Development of window opening models for residential building in hot summer and cold winter climate zone of China

Zhou, Pengzhi; Wang, Haidong; Li, Fēi; Dai, Yuwei; Huang, Chen

doi:10.1016/j.enbenv.2021.03.003

Cited by 17 publications

(6 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it requires more consumption or energy patterns, and it is difficult to attain reliability. Logistic regression (Zhou et al, 2022) is used to easily monitor and handle the humidity and temperature states, and it is easy to interpret and train, and implement. It is hard to solve nonlinear problems and also to predict continuous outcomes.…”

Section: Problem Statementmentioning

confidence: 99%

“…In 2022, Zhou et al (2022) studied the opening and closing of windows in 16 buildings, and they also analyzed this approach during both winter and summer. By analyzing their Predicting the energy consumption experimental results, they found out that the windows were opened for a long time during summer and opened for a short time range during winter because of the temperature of the air.…”

Section: Literature Survey 21 Related Workmentioning

confidence: 99%

“…In 2022, Zhou et al. (2022) studied the opening and closing of windows in 16 buildings, and they also analyzed this approach during both winter and summer.…”

Section: Literature Surveymentioning

confidence: 99%

See 2 more Smart Citations

Analyzing the opening and closing of windows in residential for predicting the energy consumption using optimized multi-scale convolution networks

Sivapriya,

Subbaiyan

2024

IJIUS

View full text Add to dashboard Cite

PurposeThis proposal aims to forecast energy consumption in residential buildings based on the effect of opening and closing windows by the deep architecture approach. In this task, the developed model has three stages: (1) collection of data, (2) feature extraction and (3) prediction. Initially, the data for the closing and opening frequency of the window are taken from the manually collected datasets. After that, the weighted feature extraction is performed in the collected data. The attained weighted feature is fed to predict energy consumption. The prediction uses the efficient hybrid multi-scale convolution networks (EHMSCN), where two deep structured architectures like a deep temporal context network and one-dimensional deep convolutional neural network. Here, the parameter optimization takes place with the hybrid algorithm named jumping rate-based grasshopper lemur optimization (JR-GLO). The core aim of this energy consumption model is to predict the consumption of energy accurately based on the effect of opening and closing windows. Therefore, the offered energy consumption prediction approach is analyzed over various measures and attains an accurate performance rate than the conventional techniques.Design/methodology/approachAn EHMSCN-aided energy consumption prediction model is developed to forecast the amount of energy usage during the opening and closing of windows accurately. The emission of CO2 in indoor spaces is highly reduced.FindingsThe MASE measure of the proposed model was 52.55, 43.83, 42.01 and 36.81% higher than ANN, CNN, DTCN and 1DCNN.Originality/valueThe findings of the suggested model in residences were attained high-quality measures with high accuracy, precision and variance.

show abstract

Section: Problem Statementmentioning

confidence: 99%

Section: Literature Survey 21 Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Analyzing the opening and closing of windows in residential for predicting the energy consumption using optimized multi-scale convolution networks

Sivapriya,

Subbaiyan

2024

IJIUS

View full text Add to dashboard Cite

show abstract

“…Air quality and urban wind environments are crucial to the health of urban inhabitants [1], and pollutants released from vehicles and industrial parks are the main air pollution sources [2]. With the continuous increase in the scale of urban and industrial production, when harmful gas leakage occurs in cities or industrial parks, the ability to trace pollution sources in a timely manner is of great significance for providing early warnings regarding disasters and post-disaster emergency rescue.…”

Section: Introductionmentioning

confidence: 99%

Identification of Pollution Sources in Urban Wind Environments Using the Regularized Residual Method

Tang,

Xue,

et al. 2023

Atmosphere

Self Cite

View full text Add to dashboard Cite

The scale of cities is increasing with continuous urban development. Effective methods, such as the source term estimation (STE) method, must be established for identifying the sources of air pollution in cities to prevent economic losses and casualties caused by pollutant leakage. Herein, methods for optimizing sensor configuration and identifying pollution sources are discussed, and an STE method based on the regularized minimum residual method is proposed. Urban wind environments were simulated using a computational fluid dynamics (CFD) model, and the results were compared with experimental data pertaining to the wind tunnel of an architectural ensemble to verify the model’s accuracy. The sensor layout was optimized using the simulated annealing (SA) algorithm and adjoint entropy, and the relationship between sensor responses and potential pollution sources was established using the CFD model. Pollutant concentrations measured using sensors were combined with the regularization method to extrapolate the pollution source strength, and the regularized minimum residual method was used to obtain the locations of the real pollution sources. The results show that compared with the Bayesian methods, the proposed method can more accurately identify pollution sources (100%), with a smaller source strength error of 2.01% for constant sources and one of 2.62% for attenuation sources.

show abstract

“…Additionally, regardless of the building type, occupants' window-opening behavior is also affected by environmental factors, again with the ambient temperature as the major factor [26,27]. Besides temperature, the CO 2 concentration also showed a significant influence [28][29][30]. In some studies, it has been found that the influence of CO 2 concentration was even more significant than that of indoor and outdoor temperatures [31].…”

Section: Introductionmentioning

confidence: 99%

Modeling Occupant Window Behavior in Hospitals—A Case Study in a Maternity Hospital in Beijing, China

Jiang

Pan

et al. 2023

Sustainability

View full text Add to dashboard Cite

Nowadays, relevant data collected from hospital buildings remain insufficient because hospital buildings often have stricter environmental requirements resulting in more limited data access than other building types. Additionally, existing window-opening behavior models were mostly developed and validated using data measured from the experimental building itself. Hence, their accuracy is only assessed by the algorithm’s evaluation index, which limits the model’s applicability, given that it is not tested by the actual cases nor cross-verified with other buildings. Based on the aforementioned issues, this study analyzes the window-opening behavior of doctors and patients in spring in a maternity hospital in Beijing and develops behavioral models using logistic regression. The results show that the room often has opened windows in spring when the outdoor temperature exceeds 20 °C. Moreover, the ward windows’ use frequency is more than 10 times higher than those of doctors’ office. The window-opening behavior in wards is more susceptible to the influence of outdoor temperature, while in the doctors’ office, more attention is paid to indoor air quality. Finally, by embedding the logistic regression model of each room into the EnergyPlus software to simulate the CO2 concentration of the room, it was found that the model has better applicability than the fixed schedule model. However, by performing cross-validation with different building types, it was found that, due to the particularity of doctors’ offices, the models developed for other building types cannot accurately reproduce the window-opening behavior of doctors. Therefore, more data are still needed to better understand window usage in hospital buildings and support the future building performance simulations of hospital buildings.

show abstract

Development of window opening models for residential building in hot summer and cold winter climate zone of China

Cited by 17 publications

References 38 publications

Analyzing the opening and closing of windows in residential for predicting the energy consumption using optimized multi-scale convolution networks

Analyzing the opening and closing of windows in residential for predicting the energy consumption using optimized multi-scale convolution networks

Identification of Pollution Sources in Urban Wind Environments Using the Regularized Residual Method

Modeling Occupant Window Behavior in Hospitals—A Case Study in a Maternity Hospital in Beijing, China

Contact Info

Product

Resources

About