Lighting energy consumption in ultra-low energy buildings: Using a simulation and measurement methodology to model occupant behavior and lighting controls

Zhu, Panyu; Gilbride, Michael; Yan, Da; Sun, Hongshan; Meek, Christopher

doi:10.1007/s12273-017-0408-6

Cited by 28 publications

(12 citation statements)

References 19 publications

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“…The results showed that energy consumption might change by more than 150% when occupants with different energy characteristics are considered. Similar studies on lighting energy uncertainty using light switch models [17,18] and on cooling or heating energy uncertainty using different energy use patterns have also been reported. Due to limited availability of shade behavior models, the uncertainty of energy performance related to manual shades has rarely been studied by researchers.…”

Section: Yearsupporting

confidence: 65%

Uncertainty of Energy and Economic Performance of Manual Solar Shades in Hot Summer and Cold Winter Regions of China

Yao

Zheng

2019

Sustainability

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Occupant behavior is recognized as a major source of discrepancy between simulated and actual energy consumption. This study investigates the uncertainty of energy and economic performance of manual solar shades for the south facade. A developed stochastic model for manual solar shades based on a discrete-time Markov chain method was constructed in Building Controls Virtual Test Bed (BCVTB) for co-simulation with EnergyPlus. The stochastic shade model was compared with deterministic models concerning energy savings potential and life cycle economic performance at different building scales (i.e., from a single room to a whole building). The results show that annual energy uncertainty, due to occupant behavior, on manual shades can be neglected at the building level, whereas for sizing heating equipment, energy uncertainty should be considered. The payback period for manual shades is about 10 years and, in general, a larger building has a higher economic performance. Comparative analysis shows that there is a relatively big performance overestimation or underestimation by commonly used deterministic models in building simulation tools, and thus may lead to a biased economic analysis or even an inappropriate design decision when comparing different energy-saving measures.

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Section: Yearsupporting

confidence: 65%

Uncertainty of Energy and Economic Performance of Manual Solar Shades in Hot Summer and Cold Winter Regions of China

Yao

Zheng

2019

Sustainability

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“…Similarly alternative less energy intensive suggestions have been made for cooling and miscellaneous energy consumption equipment as shown in the figures 4 and 5. In addition Use of sensors for automatic lighting control system in washrooms, corridors, staff rooms and in offices to control and detect the actual requirements of lights [17]. It was also recommended to switch off the lights in unused area by installing automatic sensors and control system to save sustainable amount of energy.…”

Section: Resultsmentioning

confidence: 99%

Energy Audit: A Case Study of UET Jalozai Campus

Khan¹,

Peshawar²,

Muhammad³

et al. 2019

IJEW

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Pakistan is facing a decade long worst crises of energy. The demand supply gaps, heavy dependence on imported fossil fuel and inefficient consumption patterns are major thematic reasons of crises. Energy conservation is a low hanging fruits which can be materialized to answer the crises. This is a joint academia and industry projects, where detailed energy audit of educational facility of University of Engineering and Technology Jalozai Campus was carried. The significant energy consumer areas of educational building like lighting, Heating and cooling system and generation plants were deeply investigated. In additions, the male and female boarding houses were audited. Based on Illumination Engineering Standards and ASHRAE level III benchmarking, various energy conservations measures were recommended including efficient lighting system, installation of occupancy sensors, office appliances scheduling and transition towards energy efficient fans. The suggested recommendations implementations are capable to save a 22.3% of electricity in the facility. The economic analysis shows a payback period six to eighteen months for various conservation measures. In addition, a behavioral training and mobilization drive is highly recommended to build the conservation culture on gross root level as integrated part of societal practices and cultural values.

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“…To study occupant behaviors, most of the related research defined them on the basis of occupant movements [39][40][41][42][43] and the control actions of occupants on windows, lights, air conditioning terminals and other equipment [26,[44][45][46][47][48][49][50][51]. Wang et al [40] indicated that building occupant behaviors can be broken down into two aspects: occupancy, which refers to the in-building rate; and occupants' control behaviors on building devices, including windows, doors, blinds, air conditioning terminals, lights and equipment (TVs, computers, printers, etc.).…”

Section: Definition Of the Occupant Behaviors In Buildingsmentioning

confidence: 99%

The Interaction Effect of Occupant Behavior-Related Factors in Office Buildings Based on the DNAS Theory

Yang

Liu

2021

Sustainability

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Occupant behavior is acknowledged as a main contribution to building energy consumption. Many efforts have been devoted to identifying the impact of occupant behaviors on building energy consumption. However, the lack of understanding of the interaction effects among occupant behavior-related factors, to some extent, can lead to inaccurate results. To decode these complex interactions, this study was conducted to investigate the interaction effects of occupant behavior-related factors. A survey based on the Drive-Need-Action-System (DNAS) theory was used to describe the occupant behaviors. Then, based on the survey, a simulation model of an office building was applied for estimating the energy consumption led by different occupant behaviors. Finally, an orthogonal design of experiments (DOE) method combined with Pareto analysis was used to quantify the interactions of occupant behavior-related factors on energy consumption. Results show that factor combinations with strong interaction effects include: (1) lighting control and lighting fixture type and (2) computer control and tolerance of temperature range. The results provide important reference for building designers and facility managers toward a better understanding of the influences of occupant behaviors on building energy consumption.

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Lighting energy consumption in ultra-low energy buildings: Using a simulation and measurement methodology to model occupant behavior and lighting controls

Cited by 28 publications

References 19 publications

Uncertainty of Energy and Economic Performance of Manual Solar Shades in Hot Summer and Cold Winter Regions of China

Uncertainty of Energy and Economic Performance of Manual Solar Shades in Hot Summer and Cold Winter Regions of China

Energy Audit: A Case Study of UET Jalozai Campus

The Interaction Effect of Occupant Behavior-Related Factors in Office Buildings Based on the DNAS Theory

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