Coupling of dynamic building simulation with stochastic modelling of occupant behaviour in offices – a review-based integrated methodology

Parys, Wout; Sælens, Dirk; Hens, Hugo

doi:10.1080/19401493.2010.524711

Cited by 104 publications

(58 citation statements)

References 39 publications

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“…In a previous research, Parys concluded that the operation of office equipment is obviously not driven by indoor environmental quality motives. Therefore, it is more logical to link the ratio of internal heat gains over the nominal power of office equipment to the occupancy rate (Parys, Saelens, & Hens, 2011) When the averaged profiles for occupancy and use of electrical appliances are looked into, there is a strong correlation between them with a determination coefficient of 0.94. Looking at workplace level there is no clear correlation.…”

Section: Simulation Energy Reduction Results Compared With the Literamentioning

confidence: 99%

Occupants’ behavioural impact on energy consumption: ‘human-in-the-loop’ comfort process control

Zeiler

Vissers

Maaijen

et al. 2013

Architectural Engineering and Design Management

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Optimizing comfort for occupants and its related energy use is becoming more important. Presently, however, heating ventilation air-conditioning (HVAC) system installations often do not work in practice effectively and efficiently as the behaviour of the occupants is not included. The results are comfort complaints as well as unnecessary high-energy consumption. As the end-user influence becomes even more important within the energy process of sustainable buildings, it is necessary to integrate the occupants in the buildings' performance control loop. Laboratory experiments were performed to look for a correlation between infrared (IR) sensor temperature registrations and individual perceived thermal comfort in an individually conditioned workplace. It proved that it is in principle possible to use the third finger skin temperature as a control parameter for perceived thermal comfort. In another experiment in a real in-use office building, a wireless sensor network was applied to describe user behaviour on room and floor level. The results showed that it is possible to capture individual user behaviour and to use this to further optimize comfort in relation to energy consumption. Based on our experiments, we could determine the influence of occupants' behaviour on energy use and determine possible energy reduction by implementing the human-in-the-loop process control strategy.

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Section: Simulation Energy Reduction Results Compared With the Literamentioning

confidence: 99%

Occupants’ behavioural impact on energy consumption: ‘human-in-the-loop’ comfort process control

Zeiler

Vissers

Maaijen

et al. 2013

Architectural Engineering and Design Management

View full text Add to dashboard Cite

show abstract

“…Occupant"s presence and building managers" control decisions affect energy flexibility in office buildings [40]. Buildings can reduce their energy consumption by 10% if energy consumers (e.g.…”

Section: Hypothesis 22-received Sufficient Information Can Encouragementioning

confidence: 99%

“…Different appliances, devices and distributed energy resources installed in buildings can provide different energy flexibility [7]. Parys et al [40] argue that diverse characteristics of buildings should be acknowledged when developing building systems. Therefore, the integration of renewable energy resources and building management and control system can encourage building owners to participate in the energy flexibility programs (Hypothesis 3).…”

Section: Energy Flexibility Resources and Technologiesmentioning

confidence: 99%

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Consumer Central Energy Flexibility in Office Buildings

Billanes¹,

Ma²,

Jôrgensen³

2017

JEPE

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Energy flexibility in buildings will play an important role in the smart energy system. Office buildings have more potentials to provide energy flexibility to the grid compared to other types of buildings, due to the existing building management, control systems and large energy consumption. Consumers in office buildings (building owners/managers and occupants) take a main role for adopting and engaging in building energy flexibility. This paper provides a systematic review of consumer central energy flexibility in office buildings with the discussion of social, technical and business aspects. This paper clarifies the correlations of consumers" concerns, external influential factors, energy flexibility resources and technology with eight hypotheses. This paper suggests that technical solutions with the integration of distributed energy resources, building management and control system can boost energy flexibility in the office buildings.

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“…Hong et al [24] identified the major types of occupant behaviors in buildings, including occupant presence and movement as well as occupant actions on windows, shades (blinds), lighting, thermostat, HVAC, and plug-in equipment. Data were collected from various locations and types of buildings around the world to construct a library of stochastic models for these occupant behaviors [25][26][27][28][29][30][31][32][33]. For example, window-opening behaviors were described by probabilistic models (logit or Weibull functions) based on field-measured data and largescale surveys; these models have been adopted by several building performance simulation (BPS) programs to determine when occupants open or close windows [34,35].…”

Section: Introductionmentioning

confidence: 99%

A novel stochastic modeling method to simulate cooling loads in residential districts

Yan

Hong

et al. 2017

Applied Energy

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District cooling systems are widely used in urban residential communities in China. Most district cooling systems are oversized; this leads to wasted investment and low operational efficiency and thus energy wastage. The accurate prediction of district cooling loads that supports rightsizing cooling plant equipment remains a challenge. This study developed a new stochastic modeling method that includes (1) six prototype house models representing a majority of apartments in the district, (2) occupant behavior models in residential buildings reflecting the temporal and spatial diversity and complexity based on a large-scale residential survey in China, and (3) a stochastic sampling process to represent all apartments and occupants in the district. The stochastic method was employed in a case study using the DeST simulation engine to simulate the cooling loads of a real residential district in Wuhan, China. The simulation results agree well with the actual measurement data based on five performance metrics representing the aggregated cooling loads, the peak cooling loads as well as the spatial load distribution, and the load profiles. Two currently used simulation methods were also employed to simulate the district cooling loads. The simulation results showed that oversimplified occupant behavior assumptions lead to significant overestimations of the peak cooling load and total district cooling loads. Future work will aim to simplify the workflow and data requirements of the stochastic method to enable its practical application as well as explore its application in predicting district heating loads and in commercial or mixed-use districts.

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Coupling of dynamic building simulation with stochastic modelling of occupant behaviour in offices – a review-based integrated methodology

Cited by 104 publications

References 39 publications

Occupants’ behavioural impact on energy consumption: ‘human-in-the-loop’ comfort process control

Occupants’ behavioural impact on energy consumption: ‘human-in-the-loop’ comfort process control

Consumer Central Energy Flexibility in Office Buildings

A novel stochastic modeling method to simulate cooling loads in residential districts

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