Urban building energy modeling: State of the art and future prospects
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J. Widén | F. Johari | G. Peronato | P. Sadeghian | X. Zhao
[1] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.
[2] Gebräuchliche Fertigarzneimittel,et al. V , 1893, Therapielexikon Neurologie.
[3] Donald P. Greenberg,et al. Modeling the interaction of light between diffuse surfaces , 1984, SIGGRAPH.
[4] J. Michalsky,et al. Modeling daylight availability and irradiance components from direct and global irradiance , 1990 .
[5] J. Michalsky,et al. All-weather model for sky luminance distribution—Preliminary configuration and validation , 1993 .
[6] Anil K. Jain,et al. Data clustering: a review , 1999, CSUR.
[7] Thomas de Quincey. [C] , 2000, The Works of Thomas De Quincey, Vol. 1: Writings, 1799–1820.
[8] F Déqué,et al. Grey boxes used to represent buildings with a minimum number of geometric and thermal parameters , 2000 .
[9] Brian Norton,et al. Domestic energy use and air quality; a case study of the city of Belfast , 2001 .
[10] M. Santamouris,et al. On the impact of urban climate on the energy consumption of buildings , 2001 .
[11] H. Kondo,et al. A Simple Single-Layer Urban Canopy Model For Atmospheric Models: Comparison With Multi-Layer And Slab Models , 2001 .
[12] P. Rich,et al. A geometric solar radiation model with applications in agriculture and forestry , 2002 .
[13] David J. Sailor,et al. A top-down methodology for developing diurnal and seasonal anthropogenic heating profiles for urban areas , 2004 .
[14] D. Robinson,et al. Solar radiation modelling in the urban context , 2004 .
[15] S. Thorsson,et al. Thermal bioclimatic conditions and patterns of behaviour in an urban park in Göteborg, Sweden , 2004, International journal of biometeorology.
[16] D. Robinson,et al. A simplified radiosity algorithm for general urban radiation exchange , 2005 .
[17] G. Chicco,et al. Comparisons among clustering techniques for electricity customer classification , 2006, IEEE Transactions on Power Systems.
[18] J. Kämpf,et al. A simplified thermal model to support analysis of urban resource flows , 2007 .
[19] D. Stensrud. Parameterization Schemes: Keys to Understanding Numerical Weather Prediction Models , 2007 .
[20] Andrew Stone,et al. SUNtool - A new modelling paradigm for simulating and optimising urban sustainability , 2007 .
[21] H. Stanley,et al. Gravity model in the Korean highway , 2007, 0710.1274.
[22] K. Oleson,et al. An Urban Parameterization for a Global Climate Model. Part I: Formulation and Evaluation for Two Cities , 2008 .
[23] Shem Heiple,et al. Using building energy simulation and geospatial modeling techniques to determine high resolution building sector energy consumption profiles , 2008 .
[24] Georgios Kokogiannakis,et al. Comparison of the simplified methods of the ISO 13790 standard and detailed modelling programs in a regulatory context , 2008 .
[25] Ewa Wäckelgård,et al. A combined Markov-chain and bottom-up approach to modelling of domestic lighting demand , 2009 .
[26] A. Rasheed,et al. CITYSIM: Comprehensive Micro-Simulation of Resource Flows for Sustainable Urban Planning , 2009 .
[27] Alessandro Vespignani,et al. Multiscale mobility networks and the spatial spreading of infectious diseases , 2009, Proceedings of the National Academy of Sciences.
[28] Darren Robinson,et al. Optimisation of Urban Energy Demand Using an Evolutionary Algorithm , 2009 .
[29] V. Ismet Ugursal,et al. Modeling of end-use energy consumption in the residential sector: A review of modeling techniques , 2009 .
[30] J. Widén,et al. Constructing load profiles for household electricity and hot water from time-use data—Modelling approach and validation , 2009 .
[31] Michael T. Gastner,et al. The complex network of global cargo ship movements , 2010, Journal of The Royal Society Interface.
[32] J. Widén,et al. A high-resolution stochastic model of domestic activity patterns and electricity demand , 2010 .
[33] Dejan Mumovic,et al. A review of bottom-up building stock models for energy consumption in the residential sector , 2010 .
[34] Bert G. Heusinkveld,et al. Quantifying urban heat island effects and human comfort for cities of variable size and urban morphology in the Netherlands , 2011 .
[35] George Havenith,et al. UTCI—Why another thermal index? , 2011, International Journal of Biometeorology.
[36] H. Landsberg. Urban Climate , 2011, Urban Ecology for Citizens and Planners.
