Cited by 294Building Energy and Comfort OptimizationSmart Grid Energy ManagementData Mining and Machine Learning Applications

The DOI matches its Crossref record; 31 of the 33 references it cites were found in a registry or an index.

Occupancy schedules learning process through a data mining framework

Open access

D’Oca, Simona, Hong, Tianzhen·2015·Energy and Buildings

DOI 10.1016/j.enbuild.2014.11.065

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1016/j.enbuild.2014.11.065

Cites

31 of 31 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030
A Cluster Separation Measure
Davies, D. L., & Bouldin, D. W. (1979) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1979.4766909 · Cited by 9,145
Advances in knowledge discovery and data mining
(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90007-3 · Cited by 2,996
Principles of Data Mining
Hand, D. J., Mannila, H., & Smyth, P. (2001) · Cited by 2,581
Simplifying decision trees
Quinlan, J. R. (1987) · International Journal of Man-Machine Studies · doi:10.1016/s0020-7373(87)80053-6 · Cited by 2,553
From data mining to knowledge discovery: an overview
Fayyad, U. M., Piatetsky-Shapiro, G., & Smyth, P. (1996) · Knowledge Discovery and Data Mining · Cited by 2,395
Fast discovery of association rules
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H. T. T., & Verkamo, A. I. (1996) · Knowledge Discovery and Data Mining · Cited by 2,376
Principles of Data Mining
Smith, A. E. (2002) · Artificial Intelligence in Medicine · doi:10.1016/s0933-3657(02)00058-1 · Cited by 1,711
Frequent pattern mining: current status and future directions
Han, J., Cheng, H., Xin, D., & Yan, X. (2007) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-006-0059-1 · Cited by 1,413

Show all 33 references

Research works printed but not linked: 1

RapidMiner Studio, V 5.3, Cambridge, MA 02138, USA 〈http://rapid-i.com/content/view/181/190/〉.

