{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T02:20:36Z","timestamp":1780453236464,"version":"3.54.1"},"reference-count":41,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,1,9]],"date-time":"2020-01-09T00:00:00Z","timestamp":1578528000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61573225"],"award-info":[{"award-number":["61573225"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61903226"],"award-info":[{"award-number":["61903226"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010040","name":"Taishan Scholar Project of Shandong Province","doi-asserted-by":"publisher","award":["TSQN201812092"],"award-info":[{"award-number":["TSQN201812092"]}],"id":[{"id":"10.13039\/501100010040","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014103","name":"Key Technology Research and Development Program of Shandong","doi-asserted-by":"publisher","award":["2019GGX101072"],"award-info":[{"award-number":["2019GGX101072"]}],"id":[{"id":"10.13039\/100014103","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Prediction of energy use behaviors is a necessary prerequisite for designing personalized and scalable energy efficiency programs. The energy use behaviors of office occupants are different from those of residential occupants and have not yet been studied as intensively as residential occupants. This paper proposes a method based on Markov chain Monte Carlo (MCMC) to predict the energy use behaviors of office occupants. Firstly, an indoor electrical Internet of Things system (IEIoTS) for the office scenario is developed to collect the switching state time series data of selected user electrical equipment (desktop computer, water dispenser, light) and the historical environment parameters. Then, the Metropolis\u2013Hastings (MH) algorithm is used to sample and obtain the optimal solution of the parameters for the office occupants\u2019 behavior function, the model of which includes the energy action model, energy working hours model, and air-conditioner energy use behavior model. Finally, comparative experiments are carried out to evaluate the performance of the proposed method. The experimental results show that while the mean value performs similarly in estimating the energy use model, the proposed method outperforms the Maximum Likelihood Estimation (MLE) method on uncertainty quantification with relatively narrower confidence intervals.<\/jats:p>","DOI":"10.3390\/a13010021","type":"journal-article","created":{"date-parts":[[2020,1,10]],"date-time":"2020-01-10T04:06:51Z","timestamp":1578629211000},"page":"21","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Markov Chain Monte Carlo Based Energy Use Behaviors Prediction of Office Occupants"],"prefix":"10.3390","volume":"13","author":[{"given":"Qiao","family":"Yan","sequence":"first","affiliation":[{"name":"School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China"},{"name":"Shandong Provincial Key Laboratory of Intelligent Buildings Technology, Jinan 250101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoqian","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China"},{"name":"Shandong Provincial Key Laboratory of Intelligent Buildings Technology, Jinan 250101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2233-2113","authenticated-orcid":false,"given":"Xiaoping","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China"},{"name":"Shandong Provincial Key Laboratory of Intelligent Buildings Technology, Jinan 250101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China"},{"name":"Shandong Provincial Key Laboratory of Intelligent Buildings Technology, Jinan 250101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guiqing","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China"},{"name":"Shandong Provincial Key Laboratory of Intelligent Buildings Technology, Jinan 250101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.enbuild.2019.01.034","article-title":"Data driven parallel prediction of building energy consumption using generative adversarial nets","volume":"186","author":"Tian","year":"2019","journal-title":"Energy Build."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1007\/s12667-016-0203-y","article-title":"An overview of energy demand forecasting methods published in 2005-2015","volume":"8","author":"Ghalehkhondabi","year":"2017","journal-title":"Energy Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.buildenv.2014.06.022","article-title":"Air-conditioning usage conditional probability model for residential buildings","volume":"81","author":"Ren","year":"2014","journal-title":"Build. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"10996","DOI":"10.3390\/en81010996","article-title":"A review of approaches for sensing, understanding, and improving occupancy-related energy-use behaviors in commercial buildings","volume":"8","author":"Rafsanjani","year":"2015","journal-title":"Energies"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Muncaster, J., and Ma, Y. (2007, January 23\u201324). Activity recognition using dynamic Bayesian networks with automatic state selection. Proceedings of the 2007 IEEE Workshop on Motion and Video Computing (WMVC\u201907), Austin, TX, USA.","DOI":"10.1109\/WMVC.2007.5"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1109\/TITB.2011.2113352","article-title":"Detection of abnormal living patterns for elderly living alone using support vector data description","volume":"15","author":"Shin","year":"2011","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1109\/JBHI.2012.2235075","article-title":"Child activity recognition based on cooperative fusion model of a triaxial accelerometer and a barometric pressure sensor","volume":"17","author":"Nam","year":"2013","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.phycom.2012.09.004","article-title":"Hf spectrum activity prediction model based on hmm for cognitive radio applications","volume":"9","author":"Zazo","year":"2013","journal-title":"Phys. Commun."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Singh, D., Merdivan, E., Hanke, S., Kropf, J., Geist, M., and Holzinger, A. (2017). Convolutional and recurrent neural networks for activity recognition in smart environment. Towards Integrative Machine Learning and Knowledge Extraction, Springer.","DOI":"10.1007\/978-3-319-69775-8_12"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2312","DOI":"10.1109\/TII.2016.2590339","article-title":"Fall recovery subactivity recognition with rgb-d cameras","volume":"12","author":"Withanage","year":"2016","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zou, H., Chen, Z., Jiang, H., Xie, L., and Spanos, C. (2017, January 27\u201330). Accurate indoor localization and tracking using mobile phone inertial sensors, wifi and ibeacon. Proceedings of the 2017 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL), Kauai, HI, USA.","DOI":"10.1109\/ISISS.2017.7935650"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Huang, B., Qi, G., Yang, X., Zhao, L., and Zou, H. (2016, January 12\u201316). Exploiting cyclic features of walking for pedestrian dead reckoning with unconstrained smartphones. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany.","DOI":"10.1145\/2971648.2971742"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"van Kasteren, T., Noulas, A., Englebienne, G., and Kr\u00f6se, B. (2008, January 21\u201324). Accurate activity recognition in a home setting. Proceedings of the 10th International Conference On Ubiquitous Computing, Seoul, Korea.","DOI":"10.1145\/1409635.1409637"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1007\/s12652-009-0007-1","article-title":"Recognizing independent and joint activities among multiple residents in smart environments","volume":"1","author":"Singla","year":"2010","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"van Kasteren, T.L., Englebienne, G., and Krose, B.J. (2011, January 16\u201318). Hierarchical activity recognition using automatically clustered actions. Proceedings of the International Joint Conference on Ambient Intelligence, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-642-25167-2_9"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Vail, D.L., Veloso, M.M., and Lafferty, J.D. (2007, January 14\u201318). Conditional random fields for activity recognition. Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems, Honolulu, HI, USA.","DOI":"10.1145\/1329125.1329409"},{"key":"ref_17","unstructured":"Wu, T.-Y., Lian, C.-C., and Hsu, J.Y.-J. (2007, January 22\u201326). Joint recognition of multiple concurrent activities using factorial conditional random fields. Proceedings of the 22nd Conference on Artificial Intelligence (AAAI-2007), Vancouver, BC, Canada."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2184","DOI":"10.3390\/e16042184","article-title":"A two-stage method for solving multi-resident activity recognition in smart environments","volume":"16","author":"Chen","year":"2014","journal-title":"Entropy"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.enbuild.2014.09.071","article-title":"Data analysis and stochastic modeling of lighting energy use in large office buildings in china","volume":"86","author":"Zhou","year":"2015","journal-title":"Energy Build."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1016\/j.enbuild.2010.01.014","article-title":"Behavioural adaptation and the use of environmental controls in summer for thermal comfort in apartments in india","volume":"42","author":"Indraganti","year":"2010","journal-title":"Energy Build."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.1016\/j.renene.2010.10.002","article-title":"Thermal comfort in apartments in india: Adaptive use of environmental controls and hindrances","volume":"36","author":"Indraganti","year":"2011","journal-title":"Renew. Energy"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1016\/j.egypro.2016.06.128","article-title":"Modeling individual\u2019s light switching behavior to understand lighting energy use of office building","volume":"88","author":"Wang","year":"2016","journal-title":"Energy Procedia"},{"key":"ref_23","first-page":"620","article-title":"Building air conditioning model using the room-specific thermal inertia and its implementation as a controller","volume":"134","author":"Kumagai","year":"2014","journal-title":"IEEJ Trans. Electron. Inf. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/S0378-7788(96)01019-5","article-title":"Thermal and behavioral modeling of occupant-controlled heating, ventilating and air conditioning systems","volume":"25","author":"Glicksman","year":"1997","journal-title":"Energy Build."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v033.i02","article-title":"Mcmc methods for multi-response generalized linear mixed models: the mcmcglmm r package","volume":"33","author":"Hadfield","year":"2010","journal-title":"J. Stat. Softw."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Robert, C.P., Casella, G., and Casella, G. (2010). Introducing Monte Carlo Methods with R, Springer.","DOI":"10.1007\/978-1-4419-1576-4"},{"key":"ref_27","unstructured":"Li, C.-Y., and Ji, H.-B. (2007, January 2\u20134). A new particle filter with ga-mcmc resampling. Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition, Beijing, China."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Mingas, G., Rahman, F., and Bouganis, C.-S. (2013, January 28\u201330). On optimizing the arithmetic precision of mcmc algorithms. Proceedings of the 2013 IEEE 21st Annual International Symposium on Field-Programmable Custom Computing Machines, Seattle, WA, USA.","DOI":"10.1109\/FCCM.2013.31"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1020281327116","article-title":"An introduction to mcmc for machine learning","volume":"50","author":"Andrieu","year":"2003","journal-title":"Mach. Learn."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/S0167-7152(03)00126-3","article-title":"Testing lumpability in Markov chains","volume":"64","author":"Jernigan","year":"2003","journal-title":"Stat. Probab. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1109\/TSP.2004.838934","article-title":"Wideband array signal processing using mcmc methods","volume":"53","author":"Ng","year":"2005","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_32","unstructured":"Dwivedi, R., Chen, Y., Wainwright, M.J., and Yu, B. (2018). Log-concave sampling: Metropolis-hastings algorithms are fast!. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1093\/biomet\/88.3.603","article-title":"On inference for partially observed nonlinear diffusion models using the metropolis\u2013hastings algorithm","volume":"88","author":"Roberts","year":"2001","journal-title":"Biometrika"},{"key":"ref_34","first-page":"100","article-title":"Metropolis-hastings adaptive algorithm and its application","volume":"1","author":"Chen","year":"2008","journal-title":"Syst. Eng. Theory Pract."},{"key":"ref_35","first-page":"1","article-title":"Bayesian statistics and mcmc method-matlab programming for metropolis-hastings (mh) algorithm","volume":"1","author":"Chen","year":"2018","journal-title":"J. East China Jiaotong Univ."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Fries, M., Baum, A., Wittmann, M., and Lienkamp, M. (2018, January 4\u20137). Derivation of a real-life driving cycle from fleet testing data with the Markov-chain-monte-carlo method. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569547"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"103269","DOI":"10.1016\/j.engappai.2019.103269","article-title":"Interval type-2 fuzzy logic based transmission power allocation strategy for lifetime maximization of wsns","volume":"87","author":"Peng","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"17765","DOI":"10.1007\/s11042-015-3053-z","article-title":"A Bayesian approach for sleep and wake classification based on dynamic time warping method","volume":"76","author":"Fu","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.ijengsci.2015.10.013","article-title":"Bending of euler\u2013bernoulli beams using eringen\u2019s integral formulation: a paradox resolved","volume":"99","author":"Zaera","year":"2016","journal-title":"Int. J. Eng. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.1016\/j.celrep.2013.07.039","article-title":"Preconfigured, skewed distribution of firing rates in the hippocampus and entorhinal cortex","volume":"4","author":"Mizuseki","year":"2013","journal-title":"Cell Rep."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Schneck, A., Kalle, S., Pryss, R., Schlee, W., Probst, T., Langguth, B., Landgrebe, M., Reichert, M., and Spiliopoulou, M. (2017, January 22\u201324). Studying the potential of multi-target classification to characterize combinations of classes with skewed distribution. Proceedings of the 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), Thessaloniki, Greece.","DOI":"10.1109\/CBMS.2017.136"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/13\/1\/21\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:42:35Z","timestamp":1760362955000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/13\/1\/21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,9]]},"references-count":41,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["a13010021"],"URL":"https:\/\/doi.org\/10.3390\/a13010021","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,9]]}}}