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Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining
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“Moreover, occupancy detection itself represents an opportunity for ML algorithms, ranging from decision trees [193,882] to deep neural networks [896] that take input from occupancy sensors [193], WiFi signals [896,897], or appliance power consumption data [882].”
From Tackling climate change with machine learning (2023) · cites this paper as [882]
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Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,543
(1992) · Cited by 23,431
A training algorithm for optimal margin classifiers
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992) · Proceedings of the fifth annual workshop on Computational learning theory · doi:10.1145/130385.130401 · Cited by 11,992
Robust Locally Weighted Regression and Smoothing Scatterplots
Cleveland, W. S. (1979) · Journal of the American Statistical Association · doi:10.1080/01621459.1979.10481038 · Cited by 11,594
Support Vector Regression Machines
Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A. J., & Vapnik, V. N. (1996) · Cited by 4,666
Supervised Machine Learning: A Review of Classification Techniques
Kotsiantis, S. B. (2007) · Informatica · Cited by 4,192
Survey of Wireless Indoor Positioning Techniques and Systems
Liu, H., Darabi, H., Banerjee, P. P., & Liu, J. (2007) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2007.905750 · Cited by 4,184
(1998) · Cited by 2,679
Cited by
Sorted by citations
The impact of occupants’ behaviours on building energy analysis: A research review
Delzendeh, E., Wu, S., Lee, A. H.-C., & Zhou, Y. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.05.264 · Cited by 593
Advances in research and applications of energy-related occupant behavior in buildings
Hong, T., Taylor-Lange, S. C., D’Oca, S., Yan, D., & Corgnati, S. P. (2015) · Energy and Buildings · doi:10.1016/j.enbuild.2015.11.052 · Cited by 543
Towards sustainable, energy-efficient and healthy ventilation strategies in buildings: A review
Chenari, B., Carrilho, J. D., & da Silva, M. G. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.01.074 · Cited by 517
IEA EBC Annex 66: Definition and simulation of occupant behavior in buildings
Yan, D., Hong, T., Dong, B., Mahdavi, A., D’Oca, S., Gaetani, I., & Feng, X. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.09.084 · Cited by 509
Rethinking the role of occupant behavior in building energy performance: A review
Zhang, Y., Bai, X., Mills, F. P., & Pezzey, J. C. V. (2018) · Energy and Buildings · doi:10.1016/j.enbuild.2018.05.017 · Cited by 478
The human dimensions of energy use in buildings: A review
D’Oca, S., Hong, T., & Langevin, J. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.08.019 · Cited by 394
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336
Hong, T., D’Oca, S., Turner, W. J. N., & Taylor-Lange, S. C. (2015) · Building and Environment · doi:10.1016/j.buildenv.2015.02.019 · Cited by 324
Jia, M., Srinivasan, R. S., & Raheem, A. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.10.011 · Cited by 276
Artificial intelligence in green building
Debrah, C., Chan, A. P. C., & Darko, A. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104192 · Cited by 278
Park, J. Y., Dougherty, T., Fritz, H., & Nagy, Z. (2019) · Building and Environment · doi:10.1016/j.buildenv.2018.10.028 · Cited by 238
Deng, H., Fannon, D., & Eckelman, M. J. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.12.031 · Cited by 235
Yang, J., Santamouris, M., & Lee, S. E. (2015) · Energy and Buildings · doi:10.1016/j.enbuild.2015.12.019 · Cited by 226
Ma, N., Aviv, D., Guo, H., & Braham, W. W. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110436 · Cited by 235
The impacts of occupant behavior on building energy consumption: A review
Chen, S., Zhang, G., Xia, X., Chen, Y., Setunge, S., & Shi, L. X. (2021) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2021.101212 · Cited by 208
Ryu, S., & Moon, H. J. (2016) · Building and Environment · doi:10.1016/j.buildenv.2016.06.039 · Cited by 199
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Yu, Z. J., Haghighat, F., & Fung, B. C. M. (2015) · Sustainable Cities and Society · doi:10.1016/j.scs.2015.12.001 · Cited by 121
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Ding, C., & Lam, K. P. (2019) · Building and Environment · doi:10.1016/j.buildenv.2019.106394 · Cited by 110
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Uddin, M. N., Wei, H.-H., Chi, H.-L., & Ni, M. (2021) · Buildings · doi:10.3390/buildings11020041 · Cited by 93
Zhou, C., Wang, Z., Wang, X., Guo, R., Zhang, Z., Xiang, X., & Wu, Y. (2024) · Building and Environment · doi:10.1016/j.buildenv.2024.112035 · Cited by 95
Salcido, J. C., Raheem, A., & Issa, R. R. A. (2016) · Energy and Buildings · doi:10.1016/j.enbuild.2016.06.054 · Cited by 88
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Rafsanjani, H. N., Ahn, C. R., & Alahmad, M. A. (2015) · Energies · doi:10.3390/en81010996 · Cited by 82
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Pan, S., Wang, X., Wei, Y., Zhang, X., Gál, C., Ren, G., Yan, D., Shi, Y., Wu, J., Xia, L., et al. (2017) · Building Simulation · doi:10.1007/s12273-017-0377-9 · Cited by 73