Sorted by citations

Items 101–200 of 294

Comparison of different occupancy counting methods for single system-single zone applications
Yang, J., Pantazaras, A., Chaturvedi, K. A., Chandran, A. K., Santamouris, M., Lee, S. E., & Tham, K. W. (2018) · Energy and Buildings · doi:10.1016/j.enbuild.2018.04.051 · Cited by 47
To what extent can agent-based modelling enhance a life cycle assessment? Answers based on a literature review
Micolier, A., Loubet, P., Taillandier, F., & Sonnemann, G. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.118123 · Cited by 55
An occupancy prediction model for campus buildings based on the diversity of occupancy patterns
Ding, Y. Y., Chen, W., Wei, S., & Yang, F. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102533 · Cited by 46
Thermal comfort, occupant control behaviour and performance gap – A study of office buildings in north-east China using data mining
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
Collecting building occupancy data of high resolution based on WiFi and BLE network
Chen, J., Chen, H., & Luo, X. (2019) · Automation in Construction · doi:10.1016/j.autcon.2019.02.016 · Cited by 44
Occupancy and occupant activity drivers of energy consumption in residential buildings
Akbari, S., & Haghighat, F. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.111303 · Cited by 44
Energy consumption of residential buildings and occupancy profiles. A case study in Mediterranean climatic conditions
Mora, D., Carpino, C., & Simone, M. D. (2017) · Energy Efficiency · doi:10.1007/s12053-017-9553-0 · Cited by 43
Case study of window operating behavior patterns in an open-plan office in the summer
Zhou, X., Liu, T., Shi, X., & Jin, X. (2018) · Energy and Buildings · doi:10.1016/j.enbuild.2018.01.037 · Cited by 43
Critical review and illustrative examples of office occupant modelling formalisms
D’Oca, S., Gunay, B., Gilani, S., & O’Brien, W. (2019) · Building Services Engineering Research and Technology · doi:10.1177/0143624419827468 · Cited by 45
Self-citation
Human-building interaction: A bibliometric review
Taherkhani, R., & Aziminezhad, M. (2023) · Building and Environment · doi:10.1016/j.buildenv.2023.110493 · Cited by 41
Prediction of window opening behavior and its impact on HVAC energy consumption at a residential dormitory using Deep Neural Network
Pandey, P., & Dong, B. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.113355 · Cited by 41
A guideline to document occupant behavior models for advanced building controls
Dong, B., Markovic, R., Carlucci, S., Liu, Y., Wagner, A., Liguori, A., van Treeck, C., Oleynikov, D., Azar, E., Fajilla, G., et al. (2022) · Building and Environment · doi:10.1016/j.buildenv.2022.109195 · Cited by 40
Comparative study of air-conditioning energy use of four office buildings in China and USA
Zhou, X., Yan, D., An, J., Hong, T., Shi, X., & Jin, X. (2018) · Energy and Buildings · doi:10.1016/j.enbuild.2018.03.073 · Cited by 40
Self-citation
Towards data-driven sustainable design: decision support based on knowledge discovery in disparate building data
Petrova, E., Pauwels, P., Svidt, K., & Jensen, R. L. (2018) · Architectural Engineering and Design Management · doi:10.1080/17452007.2018.1530092 · Cited by 40
An agent-based simulation of occupancy schedule in office buildings
Norouziasl, S., Jafari, A., & Wang, C. (2020) · Building and Environment · doi:10.1016/j.buildenv.2020.107352 · Cited by 40
Occupancy patterns obtained by heuristic approaches: Cluster analysis and logical flowcharts. A case study in a university office
Mora, D., Fajilla, G., Austin, M. C., & Simone, M. D. (2019) · Energy and Buildings · doi:10.1016/j.enbuild.2019.01.023 · Cited by 39
A statistical quantitative analysis of the correlations between socio-demographic characteristics and household occupancy patterns in residential buildings in China
Liu, X., Hu, S., & Yan, D. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.112842 · Cited by 39
Comparing occupancy models and data mining approaches for regular occupancy prediction in commercial buildings
Chen, Z., & Soh, Y. C. (2016) · Journal of Building Performance Simulation · doi:10.1080/19401493.2016.1199735 · Cited by 36
Comparison of Different Classification Algorithms for the Detection of User's Interaction with Windows in Office Buildings
Markovic, R., Wolf, S., Cao, J., Spinnräker, E. W., Wölki, D., Frisch, J., & van Treeck, C. (2017) · Energy Procedia · doi:10.1016/j.egypro.2017.07.333 · Cited by 35
In Search of Sustainable Design Patterns: Combining Data Mining and Semantic Data Modelling on Disparate Building Data
Petrova, E., Pauwels, P., Svidt, K., & Jensen, R. L. (2018) · Springer eBooks · doi:10.1007/978-3-030-00220-6_3 · Cited by 35
