Modelling electricity consumption in office buildings: An agent based approach

Abstract

a b s t r a c tIn this paper, we develop an agent-based model which integrates four important elements, i.e. organisational energy management policies/regulations, energy management technologies, electric appliances and equipment, and human behaviour, to simulate the electricity consumption in office buildings. Based on a case study, we use this model to test the effectiveness of different electricity management strategies, and solve practical office electricity consumption problems. This paper theoretically contributes to an integration of the four elements involved in the complex organisational issue of office electricity consumption, and practically contributes to an application of an agent-based approach for office building electricity consumption study.

The potential for energy reduction in UK commercial offices through effective management and behaviour change

“…(Webber et al 2001& Vereecken et al 2010 As noted by Junnila (2007) few studies have focused on quantifying the enduser influence on energy consumption, furthermore most energy managers believe end users influence to be minimal (Lukas, 2000). However it has previously been established that energy use of desktop equipment is highly influenced by occupant behaviour and is flexible in nature (Zhang, Siebers & Aickelin, 2011). This view is supported by a study by Kawamoto, Shimoda and Mizuno (2003) which estimated that for an average working day the actual in-use utilisation of desktop equipment may commonly be as low as 43%.…”

“…Additionally many office workers don't power down equipment at the end of the working day (Berl & de Meer 2011) and even fewer unplug equipment that may still draw power when turned off. A US field survey of office equipment operating patterns (Webber et al 2001) found that only 44% of computers and 32% of monitors where turned off at night, a similar UK based study (Zhang et al 2011) found that 60% of occupants don't power down at night time, with 31% powering down just occasionally and only 9% powering down regularly. Comparison of these two studies would seem to suggest that organisational or cultural background may have an influence on the rate of power down and reinforces the view that small power consumption patterns are generally not fully understood but offer significant potential for savings.…”

A Review of the Regulatory Energy Performance Gap and Its Underlying Causes in Non-domestic Buildings

“…People are very different in their behavior through culture, upbringing and education, making their influence on energy consumption highly variable. One of the major factors that has been reported to have a large influence on the discrepancy between predicted and measured energy use is the issue of night-time energy use related to leaving office equipment on (Kawamoto et al, 2004;Masoso and Grobler, 2010;Zhang et al, 2011;Mulville et al, 2014), this can be both related to occupant behavior (not turning off equipment) and assumptions for operational schedules, extended working hours not taken into account in the design model. In an uncontrolled environment (not extensively monitored), it is impossible to determine how one or the other is influencing the discrepancy.…”

On the multi-agent stochastic simulation of occupants in buildings

“…Agents are often described as objects within programs that control their actions based on their perceptions of the environment (Huhns and Singh, 1998). Multi-agent simulation is a tool that has been developed primarily in the social sciences to effectively model human interaction (Bonabeau, 2002, Zhang et al, 2011. Its use in the social sciences has typically been to study behaviours that emerge from bottom up interactions, allowing the creator to make judgements as to what has caused these emergent behaviours and whether they correspond with expectation from social theory.…”