Tejedor, B., Casals, M., Gangolells, M., Macarulla, M., & Forcada, N. (2020) · Building and Environment · doi:10.1016/j.buildenv.2020.107354 · Cited by 76
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Anand, P., Deb, C., Yan, K., Yang, J., Cheong, D. K. W., & Sekhar, C. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.111478 · Cited by 76
Zikos, S., Tsolakis, A. C., Meskos, D., Tryferidis, A., & Tzovaras, D. K. (2016) · Automation in Construction · doi:10.1016/j.autcon.2016.05.005 · Cited by 71
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Sandels, C., Widén, J., Nordström, L., & Andersson, E. (2015) · Energy and Buildings · doi:10.1016/j.enbuild.2015.08.052 · Cited by 65
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Jia, M., Srinivasan, R. S., Ries, R. J., Weyer, N., & Bharathy, G. K. (2019) · Energy and Buildings · doi:10.1016/j.enbuild.2019.07.009 · Cited by 60
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Wei, Y., Yu, H., Pan, S., Xia, L., Xie, J., Wang, X., Wu, J., Zhang, W., & Li, Q. (2019) · Building and Environment · doi:10.1016/j.buildenv.2019.04.040 · Cited by 59
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Lazzari, F., Mor, G., Cipriano, J., Gabaldon, E., Grillone, B., Chemisana, D., & Solsona, F. (2022) · Energy Reports · doi:10.1016/j.egyr.2022.02.260 · Cited by 53
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Luo, Z., Peng, J., & Yin, R. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121348 · Cited by 56
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Sonta, A. J., Dougherty, T. R., & Jain, R. K. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.110815 · Cited by 58
Panchabikesan, K., Haghighat, F., & Mankibi, M. E. (2020) · Energy · doi:10.1016/j.energy.2020.119539 · Cited by 56
Estimation models of heating energy consumption in schools for local authorities planning
Capozzoli, A., Grassi, D., & Causone, F. (2015) · Energy and Buildings · doi:10.1016/j.enbuild.2015.07.024 · Cited by 55
Tang, R., Wang, S., & Sun, S. (2020) · Building Simulation · doi:10.1007/s12273-020-0605-6 · Cited by 54
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Yılmaz, S., Firth, S. K., & Allinson, D. (2017) · Journal of Building Performance Simulation · doi:10.1080/19401493.2017.1287775 · Cited by 52
Xu, X., Xiao, B., & Li, C. Z. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.124955 · Cited by 52
Modelling occupant behaviour in residential buildings: A systematic literature review
Mylonas, A., Tsangrassoulis, A., & Pascual, J. (2024) · Building and Environment · doi:10.1016/j.buildenv.2024.111959 · Cited by 51
In-use monitoring of buildings: An overview and classification of evaluation methods
Guerra-Santin, O., & Christopher, T. A. (2014) · Energy and Buildings · doi:10.1016/j.enbuild.2014.10.005 · Cited by 51
Cooling load disaggregation using a NILM method based on random forest for smart buildings
Xiao, Z., Gang, W., Yuan, J., Zhang, Y. W., & Fan, C. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103202 · Cited by 50
Martins, N. R., & da Graça, G. C. (2017) · Building and Environment · doi:10.1016/j.buildenv.2017.01.030 · Cited by 48
Joe, J., Karava, P., Hou, X., Xiao, Y., & Hu, J. (2018) · Energy and Buildings · doi:10.1016/j.enbuild.2018.06.068 · Cited by 48
Khan, D. S., Kolařík, J., & Weitzmann, P. (2020) · Building and Environment · doi:10.1016/j.buildenv.2020.107192 · Cited by 48
Newsham, G. R., Xue, H., Arsenault, C. D., Valdés, J. J., Burns, G. J., Scarlett, E., Kruithof, S. G., & Shen, W. (2016) · Energy and Buildings · doi:10.1016/j.enbuild.2016.11.029 · Cited by 47
Suitability analysis of modeling and assessment approaches in energy efficiency in buildings
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Sun, C. T., Zhang, R., Sharples, S., Han, Y., & Zhang, H. (2018) · Building and Environment · doi:10.1016/j.buildenv.2018.12.036 · Cited by 43
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Lou, Y., Yang, Y., Ye, Y., Zuo, W., & Wang, J. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.111514 · Cited by 44
Metadata
- Authors
- Zhao, J., Lasternas, B., Lam, K. P., Yun, R., & Loftness, V.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- Zhao, Jie
- Venue
- Energy and Buildings
- Publisher
- Elsevier BV
- Published
- 2014-10
- Volume
- 82
- Pages
- 341-355
- ISSN
- 0378-7788 (ISSN-L)
- OpenAlex
- W2071158874
- Cited by
- 293
- References
- 66
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 29 · 2023: 13 · 2022: 26 · 2021: 42 · 2020: 33 · 2019: 27 · 2018: 30 · 2017: 32 · 2016: 24 · 2015: 20 · 2014: 1
- FWCI
- 38.03
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Energy and Buildings (publishedVersion)
- KiltHub Repository (submittedVersion)
- Funders
- National Science Foundation (NSF)
- Efficient Buildings Hub Consortium
- Topics
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Building and Construction › Building energy efficiency and sustainability
- Keywords
- occupant behavior, data mining, building energy simulation, HVAC energy consumption, indoor environment, sustainable building design, EnergyPlus, climate zone effects
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