Data mining algorithm and framework for identifying HVAC control strategies in large commercial buildings
Chen, Z., Xu, P., Feng, F., Qiao, Y., & Luo, W. H. (2020) · Building Simulation · doi:10.1007/s12273-019-0599-0 · Cited by 35
Habitual window opening behaviour in residential buildings
Verbruggen, S., Delghust, M., Laverge, J., & Janssens, A. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.111454 · Cited by 37
A prediction model to forecast passenger flow based on flight arrangement in airport terminals
Lin, L., Liu, X., Liu, X., Zhang, T., & Cao, Y. (2022) · Energy and Built Environment · doi:10.1016/j.enbenv.2022.06.006 · Cited by 34
Anthropogenic heat reduction through retrofitting strategies of campus buildings
Yang, J., Tham, K. W., Lee, S. E., Santamouris, M., Sekhar, C., & Cheong, D. K. W. (2016) · Energy and Buildings · doi:10.1016/j.enbuild.2016.11.051 · Cited by 34
Modeling occupancy-driven building loads for large and diversified building stocks through the use of parametric schedules
Bianchi, C., Zhang, L., Goldwasser, D., Parker, A., & Horsey, R. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115470 · Cited by 34
Maintenance service optimization in smart buildings through ultrasonic sensors network
Moretti, N., Cadena, J. D. B., Mannino, A., Poli, T., & Cecconi, F. R. (2020) · Intelligent Buildings International · doi:10.1080/17508975.2020.1765723 · Cited by 33
Data mining based framework to identify rule based operation strategies for buildings with power metering system
Qiu, S., Feng, F., Li, Z., Yang, G., Xu, P., & Li, Z. (2018) · Building Simulation · doi:10.1007/s12273-018-0472-6 · Cited by 32
Operation and performance of VRF systems: Mining a large-scale dataset
Qian, M., Yan, D., Hong, T., & Liu, H. (2020) · Energy and Buildings · doi:10.1016/j.enbuild.2020.110519 · Cited by 32
Self-citation
Improving dental implant stability by optimizing thread design: Simultaneous application of finite element method and data mining approach
Arabbeiki, M., Niroomand, M. R., & Rouhi, G. (2023) · Journal of Prosthetic Dentistry · doi:10.1016/j.prosdent.2023.06.034 · Cited by 31
Optimization-informed rule extraction for HVAC system: A case study of dedicated outdoor air system control in a mixed-humid climate zone
Choi, Y., Lu, X., O’Neill, Z. D., Feng, F., & Yang, T. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.113295 · Cited by 31
Data Mining of Occupant Behavior in Office Buildings
D’Oca, S., Corgnati, S. P., & Hong, T. (2015) · Energy Procedia · doi:10.1016/j.egypro.2015.11.022 · Cited by 30
Self-citation
Software engineering for big data projects: Domains, methodologies and gaps
Kumar, V. D., & Alencar, P. (2016) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2016.7840938 · Cited by 30
A non-intrusive data-driven model for detailed occupants’ activities classification in residential buildings using environmental and energy usage data
Yoon, Y. R., Lee, Y., Kim, S. H., Kim, J. W., & Moon, H. J. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.111699 · Cited by 30
Effects of Building Occupancy on Indicators of Energy Efficiency
Huovila, A., Tuominen, P., & Airaksinen, M. (2017) · Energies · doi:10.3390/en10050628 · Cited by 30
Power consumption and energy efficiency of VRF system based on large scale monitoring virtual sensors
Qian, M., Yan, D., Liu, H., Berardi, U., & Liu, Y. (2020) · Building Simulation · doi:10.1007/s12273-020-0670-x · Cited by 29
Season-Based Occupancy Prediction in Residential Buildings Using Machine Learning Models
Yang, B., Haghighat, F., Fung, B. C. M., & Panchabikesan, K. (2021) · e-Prime - Advances in Electrical Engineering Electronics and Energy · doi:10.1016/j.prime.2021.100003 · Cited by 30
Analysis of Occupant Behaviours in Energy Efficiency Retrofitting Projects
Nia, E. M., Qian, Q. K., & Visscher, H. J. (2022) · Land · doi:10.3390/land11111944 · Cited by 33
Big data and artificial intelligence application in energy field: a bibliometric analysis
Hou, Y., & Wang, Q. (2022) · Environmental Science and Pollution Research · doi:10.1007/s11356-022-24880-y · Cited by 28
Occupancy estimation with environmental sensing via non-iterative LRF feature learning in time and frequency domains
Zhu, Q., Chen, Z., Masood, M. K., & Soh, Y. C. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.01.057 · Cited by 30
On the impacts of occupancy sensing on advanced model predictive controls in commercial buildings
Sharma, H., Bhattacharya, S., Kundu, S., & Adetola, V. (2022) · Building and Environment · doi:10.1016/j.buildenv.2022.109372 · Cited by 27
A methodology for creating building energy model occupancy schedules using personal location metadata
