Mobile QR Code QR CODE : Korean Journal of Air-Conditioning and Refrigeration Engineering
Korean Journal of Air-Conditioning and Refrigeration Engineering

Korean Journal of Air-Conditioning and Refrigeration Engineering

ISO Journal TitleKorean J. Air-Cond. Refrig. Eng.
  • Open Access, Monthly
Open Access Monthly
  • ISSN : 1229-6422 (Print)
  • ISSN : 2465-7611 (Online)

References

1 
Ahn K. U., Park C. S., 2016, Correlation between occupants and energy consumption, Energy and Buildings, Vol. 116, pp. 420-433DOI
2 
Obeidat L. M., Al Nusair S., Ma'bdeh S., Bataineh R., 2024, Redefining realistic and stochastic occupancy schedules and patterns for residential buildings in Jordan, Energy, Vol. 313, pp. 133641DOI
3 
Laitner J. A., Ehrhardt-Martinez K., McKinney V., 2009, Examining the scale of the behaviour energy efficiency continuum, pp. 217-224Google Search
4 
Armel K. C., Gupta A., Shrimali G., Albert A., 2013, Is disaggregation the holy grail of energy efficiency?, The case of electricity, Energy policy, Vol. 52, pp. 213-234DOI
5 
Song W., Calautit J., 2025, Impact of occupancy behavior on building energy efficiency: What’s next in detection and monitoring technologies?, Next Energy, Vol. 8, pp. 100350DOI
6 
Labeodan T. M., De Bakker C., Rosemann A. L. P., Zeiler W., 2016, On the application of wireless sensors and actuators network in existing buildings for occupancy detection and occupancy-driven lighting control, Energy and Buildings, Vol. 127, pp. 75-83DOI
7 
Roselyn J. P., Uthra R. A., Raj A., Devaraj D., Bharadwaj P., Kaki S. V. D. K., 2019, Development and implementation of novel sensor fusion algorithm for occupancy detection and automation in energy efficient buildings, Sustainable Cities and Society, Vol. 44, pp. 85-98DOI
8 
Zuraimi M. S., Pantazaras A., Chaturvedi K. A., Yang J. J., Tham K. W., Lee S. E., 2017, Predicting occupancy counts using physical and statistical Co2-based modeling methodologies, Building and Environment, Vol. 123, pp. 517-528DOI
9 
Chaudhari P., Xiao Y., Cheng M. M. C., Li T., 2024, Fundamentals, algorithms, and technologies of occupancy detection for smart buildings using IOT sensors, Sensors, Vol. 24, No. 7, pp. 2123DOI
10 
Li B., Tavakoli A., Heydarian A., 2023, Occupant privacy perception, awareness, and preferences in smart office environments, Scientific Reports, Vol. 13, No. 1, pp. 4073DOI
11 
Zhan S., Chong A., 2021, Building occupancy and energy consumption: Case studies across building types, Energy and Built Environment, Vol. 2, No. 2, pp. 167-174DOI
12 
Richardson I., Thomson M., Infield D., 2008, A high-resolution domestic building occupancy model for energy demand simulations, Energy and buildings, Vol. 40, No. 8, pp. 1560-1566DOI
13 
Razavi R., Gharipour A., Fleury M., Akpan I. J., 2019, Occupancy detection of residential buildings using smart meter data: A large-scale study, Energy and Buildings, Vol. 183, pp. 195-208DOI
14 
Kleiminger W., Beckel C., Staake T., Santini S., 2013, Occupancy detection from electricity consumption data, pp. 1-8Google Search
15 
Zhou Z., Liu Y., Hu T., Wang C., 2023, Two unsupervised learning algorithms for detecting abnormal inactivity within a household based on smart meter data, Expert Systems with Applications, Vol. 230, pp. 120565DOI
16 
Bethke G. M., Cohen A. R., Stillwell A. S., 2021, Emerging investigator series: disaggregating residential sector high-resolution smart water meter data into appliance end-uses with unsupervised machine learning, Environmental Science: Water Research and Technology, Vol. 7, No. 3, pp. 487-503DOI
17 
Huebner G. M., McMichael M., Shipworth D., Shipworth M., Durand-Daubin M., Summerfield A., 2013, Heating patterns in English homes: Comparing results from a national survey against common model assumptions, Building and Environment, Vol. 70, pp. 298-305DOI
18 
McKenna E., Thomson M., 2016, High-resolution stochastic integrated thermal–electrical domestic demand model, Applied Energy, Vol. 165, pp. 445-461DOI
19 
Zhang Y., Chen G., 2024, A Building Heat Load Prediction Method Driven by a Multi-Component Fusion LSTM Ridge Regression Ensemble Model, Applied Sciences, Vol. 14, No. 9, pp. 3810DOI
20 
Mahamoodally N., Dridi J., Amayri M., 2024, Explainable domain adaptation for imbalanced occupancy estimation, Journal of Building Engineering, Vol. 97, pp. 110613DOI
21 
Huang Z., Gou Z., 2023, Gaussian Mixture Model based pattern recognition for understanding the long-term impact of COVID-19 on energy consumption of public buildings, Journal of Building Engineering, Vol. 72, pp. 106653DOI
22 
Candanedo L. M., Feldheim V., Deramaix D., 2017, A methodology based on Hidden Markov Models for occupancy detection and a case study in a low energy residential building, Energy and Buildings, Vol. 148, pp. 327-341DOI
23 
Yoo J. H., Hwang H. J., Lee J. E., 2016, A Comprehensive Validation for Energy Performance of Houses through the Occupant Behavior 1st The Evaluation of Energy consumption and Derive of Reference housing model, Land and Housing InstituteGoogle Search
24 
Delzendeh E., Wu S., Lee A., Zhou Y., 2017, The impact of occupants’ behaviours on building energy analysis: A research review, Renewable and sustainable energy reviews, Vol. 80, pp. 1061-1071DOI
25 
Hu S., Yan D., Guo S., Cui Y., Dong B., 2017, A survey on energy consumption and energy usage behavior of households and residential building in urban China, Energy and Buildings, Vol. 148, pp. 366-378DOI