use of Indoor positioning to understand building occupancy for the purpose of understanding energy consumption in Buildings. The fact that indoor positioning can provide a more realistic movement profile is an advantage. This PhD looks to exploit this advantage.
The first part of the PhD will look into novel ways how indoor positioning techniques can be used to detect movement taking into consideration various constraints that come into play when observing a realistic occupancy pattern. The proposed positioning solution will be dependent on a simple smart phone to be carried by participants in their pockets all the time and carry on with their daily work in an office environment.
In the second part the positioning algorithm developed, will be used to collect occupancy data from participants and used in a building occupant simulation using an Agent based modelling approach to understand the potential impact on energy consumption such as electricity.
This author is supported by the Horizon Centre for Doctoral Training at the University of Nottingham (RCUK Grant No. EP/L015463/1) and Satellite Applications Catapult.