Parker, J., Hardy, A., Glew, D., & Gorse, C. A. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.06.014 · Cited by 27
A Future Direction of Machine Learning for Building Energy Management: Interpretable Models
Gugliermetti, L., Cumo, F., & Agostinelli, S. (2024) · Energies · doi:10.3390/en17030700 · Cited by 26
Probabilistic occupancy forecasting for risk-aware optimal ventilation through autoencoder Bayesian deep neural networks
Zhuang, C., Choudhary, R., & Mavrogianni, A. (2022) · Building and Environment · doi:10.1016/j.buildenv.2022.109207 · Cited by 26
A Framework for Privacy-Preserving Data Publishing with Enhanced Utility for Cyber-Physical Systems
Sangogboye, F. C., Jia, R., Hong, T., Spanos, C. J., & Kjærgaard, M. B. (2018) · ACM Transactions on Sensor Networks · doi:10.1145/3275520 · Cited by 25
Self-citation
Data-driven occupant-behavior analytics for residential buildings
Sun, Y., Hao, W., Chen, Y., & Liu, B. (2020) · Energy · doi:10.1016/j.energy.2020.118100 · Cited by 26
A statistically-based fault detection approach for environmental and energy management in buildings
Horrigan, M., Turner, W. J. N., & O’Donnell, J. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.11.023 · Cited by 25
Long-term monitoring data from a naturally ventilated office building
Schweiker, M., Kleber, M., & Wagner, A. (2019) · Scientific Data · doi:10.1038/s41597-019-0283-3 · Cited by 25
PAD
Jia, R., Sangogboye, F. C., Hong, T., Spanos, C. J., & Kjærgaard, M. B. (2017) · ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys) · doi:10.1145/3137133.3137140 · Cited by 23
Self-citation
Influence of occupant presence patterns on energy consumption and its relation to comfort: a case study based on sensor and crowd-sensed data
Rusek, R., Frigola, J. M. I., & Colomer, J. (2022) · Energy Sustainability and Society · doi:10.1186/s13705-022-00336-6 · Cited by 23
A data mining-based method for revealing occupant behavior patterns in using mechanical ventilation systems of Dutch dwellings
Ren, X., Zhang, C., Zhao, Y., Boxem, G., Zeiler, W., & Li, T. (2019) · Energy and Buildings · doi:10.1016/j.enbuild.2019.03.047 · Cited by 23
Energy-saving effects of yard spaces considering spatiotemporal activity patterns of rural Chinese farm households
Huang, Z., Liu, Y., Chen, P., Wang, Y., Yu, H., & He, W. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.131843 · Cited by 22
An approach for building occupancy modelling considering the urban context
Hou, H., Pawlak, J., Sivakumar, A., Howard, B., & Polak, J. W. (2020) · Building and Environment · doi:10.1016/j.buildenv.2020.107126 · Cited by 22
Occupant behavior modeling for smart buildings: A critical review of data acquisition technologies and modeling methodologies
Jia, M., & Srinivasan, R. S. (2015) · Winter Simulation Conference · doi:10.1109/wsc.2015.7408496 · Cited by 21
Multi-Sensor-Based Occupancy Prediction in a Multi-Zone Office Building with Transformer
Qaisar, I., Sun, K., Zhao, Q., Xing, T., & Yan, H. (2023) · Buildings · doi:10.3390/buildings13082002 · Cited by 23
AI application in architecture in UAE: Application of an advanced optimized shading structure as a retrofit strategy of a midrise residential building façade in downtown Abu Dhabi
Ahmad, A., Bande, L., Ahmed, W. K., Kim, Y. K., & Jha, M. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114995 · Cited by 21
Are net zero energy buildings necessarily also net zero emission buildings? Time-integrated analysis using dynamic grid emission factors
Amaripadath, D., Sailor, D. J., Bertini, A., Barker, M., & Attia, S. (2025) · Building and Environment · doi:10.1016/j.buildenv.2025.113367 · Cited by 20
Comparative review of occupant-related energy aspects of the National Building Code of Canada
Abdeen, A., O’Brien, W., Gunay, B., Newsham, G. R., & Knudsen, H. (2020) · Building and Environment · doi:10.1016/j.buildenv.2020.107136 · Cited by 20
Study workplace space occupancy: a review of measures and technologies
Zhou, Y., Hua, Y., & Liu, J. (2021) · Journal of Facilities Management · doi:10.1108/jfm-01-2021-0013 · Cited by 20
Generic visual data mining-based framework for revealing abnormal operation patterns in building energy systems
Zhang, C., Zhao, Y., Li, T., Zhang, X., & Adnouni, M. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103624 · Cited by 19
Modeling window and thermostat use behavior to inform sequences of operation in mixed-mode ventilation buildings
Liu, W., Gunay, B., & Ouf, M. M. (2021) · Science and Technology for the Built Environment · doi:10.1080/23744731.2021.1936629 · Cited by 19
A novel approach for occupants' horizontal and vertical movement modeling in non-residential buildings using Immersive Virtual Environment (IVE)
