Occupancy behaviour and patterns: Impact on energy consumption of high-rise households in southeast China

Zhang, Wuxia; Calautit, John Kaiser

doi:10.1016/j.segy.2022.100072

Cited by 13 publications

(3 citation statements)

References 36 publications

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“…The reason for this is the difficulty of obtaining information on actual equipment ownership, lifestyles of occupants, comfort preferences and attitudes for large sets of households, such as in high rise buildings. This research gap is addressed by Zhang et al [5], in a paper of this special issue, which studies the relationship between the energy demand and the occupancy of a high rise building in China. The authors survey 112 urban households on questions related to their lifestyles and energy demand, then simulate the energy demand of a case-study skyscraper after which they compare the simulations results to actual energy bills.…”

Section: Smart Energy Demand For Sustainable Developmentmentioning

confidence: 99%

Smart energy demand for the sustainable development of energy, water and environment systems

Gjorgievski

Markovska²,

Mathiesen³

et al. 2022

Smart Energy

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Section: Smart Energy Demand For Sustainable Developmentmentioning

confidence: 99%

Smart energy demand for the sustainable development of energy, water and environment systems

Gjorgievski

Markovska²,

Mathiesen³

et al. 2022

Smart Energy

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“…Effective window design, allowing for ease of operation and access, can lead to improved natural ventilation and light, better indoor air quality (IAQ), and reduced reliance on mechanical HVAC systems [27,28]. However, integrating occupant preferences and external conditions into window design to optimize energy efficiency while ensuring satisfaction remains a complex challenge, underscoring the necessity for further research [29][30][31]. The interaction between window design and occupant behavior exemplifies the broader issue of the gap between expected and actual energy performance.…”

Section: Introductionmentioning

confidence: 99%

Linking Occupant Behavior and Window Design through Post-Occupancy Evaluation: Enhancing Natural Ventilation and Indoor Air Quality

Pourtangestani,

Izadyar,

Jamei

et al. 2024

Buildings

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This study investigates how window design features, such as size, placement, and orientation, might impact occupants’ behavior related to natural ventilation in residential houses and how residents manage natural ventilation to affect indoor air quality (IAQ), comfort, and energy efficiency. By analyzing responses from a questionnaire distributed among 200 occupants, this article reveals that stuffy air, perceived outdoor pollutants, odors, and relative humidity, along with factors like inadequate ventilation, temperature fluctuations, and energy consumption concerns, emerge as primary issues affecting occupants’ comfort and well-being. This study proposes design recommendations for enhancing IAQ, including optimal window placement for cross-ventilation, window-to-wall ratio (WWR) considerations, and the integration of smart window technologies. This research recognizes that window design is not just a technical matter but involves understanding social and behavioral factors as well. By analyzing occupant responses, it aims to provide insights into the socio-technical parameters that should be considered in window design. The findings offer valuable strategies for architects, designers, and homeowners to optimize natural ventilation and underscore the importance of an occupant-centered approach in sustainable building design.

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“…When compared to the normal style, their work shows a 13.4% increase in consumption of the wasteful style, a 9.5% reduction in the moderate style, and a 21% reduction in the austere style. W. Zhang et al [6] conducted a survey on the energy usage of 112 families in high-rise buildings and found that energy consumption and thermal satisfaction vary widely between occupants and that occupant behavior matters more than the quality and quantity of the equipment used for lowering energy usage. MS. Aliero et al [7] showed that different control strategies must be used between commercial and residential buildings to account for occupant responses and unexpected variations in occupancy and weather conditions.…”

Section: Introductionmentioning

confidence: 99%

Occupancy State Prediction by Recurrent Neural Network (LSTM): Multi-Room Context

Diarra,

Maniar,

Masson

et al. 2023

Sensors

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The energy consumption of a building is significantly influenced by the habits of its occupants. These habits not only pertain to occupancy states, such as presence or absence, but also extend to more detailed aspects of occupant behavior. To accurately capture this information, it is essential to use tools that can monitor occupant habits without altering them. Invasive methods such as body sensors or cameras could potentially disrupt the natural habits of the occupants. In our study, we primarily focus on occupancy states as a representation of occupant habits. We have created a model based on artificial neural networks (ANNs) to ascertain the occupancy state of a building using environmental data such as CO2 concentration and noise level. These data are collected through non-intrusive sensors. Our approach involves rule-based a priori labeling and the use of a long short-term memory (LSTM) network for predictive purposes. The model is designed to predict four distinct states in a residential building. Although we lack data on actual occupancy states, the model has shown promising results with an overall prediction accuracy ranging between 78% and 92%.

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Occupancy behaviour and patterns: Impact on energy consumption of high-rise households in southeast China

Cited by 13 publications

References 36 publications

Smart energy demand for the sustainable development of energy, water and environment systems

Smart energy demand for the sustainable development of energy, water and environment systems

Linking Occupant Behavior and Window Design through Post-Occupancy Evaluation: Enhancing Natural Ventilation and Indoor Air Quality

Occupancy State Prediction by Recurrent Neural Network (LSTM): Multi-Room Context

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