Civil Engineering Reference
In-Depth Information
energy (Oikonomou et al.
2009
). In particular, most of the energy waste in a
building occurs during non-occupancy hours (Masoso and Grobler
2010
).
Usually, building retrofitting projects are associated with high costs and large
payback periods. These projects rely on intelligent building technologies or
energy-efficient technologies to achieve sustainable consumer behaviour (Midden
et al.
2008
). The outcome of building retrofitting projects is often unpredictable
due to the uncertainty surrounding occupant behaviour as studied in (Murray
2009
). Therefore, it is crucial to take into account the interactions between the
occupants and the building when investing in energy efficient technologies. These
investments could be sustained through a model that reliably reproduces the
building's usage patterns and predicts the building energy consumption while
analysing potential energy saving areas.
Technology can help raising awareness of energy use and reduce the level of
consumption and waste (Ockwell
2008
; Midden et al.
2007
). Retrofitting of
existing buildings has many challenges and opportunities, and a major issue is the
effectiveness in the adopted technologies and measures (Ma et al.
2012
). As a
result, a framework to support the decision-making process in order to ensure the
outcome of investments in energy efficiency is mandatory (Masini and Menichetti
2012
).
To summarise, retrofitting projects should be adapted to each specific building
and not through a generic or systematic approach to the building stock. Moreover,
being able to conveniently adapt a building to its occupants ensures a better in-
doors working and living environment (Frontczak and Wargocki
2011
).
1.1 Research Problem
The behaviour of building occupants are often based on assumptions rather than
based on measured observations or resulting predicting models. These assumptions
lead to limitations in current simulation tools and provide a poor instrument to
predict the outcome of energy efficiency measures in a building. This results in a
gap between the estimated energy savings and the actual energy savings from
retrofitting projects (Yalcintas
2008
). Hence, current simulation tools must be
provided with prediction models that reliably predict the energy consumption of a
specific building according to its actual utilisation patterns.
The objective is to develop an energy consumption model (ECM) for predicting
the energy consumption of a building based on actual measured performance and
usage data. Furthermore, this ECM is an added value when applied to the creation
of alternative scenarios implementing energy-efficient technologies.
This research work explores the applicability of stochastic models to represent
the space occupancy and the occupant's behaviour. The research question is as
follows: Can stochastic models be used to model the space occupancy patterns and
occupant's behaviour in order to predict the energy consumption of the building
and its waste under different conditions? The adopted solution approach is