Kang, X., Wu, Y., Yan, D., Zhu, Y., Yao, Y., & Sun, H. (2022) · Sustainable Cities and Society · doi:10.1016/j.scs.2022.104193 · Cited by 20
Investigating DesignBuilder Simulation Software's Validation in Term of Heat Gain through Field Measured Data of Adjacent Rooms of Courtyard House
Tayari, N., & Nikpour, M. (2022) · Iranica Journal of Energy and Environment · doi:10.5829/ijee.2023.14.01.01 · Cited by 19
Sensitivity analysis of probabilistic occupancy prediction model using big data
Salimi, S., & Hammad, A. (2020) · Building and Environment · doi:10.1016/j.buildenv.2020.106729 · Cited by 19
A comprehensive investigation of knowledge discovered from historical operational data of a typical building energy system
Zhang, C., Zhao, Y., Li, T., Zhang, X., & Luo, J. (2021) · Journal of Building Engineering · doi:10.1016/j.jobe.2021.102502 · Cited by 19
Numerical simulation of the effect of bamboo composite building envelope on summer overheating problem
Al-Rukaibawi, L. S., Szalay, Z., & Károlyi, G. (2021) · Case Studies in Thermal Engineering · doi:10.1016/j.csite.2021.101516 · Cited by 19
Utilizing multi-connected three-fluid heat exchangers in indoor units to improve the low load-ratio performance of variable refrigerant flow systems
Zha, F., Wang, Y., Liang, C., Shi, W., Li, X., Guo, D., & Mao, S. (2025) · Journal of Building Engineering · doi:10.1016/j.jobe.2025.112150 · Cited by 18
Current Trajectories and New Challenges for Visual Comfort Assessment in Building Design and Operation: A Critical Review
Cadena, J. D. B., Poli, T., Košir, M., Lobaccaro, G., Mainini, A. G., & Speroni, A. (2022) · Applied Sciences · doi:10.3390/app12063018 · Cited by 19
Generating Occupancy Profiles for Building Simulations Using a Hybrid GNN and LSTM Framework
Xie, Y., & Stravoravdis, S. (2023) · Energies · doi:10.3390/en16124638 · Cited by 18
Discovering Knowledge from a Residential Building Stock through Data Mining Analysis for Engineering Sustainability
Capozzoli, A., Grassi, D., Piscitelli, M. S., & Serale, G. (2015) · Energy Procedia · doi:10.1016/j.egypro.2015.12.212 · Cited by 18
Investigation on spatial distributions and occupant schedules of typical residential districts in South China's Pearl River Delta
Peng, H., He, M., Li, M., Huang, Y. J., & Wen, L. (2019) · Energy and Buildings · doi:10.1016/j.enbuild.2019.109710 · Cited by 17
Data mining for energy analysis of a large data set of flats
Capozzoli, A., Serale, G., Piscitelli, M. S., & Grassi, D. (2016) · Proceedings of the Institution of Civil Engineers - Engineering Sustainability · doi:10.1680/jensu.15.00051 · Cited by 17
Profiling Occupancy Patterns to Calibrate Urban Building Energy Models (UBEMs) Using Measured Data Clustering
Kontar, R. E., & Rakha, T. (2018) · Technology|Architecture + Design · doi:10.1080/24751448.2018.1497369 · Cited by 17
Development of a systematic procedure to establish customized shading behavior identification model
Li, Z., Zhu, H., Dong, B., & Xu, X. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.110793 · Cited by 17
Modelling method of inter-building movement for campus-scale occupancy simulation: A case study
Zhu, M., Pan, Y., Wu, Z., Huang, Z., & Kosonen, R. (2022) · Building Simulation · doi:10.1007/s12273-022-0948-2 · Cited by 16
Documenting occupant models for building performance simulation: a state-of-the-art
Vellei, M., Azar, E., Bandurski, K., Berger, C., Carlucci, S., Dong, B., Favero, M., Mahdavi, A., & Schweiker, M. (2022) · Journal of Building Performance Simulation · doi:10.1080/19401493.2022.2061050 · Cited by 15
A Cognitive Social IoT Approach for Smart Energy Management in a Real Environment
Marche, C., Soma, G. G., & Nitti, M. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3255409 · Cited by 17
Enhancing energy efficiency in buildings through innovative data analytics technologiesa
Capozzoli, A., Cerquitelli, T., & Piscitelli, M. S. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-803663-1.00011-5 · Cited by 15
Data-driven Occupancy Profiles for Apartment-style Student Housing
Nikdel, L., Schay, A. E. S., Hou, D., & Powers, S. E. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.111070 · Cited by 15
Occupancy-aware energy optimization in building-to-grid systems using deep neural networks and model predictive control
Babosalam, S., Kargar, S. M., Moazzami, M., & Zanjani, S. M. (2025) · Journal of Building Engineering · doi:10.1016/j.jobe.2025.113714 · Cited by 14
Climate change induced heat stress impact on workplace productivity in a net zero-carbon timber building towards the end of the century
Amaripadath, D., Santamouris, M., & Attia, S. (2024) · Building Simulation · doi:10.1007/s12273-024-1116-7 · Cited by 14
Temporal and spatial variation in the predictability of building occupancy
