Review on occupancy detection and prediction in building simulation
暂无分享,去创建一个
Wanyue Chen | Yan Ding | Shuxue Han | Zhe Tian | Jian Yao | Qiang Zhang | Yan Ding | Jian Yao | Qiang Zhang | Wanyue Chen | Zhe-Tong Tian | Shuxue Han | Qiang Zhang
[1] Afrooz Ebadat,et al. Regularized Deconvolution-Based Approaches for Estimating Room Occupancies , 2015, IEEE Transactions on Automation Science and Engineering.
[2] Jin Wen,et al. Simulating the human-building interaction: Development and validation of an agent-based model of office occupant behaviors , 2015 .
[3] Rui Zhang,et al. Information-theoretic environment features selection for occupancy detection in open office spaces , 2012 .
[4] Tina Yu,et al. Modeling Occupancy Behavior for Energy Efficiency and Occupants Comfort Management in Intelligent Buildings , 2010, 2010 Ninth International Conference on Machine Learning and Applications.
[5] Tianzhen Hong,et al. Advances in research and applications of energy-related occupant behavior in buildings ☆ , 2016 .
[6] Linda Steg,et al. Energy saving and energy efficiency concepts for policy making , 2009 .
[7] Karl Henrik Johansson,et al. Multi-room occupancy estimation through adaptive gray-box models , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).
[8] Hua Li,et al. Indoor occupancy estimation from carbon dioxide concentration , 2016, ArXiv.
[9] Ya Wang,et al. Unobtrusive Sensor-Based Occupancy Facing Direction Detection and Tracking Using Advanced Machine Learning Algorithms , 2018, IEEE Sensors Journal.
[10] Kamin Whitehouse,et al. The smart thermostat: using occupancy sensors to save energy in homes , 2010, SenSys '10.
[11] Junqi Wang,et al. Wi-Fi based occupancy detection in a complex indoor space under discontinuous wireless communication: A robust filtering based on event-triggered updating , 2019, Building and Environment.
[12] H. Nishi,et al. Estimation of the number of people under controlled ventilation using a CO2 concentration sensor , 2012, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society.
[13] Donatella Sciuto,et al. BlueSentinel: a first approach using iBeacon for an energy efficient occupancy detection system , 2014, BuildSys@SenSys.
[14] Luca P. Carloni,et al. An experimental investigation of occupancy-based energy-efficient control of commercial building indoor climate , 2014, 53rd IEEE Conference on Decision and Control.
[15] Neil Brown,et al. A design model for building occupancy detection using sensor fusion , 2012, 2012 6th IEEE International Conference on Digital Ecosystems and Technologies (DEST).
[16] P Pieter-Jan Hoes,et al. Occupant behavior in building energy simulation: towards a fit-for-purpose modeling strategy , 2016 .
[17] Yunhao Liu,et al. LANDMARC: Indoor Location Sensing Using Active RFID , 2004, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..
[18] Donatella Sciuto,et al. Occupancy detection via iBeacon on Android devices for smart building management , 2015, 2015 Design, Automation & Test in Europe Conference & Exhibition (DATE).
[19] Gregor P. Henze,et al. The performance of occupancy-based lighting control systems: A review , 2010 .
[20] Yeng Chai Soh,et al. Modeling building occupancy using a novel inhomogeneous Markov chain approach , 2014, 2014 IEEE International Conference on Automation Science and Engineering (CASE).
[21] Tianzhen Hong,et al. Occupancy prediction through machine learning and data fusion of environmental sensing and Wi-Fi sensing in buildings , 2018, Automation in Construction.
[22] Ian Richardson,et al. A high-resolution domestic building occupancy model for energy demand simulations , 2008 .
[23] Darren Robinson,et al. On the behaviour and adaptation of office occupants , 2008 .
[24] Benjamin C. M. Fung,et al. A novel methodology for knowledge discovery through mining associations between building operational data , 2012 .
[25] Saandeep Depatla,et al. Occupancy Estimation Using Only WiFi Power Measurements , 2015, IEEE Journal on Selected Areas in Communications.
