An agent-based framework to study occupant multi-comfort level in office buildings

With the trend towards energy efficient buildings that diminish fossil fuel usage and carbon emissions, achieving high energy performance became a necessity. Allowing occupants to be actively involved during the design and operation phases of buildings is vital in fulfilling this goal without jeopardizing occupant satisfaction. Although different occupant behavior types were considered in prior research efforts, recent tools did not however examine simultaneously visual, thermal and acoustic comfort levels. This paper presents work targeted at efficiently studying occupant multi-comfort level using agent-based modeling with the ultimate aim of reducing energy consumption within academic buildings. The proposed model was capable of testing different parameters and variables affecting occupant behavior. Several scenarios were examined and statistical results demonstrated that the presence of different occupant behavior types is deemed necessary for a more realistic overall model, and the absence of windows results in an acoustic satisfaction with a decrease in (HVAC) use.

With the expected increase in global population by 39% in 2035 (Dixit e. al., 2010), relying on new renewable resources of energy such as solar energy, wind energy, wave energy among many others has become imperative. Besides opting for alternative energy resources, current use of energy should be optimized. Recently, attention has been placed on energy efficient buildings (Yang et al., 2014). As a matter of fact, increasing energy use efficiency is one of the key approaches for energy consumption and carbon emissions reduction as part of the climate change mitigating efforts. According to Yang et al. (Yang et. al, 2014), reductions in energy expenses and decrease in the environmental pollution may be achieved by reducing the consumption of energy in buildings. People spend more than 90% of their time indoors (Virote and Neves-Silva, 2012), and as such around 40% of the global energy is consumed by buildings (Pout et al., 2002). Therefore, efficient energy use should be adopted especially that it is anticipated that the energy consumption in buildings is expected to increase by 19% in the upcoming years (Energy Information Administration, 2010).

Although several types of buildings exist, targeting the commercial type, in particular academic buildings, is of paramount importance, as the occupants seldom have the incentive to reduce their energy consumption (Gul and Patidar, 2015). They usually focus on completing their job tasks rather than saving on energy (Andrews et al., 2013). Additionally, it was stated that the energy consumed in commercial buildings during non-working hours is typically more than half of the total energy consumed (Masoso and Grobler, 2010) as typical occupant behavior includes keeping the HVAC, electronic devices, appliances and lights on even when not needed or upon exiting the space.

Therefore, getting occupants actively involved during the design and operation phases of buildings is vital in achieving high energy performance without jeopardizing occupant satisfaction or comfort level. However, recent tools did not examine simultaneously, while considering different occupant behavior types, visual, thermal and acoustic comfort levels. The objective of the paper is thereby to design a comprehensive agent based framework aiming at studying occupant multi-comfort level in academic buildings.

Measured energy consumption in buildings has demonstrated large discrepancies with the original estimates. Among various factors contributing to the discrepancies, occupant behavior is a driving factor. Researchers have observed that occupant behavior has a great influence on energy consumption at the operation phase (Azar and Menassa, 2012). Studies found that 54% of the energy in a building is wasted during non-working hours and 38% during working hours due to appliances being typically placed on standby mode (Kavulya and Becerik-Gerber, 2012). Other previous work (Kwak et al., 2013) found that occupants consumed more energy when occupying large size rooms due to a higher HVAC and light power usage. In this case, energy efficient efforts were channeled toward creating efficient meeting schedules and reallocating occupants in the right rooms (Kwak et al., 2013). Accordingly, with occupants adopting energy conscious behaviors, energy consumption can be greatly reduced (Azar and Menassa, 2012). However, typical occupant adaptive behavior includes how an occupant adapts to changing environment conditions and sets comfort criteria. Therefore, researchers worked on enhancing occupant satisfaction level and improving indoor environment qualities through capturing their comfort levels using mobile cellphone applications (Jazizadeh et al., 2014). Occupants within the area studied were asked to specify their satisfaction level toward the room temperature, air flow and light intensity. Based on this input data, the buildings’ indoor conditions were adjusted to increase the comfort levels of the majority of occupants.

Agent-Based Model Agents and Variables

Agent-Based Model Agents and Variables

Related posts