Ahn, K. U., & Park, C. S. (2018) · Building and Environment · doi:10.1016/j.buildenv.2018.12.043 · Cited by 13
Relationship Between Building Technologies, Energy Performance and Occupancy in Domestic Buildings
Guerra-Santin, O. (2016) · Springer eBooks · doi:10.1007/978-3-319-33527-8_26 · Cited by 14
Energy simulation on how to go green buildings in an earth's dry climate
Noohian, M., & Mahmoudi, J. (2023) · International Journal of Thermofluids · doi:10.1016/j.ijft.2023.100405 · Cited by 13
A framework for short-term energy consumption prediction based on room air conditioner group characteristics
Xue, K., Liu, M., Ma, M., Hu, M., Yan, L. H., Chen, X., & Zeng, W. (2022) · Journal of Building Engineering · doi:10.1016/j.jobe.2022.104400 · Cited by 13
Mining operation hours on time-series energy data to identify unnecessary building energy consumption
Tian, Z., Zhang, X., Shi, X., & Han, Y. (2022) · Journal of Building Engineering · doi:10.1016/j.jobe.2022.105509 · Cited by 13
Typical daily occupancy profiles of express hotels and its stochasticity effect on building heating and cooling loads
Chen, S., Lv, Y., Wang, Z., Ma, Y., Huang, Y., Wang, Y., Cai, Y., & Rao, Z. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2023.106775 · Cited by 12
Solar radiation control passive strategy for reduction of heating and cooling energy use in arid climate: Case of Afghanistan
Ahady, S., Dev, N., & Mandal, A. (2021) · Indoor and Built Environment · doi:10.1177/1420326x211050114 · Cited by 12
Building occupancy modelling at the district level: A combined copula-nested hazard-based approach
Hou, H., Pawlak, J., Sivakumar, A., & Howard, B. (2022) · Building and Environment · doi:10.1016/j.buildenv.2022.109661 · Cited by 12
Influencing factors in energy use of housing blocks: a new methodology, based on clustering and energy simulations, for decision making in energy refurbishment projects
Cipriano, X., Vellido, A., Cipriano, J., Martí-Herrero, J., & Danov, S. (2016) · Energy Efficiency · doi:10.1007/s12053-016-9460-9 · Cited by 12
An overview on cloud computing platform spark for Human Genome mining
Ding, D., Wu, D., & Yu, F. (2016) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma.2016.7558977 · Cited by 12
Energy modeling and data structure framework for Sustainable Human-Building Ecosystems (SHBE) — a review
Talele, S., Traylor, C., Arpan, L. M., Curley, C., Chen, C., Day, J. K., Feiock, R. C., Hadžikadić, M., Tolone, W. J., Ingman, S., et al. (2018) · Frontiers in Energy · doi:10.1007/s11708-017-0530-2 · Cited by 12
Office Building Tenants’ Electricity Use Model for Building Performance Simulations
Ferrantelli, A., Kuivjõgi, H., Kurnitski, J., & Thalfeldt, M. (2020) · Energies · doi:10.3390/en13215541 · Cited by 11
Embedding energy optimization in organizations: A case study of a Swiss decentralized renewable energy system
Meuer, J., Lamaro, F., & Vetterli, N. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2020.110710 · Cited by 12
Variation in residential occupancy profiles in the United States by household income level and characteristics
Mitra, D., Chu, Y., Cetin, K. S., Wang, Y., & Chen, C. (2021) · Journal of Building Performance Simulation · doi:10.1080/19401493.2021.2001572 · Cited by 12
Trend on the Implementation of Analytical Techniques for Big Data in Construction Research (2000–2014)
Omran, B. A., & Chen, Q. (2016) · ASCE Construction Research Congress (CRC) · doi:10.1061/9780784479827.100 · Cited by 12
SmartSPEC: A framework to generate customizable, semantics-based smart space datasets
Chio, A., Jiang, D., Gupta, P., Bouloukakis, G., Yus, R., Mehrotra, S., & Venkatasubramanian, N. (2023) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2023.101809 · Cited by 11
A visitors’ presence model for a museum environment: Description and validation
Schito, E., & Testi, D. (2017) · Building Simulation · doi:10.1007/s12273-017-0372-1 · Cited by 11
Using pattern recognition to characterise heating behaviour in residential buildings
Laskari, M., Karatasou, S., Santamouris, M., & Assimakopoulos, M.-N. (2020) · Advances in Building Energy Research · doi:10.1080/17512549.2020.1863858 · Cited by 11
A study on the transferability of computational models of building electricity load patterns across climatic zones
Ward, R. M., Wong, C. S. Y., Chong, A., Choudhary, R., & Ramasamy, S. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.110826 · Cited by 11
Multifactorial approach to indoor environmental quality perception of social housing residents in Northern Spain
Perez-Bezos, S., Grijalba, O., & Hernandez-Minguillon, R. J. (2022) · Building Research & Information · doi:10.1080/09613218.2022.2130739 · Cited by 10