[26] Francis Rubinstein,et al. Modeling occupancy in single person offices , 2005 .
[27] Pengcheng Liu,et al. Occupancy Inference Using Pyroelectric Infrared Sensors Through Hidden Markov Models , 2016, IEEE Sensors Journal.
[28] Benjamin C. M. Fung,et al. A systematic procedure to study the influence of occupant behavior on building energy consumption , 2011 .
[29] Bauke de Vries,et al. Methods for the prediction of intermediate activities by office occupants , 2010 .
[30] Tianzhen Hong,et al. An inverse approach to solving zone air infiltration rate and people count using indoor environmental sensor data , 2019, Energy and Buildings.
[31] Zhenghua Chen,et al. Occupancy estimation with environmental sensing via non-iterative LRF feature learning in time and frequency domains , 2017 .
[32] Gregor P. Henze,et al. Building occupancy detection through sensor belief networks , 2006 .
[33] Donal P. Finn,et al. A high-temporal resolution residential building occupancy model to generate high-temporal resolution heating load profiles of occupancy-integrated archetypes , 2020 .
[34] Kathleen M. Carley,et al. A high spatial resolution residential energy model based on American Time Use Survey data and the bootstrap sampling method , 2011 .
[35] Abbas Javed,et al. Design and Implementation of a Cloud Enabled Random Neural Network-Based Decentralized Smart Controller With Intelligent Sensor Nodes for HVAC , 2017, IEEE Internet of Things Journal.
[36] Athanasios Tsanas,et al. Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools , 2012 .
[37] Oliver Amft,et al. A Distributed PIR-based Approach for Estimating People Count in Office Environments , 2012, 2012 IEEE 15th International Conference on Computational Science and Engineering.
[38] Alberto E. Cerpa,et al. Energy efficient building environment control strategies using real-time occupancy measurements , 2009, BuildSys '09.
[39] Haili Liu,et al. Turning a pyroelectric infrared motion sensor into a high-accuracy presence detector by using a narrow semi-transparent chopper , 2017 .
[40] Tianzhen Hong,et al. An ontology to represent energy-related occupant behavior in buildings. Part I: Introduction to the DNAs framework , 2015 .
[41] Rhys Goldstein,et al. Real-time occupancy detection using decision trees with multiple sensor types , 2011, SpringSim.
[42] Yi Jiang,et al. A novel approach for building occupancy simulation , 2011 .
[43] Nan Li,et al. Linking energy-cyber-physical systems with occupancy prediction and interpretation through WiFi probe-based ensemble classification , 2019, Applied Energy.
[44] Zhenghua Chen,et al. Modeling regular occupancy in commercial buildings using stochastic models , 2015 .
[45] Kwok Wai Tham,et al. Predicting occupancy counts using physical and statistical Co 2 -based modeling methodologies , 2017 .
[46] Vineet R. Kamat,et al. Evaluation of position tracking technologies for user localization in indoor construction environments , 2009 .
[47] Brandon Hencey,et al. Predictive HVAC control using a Markov occupancy model , 2014, 2014 American Control Conference.
[48] Zhaoyan Fan,et al. Occupancy and indoor environment quality sensing for smart buildings , 2012, 2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings.
[49] Yang Zhao,et al. Virtual occupancy sensors for real-time occupancy information in buildings , 2015 .
[50] Vinny Cahill,et al. Exploiting user behaviour for context-aware power management , 2005, WiMob'2005), IEEE International Conference on Wireless And Mobile Computing, Networking And Communications, 2005..
[51] Thomas Weng,et al. Occupancy-driven energy management for smart building automation , 2010, BuildSys '10.
[52] Tianzhen Hong,et al. Data mining of space heating system performance in affordable housing , 2015, Building and Environment.
[53] Rachel Cardell-Oliver,et al. Occupancy Estimation Using a Low-Pixel Count Thermal Imager , 2016, IEEE Sensors Journal.
[54] Abbas Javed,et al. Smart Random Neural Network Controller for HVAC Using Cloud Computing Technology , 2017, IEEE Transactions on Industrial Informatics.