[37] Fei Chen,et al. A Study of the Urban Boundary Layer Using Different Urban Parameterizations and High-Resolution Urban Canopy Parameters with WRF , 2011 .
[38] Baizhan Li,et al. A simplified mathematical model for urban microclimate simulation , 2011 .
[39] Guillaume Deffuant,et al. A Universal Model of Commuting Networks , 2012, PloS one.
[40] Marta C. González,et al. A universal model for mobility and migration patterns , 2011, Nature.
[41] Andrea Gasparella,et al. Comparison Of Quasi-Steady State And Dynamic Simulation Approaches For The Calculation Of Building Energy Needs: Thermal Losses , 2012 .
[42] Paul Strachan,et al. Developing archetypes for domestic dwellings: An Irish case study , 2012 .
[43] Giuliano Dall'O',et al. A methodology for the energy performance classification of residential building stock on an urban scale , 2012 .
[44] Liang Chen,et al. Outdoor thermal comfort and outdoor activities: A review of research in the past decade , 2012 .
[45] Mark Jennings,et al. A review of urban energy system models: Approaches, challenges and opportunities , 2012 .
[47] Vijay Modi,et al. Spatial distribution of urban building energy consumption by end use , 2012 .
[48] David J. Spiegelhalter,et al. Handling uncertainty in housing stock models , 2012 .
[49] Santo Fortunato,et al. World citation and collaboration networks: uncovering the role of geography in science , 2012, Scientific Reports.
[50] Modeling collective human mobility: Understanding exponential law of intra-urban movement , 2012, ArXiv.
[51] Z. Néda,et al. Human Mobility in a Continuum Approach , 2012, PloS one.
[52] M. Batty,et al. Gravity versus radiation models: on the importance of scale and heterogeneity in commuting flows. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.
[53] V. Dorer,et al. Modelling The Urban Microclimate And Its Impact On The Energy Demand Of Buildings And Building Clusters , 2013, Building Simulation Conference Proceedings.
[54] Simone Ferrari,et al. A supporting method for defining energy strategies in the building sector at urban scale , 2013 .
[55] Christoph F. Reinhart,et al. UMI - AN URBAN SIMULATION ENVIRONMENT FOR BUILDING , 2013 .
[56] Nathan Eagle,et al. Limits of Predictability in Commuting Flows in the Absence of Data for Calibration , 2014, Scientific Reports.
[57] Juha Jokisalo,et al. Calculation method and tool for assessing energy consumption in the building stock , 2014 .
[58] Andrea Gasparella,et al. Selection of Representative Buildings through Preliminary Cluster Analysis , 2014 .
[59] Paul Raftery,et al. A review of methods to match building energy simulation models to measured data , 2014 .
[60] Jari Saramäki,et al. Inferring human mobility using communication patterns , 2014, Scientific Reports.
[61] Eui-Jong Kim,et al. Urban energy simulation: Simplification and reduction of building envelope models , 2014 .
[62] Diane Perez,et al. A framework to model and simulate the disaggregated energy flows supplying buildings in urban areas , 2014 .
[63] Ursula Eicker,et al. SimStadt, a new workflow-driven urban energy simulation platform for CityGML city models , 2015 .
[64] Jean-Louis Scartezzini,et al. Multi-Scale Modelling to Improve Climate Data for Building Energy Models , 2015, Building Simulation Conference Proceedings.
[65] Filip Biljecki,et al. Applications of 3D City Models: State of the Art Review , 2015, ISPRS Int. J. Geo Inf..
[66] Volker Coors,et al. Combining GIS-based statistical and engineering urban heat consumption models: Towards a new framework for multi-scale policy support , 2015 .
[67] Christoph F. Reinhart,et al. Urban building energy modeling – A review of a nascent field , 2015 .
[68] Jonas Allegrini,et al. A review of modelling approaches and tools for the simulation of district-scale energy systems , 2015 .
[69] F. Maréchal,et al. Energy Planning in the Urban Context: Challenges and Perspectives☆ , 2015 .
[70] Marjorie Musy,et al. SOLENE-microclimate: A Tool to Evaluate Envelopes Efficiency on Energy Consumption at District Scale. , 2015 .
[71] Sang Hoon Lee,et al. Commercial Building Energy Saver: An energy retrofit analysis toolkit , 2015 .
[72] Arno Schlueter,et al. Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts , 2015 .
[73] Pietro Ferraro,et al. Clustering analysis of the electrical load in european countries , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).
[74] C. Reinhart,et al. Three Methods for Characterizing Building Archetypes in Urban Energy Simulation. A Case Study in Kuwait City , 2015 .
[75] François Maréchal,et al. City Energy Analyst (CEA): Integrated framework for analysis and optimization of building energy systems in neighborhoods and city districts , 2016 .
[76] Christoph F. Reinhart,et al. Modeling Boston: A workflow for the efficient generation and maintenance of urban building energy models from existing geospatial datasets , 2016 .
[77] Marta Braulio-Gonzalo,et al. A methodology for predicting the energy performance and indoor thermal comfort of residential stocks on the neighbourhood and city scales. A case study in Spain , 2016 .
[78] Thomas H. Kolbe,et al. Extending Semantic 3D City Models by Supply and Disposal Networks for Analysing the Urban Supply Situation , 2016 .
[79] Thomas Olofsson,et al. Assessment of renovation measures for a dwelling area - Impacts on energy efficiency and building certification , 2016 .
[80] Mary Ann Piette,et al. A pattern-based automated approach to building energy model calibration , 2016 .
[81] Maxime Lenormand,et al. Systematic comparison of trip distribution laws and models , 2015, 1506.04889.
[82] Enedir Ghisi,et al. Method for obtaining reference buildings , 2016 .
[83] Arun Kumar,et al. A review on modeling and simulation of building energy systems , 2016 .
[84] Jean-Louis Scartezzini,et al. Outdoor human comfort and thermal stress: A comprehensive review on models and standards , 2016 .
[85] CESIUM—A VIRTUAL GLOBE WITH STRONG POTENTIAL APPLICATIONS IN GEOSCIENCE EDUCATION , 2016 .
[86] Marjorie Musy,et al. Simulation tools to assess microclimate and building energy – A case study on the design of a new district , 2016 .
[87] Christoph F. Reinhart,et al. Validation of a Bayesian-based method for defining residential archetypes in urban building energy models , 2017 .
[88] Christoph F. Reinhart,et al. Shoeboxer: An algorithm for abstracted rapid multi-zone urban building energy model generation and simulation , 2017 .
[89] S. Hellweg,et al. Big data GIS analysis for novel approaches in building stock modelling , 2017 .
[90] J. Taylor,et al. Urban energy flux: Spatiotemporal fluctuations of building energy consumption and human mobility-driven prediction , 2017 .
[91] Kristen S. Cetin,et al. Modeling urban building energy use: A review of modeling approaches and procedures , 2017 .
[92] Patrizia Lombardi,et al. Urban energy planning procedure for sustainable development in the built environment: A review of available spatial approaches , 2017 .
[93] Ruchi Choudhary,et al. Bayesian Calibration of Residential Building Clusters using a Single Geometric Building Representation , 2017, Building Simulation Conference Proceedings.
[94] Adil Rasheed,et al. Methodology for assessing cycling comfort during a smart city development , 2017 .
[95] Guglielmina Mutani,et al. Space heating models at urban scale for buildings in the city of Turin (Italy) , 2017 .
[96] Romain Nouvel,et al. Setting intelligent city tiling strategies for urban shading simulations , 2017 .
[97] Ali Hajiah,et al. Comparison of four building archetype characterization methods in urban building energy modeling (UBEM): A residential case study in Kuwait City , 2017 .
[98] Ardeshir Mahdavi,et al. Reductive bottom-up urban energy computing supported by multivariate cluster analysis , 2017 .
[99] Christopher Tull,et al. A data-driven predictive model of city-scale energy use in buildings , 2017 .
[100] Christoph F. Reinhart,et al. The Use of Multi-detail Building Archetypes in Urban Energy Modelling☆ , 2017 .
[101] Rishee K. Jain,et al. Data-driven Urban Energy Simulation (DUE-S): Integrating machine learning into an urban building energy simulation workflow , 2017 .
[102] Ernst Worrell,et al. Urban energy systems within the transition to sustainable development. A research agenda for urban metabolism , 2017 .
[103] Bje Bert Blocken,et al. A review on the CFD analysis of urban microclimate , 2017 .
[104] Jean-Louis Scartezzini,et al. An overview of simulation tools for predicting the mean radiant temperature in an outdoor space , 2017 .
[105] Manuel Herrera,et al. A review of current and future weather data for building simulation , 2017 .
[106] Christoph Hochenauer,et al. Novel validated method for GIS based automated dynamic urban building energy simulations , 2017 .
[107] Volker Coors,et al. The influence of data quality on urban heating demand modeling using 3D city models , 2017, Comput. Environ. