Application-driven level-of-detail modeling framework for occupant air-conditioning behavior in district cooling

[1]  Xiaoxia Zhou,et al.  Novel approach to typical air-conditioning behavior pattern extraction based on large-scale VRF system online monitoring data , 2023, Journal of Building Engineering.

[2]  Shan Hu,et al.  A statistical quantitative analysis of the correlations between socio-demographic characteristics and household occupancy patterns in residential buildings in China , 2023, Energy and Buildings.

[3]  Anthony D. Fontanini,et al.  Stochastic simulation of occupant-driven energy use in a bottom-up residential building stock model , 2022, Applied Energy.

[4]  T. Žakula,et al.  Occupant preferences on the interaction with human-centered control systems in school buildings , 2022, Journal of Building Engineering.

[5]  D. Yan,et al.  A novel approach of day-ahead cooling load prediction and optimal control for ice-based thermal energy storage (TES) system in commercial buildings , 2022, Energy and Buildings.

[6]  D. Johansson,et al.  A simulation model for the design and analysis of district systems with simultaneous heating and cooling demands , 2022, Energy.

[7]  A. Mahdavi,et al.  A level-of-details framework for representing occupant behavior in agent-based models , 2022, Automation in Construction.

[8]  D. Pan,et al.  Modeling of occupant energy consumption behavior based on human dynamics theory: A case study of a government office building , 2022, Journal of Building Engineering.

[9]  Nora El-Gohary,et al.  Real data-driven occupant-behavior optimization for reduced energy consumption and improved comfort , 2021 .

[10]  Maohui Luo,et al.  Energy and comfort performance of occupant-centric air conditioning strategy in office buildings with personal comfort devices , 2021, Building Simulation.

[11]  Yuanjie Zheng,et al.  An agent-based modeling approach combined with deep learning method in simulating household energy consumption , 2021, Journal of Building Engineering.

[12]  Wanyue Chen,et al.  Review on occupancy detection and prediction in building simulation , 2021, Building Simulation.

[13]  Zhiyuan He,et al.  A framework for estimating the energy-saving potential of occupant behaviour improvement , 2021, Applied Energy.

[14]  Kamel Ghali,et al.  Model-based adaptive controller for personalized ventilation and thermal comfort in naturally ventilated spaces , 2021, Building Simulation.

[15]  Shengwei Wang,et al.  Impacts of technology-guided occupant behavior on air-conditioning system control and building energy use , 2021 .

[16]  Hsi-Hsien Wei,et al.  Influence of Occupant Behavior for Building Energy Conservation: A Systematic Review Study of Diverse Modeling and Simulation Approach , 2021, Buildings.

[17]  Jarek Kurnitski,et al.  Office Building Tenants’ Electricity Use Model for Building Performance Simulations , 2020, Energies.

[18]  John W. Polak,et al.  An approach for building occupancy modelling considering the urban context , 2020, Building and Environment.

[19]  Hongsan Sun,et al.  Appliance use behavior modelling and evaluation in residential buildings: A case study of television energy use , 2020, Building Simulation.

[20]  Shen Wei,et al.  A prediction model coupling occupant lighting and shading behaviors in private offices , 2020, Energy and Buildings.

[21]  Louis Gosselin,et al.  Probabilistic window opening model considering occupant behavior diversity: A data-driven case study of Canadian residential buildings , 2020 .

[22]  Meng Liu,et al.  A study on temperature-setting behavior for room air conditioners based on big data , 2020 .

[23]  H. Burak Gunay,et al.  Optimization of electricity use in office buildings under occupant uncertainty , 2020 .

[24]  D. Yan,et al.  Using bottom-up model to analyze cooling energy consumption in China's urban residential building , 2019, Energy and Buildings.

[25]  Nandana Mihindukulasooriya,et al.  Enhancing energy management at district and building levels via an EM-KPI ontology , 2019, Automation in Construction.

[26]  S. Poulopoulos,et al.  Energy use and saving in residential sector and occupant behavior: A case study in Athens , 2018, Energy and Buildings.

[27]  Rishee K. Jain,et al.  Energy modeling of urban informal settlement redevelopment: Exploring design parameters for optimal thermal comfort in Dharavi, Mumbai, India , 2018, Applied Energy.

[28]  W. Feng,et al.  Scenarios of energy efficiency and CO2 emissions reduction potential in the buildings sector in China to year 2050 , 2018, Nature Energy.

[29]  Sanaz Saeidi,et al.  Spatial-temporal event-driven modeling for occupant behavior studies using immersive virtual environments , 2018, Automation in Construction.

[30]  Chuang Wang,et al.  The evaluation of stochastic occupant behavior models from an application-oriented perspective: Using the lighting behavior model as a case study , 2018, Energy and Buildings.

[31]  Pieter de Wilde,et al.  A review of uncertainty analysis in building energy assessment , 2018, Renewable and Sustainable Energy Reviews.

[32]  Roberto Lamberts,et al.  A review of occupant behaviour in residential buildings , 2018, Energy and Buildings.

[33]  Da Yan,et al.  Clustering and statistical analyses of air-conditioning intensity and use patterns in residential buildings , 2018, Energy and Buildings.

[34]  Arno Schlueter,et al.  A review on occupant behavior in urban building energy models , 2018, Energy and Buildings.

[35]  Kaiyu Sun,et al.  A novel stochastic modeling method to simulate cooling loads in residential districts , 2017 .

[36]  Hiroshi Yoshino,et al.  IEA EBC annex 53: Total energy use in buildings—Analysis and evaluation methods , 2017 .

[37]  L. Zhang,et al.  Evaluation of energy saving effects of tiered electricity pricing and investigation of the energy saving willingness of residents , 2017 .

[38]  Angela Lee,et al.  The impact of occupants’ behaviours on building energy analysis: A research review , 2017 .

[39]  Yingjun Ruan,et al.  The role of occupant behavior in low carbon oriented residential community planning: A case study in Qingdao , 2017 .

[40]  Carl-Alexander Graubner,et al.  Analysis of heating load diversity in German residential districts and implications for the application in district heating systems , 2017 .

[41]  Guoqin Zhang,et al.  Low-carbon behavior approaches for reducing direct carbon emissions: Household energy use in a coastal city , 2017 .

[42]  Dirk Saelens,et al.  Modelling uncertainty in district energy simulations by stochastic residential occupant behaviour , 2016 .

[43]  Fu Xiao,et al.  An uncertainty-based design optimization method for district cooling systems , 2016 .

[44]  Chuang Wang,et al.  A preliminary research on the derivation of typical occupant behavior based on large-scale questionnaire surveys , 2016 .

[45]  Stefano Paolo Corgnati,et al.  Occupant behaviour and robustness of building design , 2015 .

[46]  Tianzhen Hong,et al.  Occupant behavior modeling for building performance simulation: Current state and future challenges , 2015 .

[47]  Jin Wen,et al.  Simulating the human-building interaction: Development and validation of an agent-based model of office occupant behaviors , 2015 .

[48]  Chuang Wang,et al.  Air-conditioning usage conditional probability model for residential buildings , 2014 .

[49]  Ali Malkawi,et al.  Simulating multiple occupant behaviors in buildings: An agent-based modeling approach , 2014 .

[50]  Y. Li A Clustering Method Based on K-Means Algorithm , 2013 .

[51]  J. Widén,et al.  Sensitivity of district heating system operation to heat demand reductions and electricity price variations: A Swedish example , 2012 .

[52]  Marc A. Rosen,et al.  District heating and cooling: Review of technology and potential enhancements , 2012 .

[53]  Yi Jiang,et al.  A novel approach for building occupancy simulation , 2011 .

[54]  James A. Davis,et al.  Occupancy diversity factors for common university building types , 2010 .

[55]  Darren Robinson,et al.  Interactions with window openings by office occupants , 2009 .

[56]  M. Shukuya,et al.  Comparison of theoretical and statistical models of air-conditioning-unit usage behaviour in a residential setting under Japanese climatic conditions , 2009 .

[57]  Yi Jiang,et al.  IISABRE: An integrated building simulation environment , 1997 .

[58]  Yi Jiang,et al.  A new multizone model for the simulation of building thermal performance , 1997 .

[59]  Da Yan,et al.  Predicting open-plan office window operating behavior using the random forest algorithm , 2021 .

[60]  D. Yan,et al.  Influence of occupant behaviour on oversizing issue of heat pumps for residential district in Hot Summer and Cold Winter zone of China , 2017 .

[61]  Chuang Wang,et al.  A generalized probabilistic formula relating occupant behavior to environmental conditions , 2016 .

[62]  Tianzhen Hong,et al.  Simulation of occupancy in buildings , 2015 .