[55] Miguel Á. Carreira-Perpiñán,et al. OBSERVE: Occupancy-based system for efficient reduction of HVAC energy , 2011, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks.
[56] Gregory M. P. O'Hare,et al. Evaluation of energy-efficiency in lighting systems using sensor networks , 2009, BuildSys '09.
[57] Murray Thomson,et al. Four-state domestic building occupancy model for energy demand simulations , 2015 .
[58] Stéphane Ploix,et al. Estimating Occupancy In Heterogeneous Sensor Environment , 2016 .
[59] Adam Mayer,et al. Putting the green into corrections: Improving energy conservation, building function, safety and occupant well-being in an American correctional facility , 2017 .
[60] Yongjun Sun,et al. Modeling energy consumption in residential buildings: A bottom-up analysis based on occupant behavior pattern clustering and stochastic simulation , 2017 .
[61] Qianchuan Zhao,et al. Occupancy detection in the office by analyzing surveillance videos and its application to building energy conservation , 2017 .
[62] Robert X. Gao,et al. Occupancy estimation for smart buildings by an auto-regressive hidden Markov model , 2014, 2014 American Control Conference.
[63] Tugrul U. Daim,et al. Forecasting emerging technologies: Use of bibliometrics and patent analysis , 2006 .
[64] Karl Henrik Johansson,et al. Estimation of building occupancy levels through environmental signals deconvolution , 2013, BuildSys@SenSys.
[65] Yeng Chai Soh,et al. Real-time occupancy estimation using environmental parameters , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).
[66] Kevin Weekly,et al. Modeling and estimation of the humans' effect on the CO2 dynamics inside a conference room , 2014, 53rd IEEE Conference on Decision and Control.
[67] Qing-Shan Jia,et al. An Indoor Localization Algorithm for Lighting Control using RFID , 2008, 2008 IEEE Energy 2030 Conference.
[68] Nan Li,et al. Measuring and monitoring occupancy with an RFID based system for demand-driven HVAC operations , 2012 .
[69] Hélène Laurent,et al. Towards a sensor for detecting human presence and characterizing activity , 2011 .
[70] Weiming Shen,et al. Leveraging existing occupancy-related data for optimal control of commercial office buildings: A review , 2017, Adv. Eng. Informatics.
[71] Patrick X.W. Zou,et al. Review of 10 years research on building energy performance gap: Life-cycle and stakeholder perspectives , 2018, Energy and Buildings.
[72] Rita Streblow,et al. CO2 based occupancy detection algorithm: Experimental analysis and validation for office and residential buildings , 2015 .
[73] Carlos Duarte,et al. Revealing occupancy patterns in an office building through the use of occupancy sensor data , 2013 .
[74] D. Yan,et al. Global comparison of building energy use data within the context of climate change , 2020 .
[75] Julio J. Valdés,et al. Testing the accuracy of low-cost data streams for determining single-person office occupancy and their use for energy reduction of building services , 2017 .
[76] Alberto E. Cerpa,et al. POEM: Power-efficient occupancy-based energy management system , 2013, 2013 ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).
[77] Sheng-Fuu Lin,et al. Estimation of number of people in crowded scenes using perspective transformation , 2001, IEEE Trans. Syst. Man Cybern. Part A.
[78] Jian-Shuen Fang,et al. Real-time human identification using a pyroelectric infrared detector array and hidden Markov models. , 2006, Optics express.
[79] Simon Breslav,et al. Coupling stochastic occupant models to building performance simulation using the discrete event system specification formalism , 2014 .
[80] Zhaoxia Wang,et al. An occupant-based energy consumption prediction model for office equipment , 2015 .
[81] Xuan Luo,et al. Performance evaluation of an agent-based occupancy simulation model , 2017 .
[82] Rose Qingyang Hu,et al. Applying VLC in 5G Networks: Architectures and Key Technologies , 2016, IEEE Network.
[83] Rui Neves-Silva,et al. Stochastic models for building energy prediction based on occupant behavior assessment , 2012 .
[84] F. Descamps,et al. A method for the identification and modelling of realistic domestic occupancy sequences for building energy demand simulations and peer comparison , 2014 .
[85] Prabir Barooah,et al. Effect of various uncertainties on the performance of occupancy-based optimal control of HVAC zones , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).
[86] Darren Robinson,et al. Modelling occupants’ personal characteristics for thermal comfort prediction , 2011, International journal of biometeorology.
[87] Abbas Javed,et al. Experimental testing of a random neural network smart controller using a single zone test chamber , 2015, IET Networks.
[88] D. Linton,et al. Occupancy Monitoring Using Passive RFID Technology for Efficient Building Lighting Control , 2012, 2012 Fourth International EURASIP Workshop on RFID Technology.
[89] Junpei Zhong,et al. Adaptive Thermal Sensor Array Placement for Human Segmentation and Occupancy Estimation , 2020, IEEE Sensors Journal.
[90] Zheng Liu,et al. Occupancy prediction model for open-plan offices using real-time location system and inhomogeneous Markov chain , 2019, Building and Environment.
[91] Benjamin C. M. Fung,et al. A review of the-state-of-the-art in data-driven approaches for building energy prediction , 2020 .
[92] Oliver Amft,et al. Recognizing Energy-related Activities Using Sensors Commonly Installed in Office Buildings , 2013, ANT/SEIT.
[93] Hiroshi Yoshino,et al. IEA EBC annex 53: Total energy use in buildings—Analysis and evaluation methods , 2017 .
[94] Bing Dong,et al. Building energy and comfort management through occupant behaviour pattern detection based on a large-scale environmental sensor network , 2011 .
[95] D. Yan,et al. Investigation and analysis of Chinese residential building occupancy with large-scale questionnaire surveys , 2019, Energy and Buildings.
[96] Benjamin C. M. Fung,et al. A decision tree method for building energy demand modeling , 2010 .
[97] Hwataik Han,et al. Uncertainties in neural network model based on carbon dioxide concentration for occupancy estimation , 2017 .
[98] Hongsan Sun,et al. An occupant behavior modeling tool for co-simulation , 2016 .
[99] Zoltán Nagy,et al. Comprehensive analysis of the relationship between thermal comfort and building control research - A data-driven literature review , 2018 .
[100] Rajesh Gupta,et al. Sentinel: occupancy based HVAC actuation using existing WiFi infrastructure within commercial buildings , 2013, SenSys '13.
[101] José D. P. Rolim,et al. Indoor Location for Smart Environments with Wireless Sensor and Actuator Networks , 2017, 2017 IEEE 42nd Conference on Local Computer Networks (LCN).
[102] Burcin Becerik-Gerber,et al. A multi-sensor based occupancy estimation model for supporting demand driven HVAC operations , 2012, ANSS 2012.
[103] Arno Schlueter,et al. A review on occupant behavior in urban building energy models , 2018, Energy and Buildings.
[104] Arno Schlueter,et al. Occupant centered lighting control for comfort and energy efficient building operation , 2015 .
[105] Theis Heidmann Pedersen,et al. Establishing an image-based ground truth for validation of sensor data-based room occupancy detection , 2016 .
[106] James A. Davis,et al. Occupancy diversity factors for common university building types , 2010 .
[107] José A. Gallud,et al. Improving location awareness in indoor spaces using RFID technology , 2010, Expert Syst. Appl..
[108] J. Widén,et al. A high-resolution stochastic model of domestic activity patterns and electricity demand , 2010 .
[109] Eric Wai Ming Lee,et al. An intelligent approach to assessing the effect of building occupancy on building cooling load predi , 2011 .
[110] Luis M. Candanedo,et al. A methodology based on Hidden Markov Models for occupancy detection and a case study in a low energy residential building , 2017 .
[111] Jakub Kolarik,et al. Method for long-term mapping of occupancy patterns in open-plan and single office spaces by using passive-infrared (PIR) sensors mounted below desks , 2021 .
[112] Indrajit Banerjee,et al. IoT-Based Sensor Data Fusion for Occupancy Sensing Using Dempster–Shafer Evidence Theory for Smart Buildings , 2017, IEEE Internet of Things Journal.
[113] Lihua Xie,et al. Building occupancy estimation and detection: A review , 2018, Energy and Buildings.
[114] Huang-Chia Shih,et al. A robust occupancy detection and tracking algorithm for the automatic monitoring and commissioning of a building , 2014 .
[115] Siew Eang Lee,et al. Review of occupancy sensing systems and occupancy modeling methodologies for the application in institutional buildings , 2016 .
[116] H. Burak Gunay,et al. Opportunistic occupancy-count estimation using sensor fusion: A case study , 2019, Building and Environment.
[117] Alessandro A. Nacci,et al. BlueSentinel: a first approach using iBeacon for an energy efficient occupancy detection system , 2014, BuildSys@SenSys.
[118] Francesca Stazi,et al. A literature review on driving factors and contextual events influencing occupants' behaviours in buildings , 2017 .
[119] Yeng Chai Soh,et al. Building Occupancy Estimation with Environmental Sensors via CDBLSTM , 2017, IEEE Transactions on Industrial Electronics.
[120] Tsuhan Chen,et al. Active Multicamera Networks: From Rendering to Surveillance , 2008, IEEE Journal of Selected Topics in Signal Processing.
[121] Wei Wang,et al. Modeling and predicting occupancy profile in office space with a Wi-Fi probe-based Dynamic Markov Time-Window Inference approach , 2017 .
[122] Donal Finn,et al. Development of occupancy-integrated archetypes: Use of data mining clustering techniques to embed occupant behaviour profiles in archetypes , 2019, Energy and Buildings.
[123] Shin‐Tson Wu,et al. SLEEPIR: Synchronized Low-Energy Electronically Chopped PIR Sensor for True Presence Detection , 2020, IEEE Sensors Letters.
[124] Zhiping Lin,et al. Two-stage structured learning approach for stable occupancy detection , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).
[125] Alexis Boukouvalas,et al. Predicting room occupancy with a single passive infrared (PIR) sensor through behavior extraction , 2016, UbiComp.
[126] Rui Zhang,et al. An information technology enabled sustainability test-bed (ITEST) for occupancy detection through an environmental sensing network , 2010 .
[127] Bing Dong,et al. A real-time model predictive control for building heating and cooling systems based on the occupancy behavior pattern detection and local weather forecasting , 2013, Building Simulation.
[128] R.F. Hughes,et al. Substantial energy savings through adaptive lighting , 2008, 2008 IEEE Canada Electric Power Conference.
[129] Burak Gunay,et al. A critical review of field implementations of occupant-centric building controls , 2019, Building and Environment.
[130] Alberto Cerpa,et al. Occupancy based demand response HVAC control strategy , 2010, BuildSys '10.
[131] Hao Jiang,et al. Robust occupancy inference with commodity WiFi , 2016, 2016 IEEE 12th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob).
[132] Kaiyu Sun,et al. A novel stochastic modeling method to simulate cooling loads in residential districts , 2017 .
[133] Qi Hao,et al. Multiple Human Tracking and Identification With Wireless Distributed Pyroelectric Sensor Systems , 2009, IEEE Systems Journal.
[134] Ning Xu,et al. Cross-source sensing data fusion for building occupancy prediction with adaptive lasso feature filtering , 2019, Building and Environment.
[135] Ewa Wäckelgård,et al. A combined Markov-chain and bottom-up approach to modelling of domestic lighting demand , 2009 .
[136] Aravind K. Mikkilineni,et al. A novel occupancy detection solution using low-power IR-FPA based wireless occupancy sensor , 2019, Energy and Buildings.
[137] Dino Bouchlaghem,et al. Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap , 2012 .
[138] Zhenghua Chen,et al. A fusion framework for occupancy estimation in office buildings based on environmental sensor data , 2016 .
[139] Darren Robinson,et al. A generalised stochastic model for the simulation of occupant presence , 2008 .
[140] Mary Ann Piette,et al. Inferring occupant counts from Wi-Fi data in buildings through machine learning , 2019, Building and Environment.
[141] Mary Ann Piette,et al. Predicting plug loads with occupant count data through a deep learning approach , 2019, Energy.
[142] Abbas Javed,et al. Occupancy detection in non-residential buildings – A survey and novel privacy preserved occupancy monitoring solution , 2020, Applied Computing and Informatics.
[143] Na Zhu,et al. Occupancy prediction through Markov based feedback recurrent neural network (M-FRNN) algorithm with WiFi probe technology , 2018, Building and Environment.
[144] Tianzhen Hong,et al. Statistical analysis and modeling of occupancy patterns in open-plan offices using measured lighting-switch data , 2013 .
[145] Luis M. Candanedo,et al. Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models , 2016 .
[146] Sean P. Meyn,et al. A sensor-utility-network method for estimation of occupancy in buildings , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.
[147] Kevin Weekly,et al. Occupancy Detection via Environmental Sensing , 2018, IEEE Transactions on Automation Science and Engineering.
[148] Franklin P. Mills,et al. Rethinking the role of occupant behavior in building energy performance: A review , 2018, Energy and Buildings.
[149] Yixing Chen,et al. Simulation and visualization of energy-related occupant behavior in office buildings , 2017 .
[150] Yi Jiang,et al. Recognition of air-conditioner operation from indoor air temperature and relative humidity by a data mining approach , 2016 .
[151] Ya Wang,et al. A Low-Power Electric-Mechanical Driving Approach for True Occupancy Detection Using a Shuttered Passive Infrared Sensor , 2019, IEEE Sensors Journal.
[152] Ian Plewis,et al. Accumulated labour market disadvantage and limiting long-term illness: data from the 1971-1991 Office for National Statistics' Longitudinal Study. , 2002, International journal of epidemiology.
[153] Yixing Chen,et al. Occupant behavior models: A critical review of implementation and representation approaches in building performance simulation programs , 2018 .
[154] Youtian Du,et al. Measuring indoor occupancy in intelligent buildings using the fusion of vision sensors , 2013 .
[155] Bo Yang,et al. Indoor multiple human targets localization and tracking using thermopile sensor , 2019 .
[156] Agnieszka Wyłomańska,et al. Detection of occupancy profile based on carbon dioxide concentration pattern matching , 2016 .
[157] A. Szczurek,et al. Occupancy determination based on time series of CO2 concentration, temperature and relative humidity , 2017 .
[158] Jie Zhang,et al. An Intelligent Building Occupancy Detection System Based on Sparse Auto-Encoder , 2017, 2017 IEEE Winter Applications of Computer Vision Workshops (WACVW).
[159] Xuan Luo,et al. An agent-based stochastic Occupancy Simulator , 2018 .
[160] Zheng Yang,et al. Modeling personalized occupancy profiles for representing long term patterns by using ambient context , 2014 .
[161] Qi Hao,et al. Preprocessing Design in Pyroelectric Infrared Sensor-Based Human-Tracking System: On Sensor Selection and Calibration , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.
[162] Charles Culp,et al. Uncalibrated Building Energy Simulation Modeling Results , 2006 .
[163] Yeng Chai Soh,et al. Occupancy estimation from environmental parameters using wrapper and hybrid feature selection , 2017, Appl. Soft Comput..
[164] Ming Jin,et al. PresenceSense: zero-training algorithm for individual presence detection based on power monitoring , 2014, BuildSys@SenSys.
[165] Diego López-de-Ipiña,et al. Building an occupancy model from sensor networks in office environments , 2011, 2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras.
[166] Tianzhen Hong,et al. Occupancy schedules learning process through a data mining framework , 2015 .
[167] Li Shao,et al. Understanding occupancy pattern and improving building energy efficiency through Wi-Fi based indoor positioning , 2017 .
[168] Shaojie Tang,et al. Electronic frog eye: Counting crowd using WiFi , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.
[169] Mohammad Yusri Hassan,et al. A review on lighting control technologies in commercial buildings, their performance and affecting factors , 2014 .
[170] Tianzhen Hong,et al. Simulation of occupancy in buildings , 2015 .