Authors: D’Oca, S., & Hong, T.
Affiliations: Lawrence Berkeley National Laboratory (US)
Corresponding author: Hong, Tianzhen
ORCID: D’Oca, Simona
Hong, Tianzhen
Venue: Energy and Buildings
Publisher: Elsevier BV
Published: 2015-02
Volume: 88
Pages: 395-408
ISSN: 0378-7788 (ISSN-L)
DOI: 10.1016/j.enbuild.2014.11.065
OpenAlex: W1981885501
Cited by: 294
References: 33
Citations by year: 2026: 12 · 2025: 17 · 2024: 17 · 2023: 26 · 2022: 27 · 2021: 33 · 2020: 32 · 2019: 25 · 2018: 36 · 2017: 31 · 2016: 26 · 2015: 12
FWCI: 31.81
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (am, 439 d)
Locations: Energy and Buildings (acceptedVersion)
OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
eScholarship (California Digital Library) (submittedVersion)
Funders: U.S. Department of Energy (DE-AC02-05CH11231)
U.S.-China Clean Energy Research Center
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 › Computer Science › Information Systems › Data Mining and Machine Learning Applications
Keywords: building occupancy, occupancy schedules, office buildings, occupancy patterns, building energy simulation, decision tree, rule induction, clustering, occupant behavior, human-building interaction

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Occupancy schedules learning process through a data mining framework

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks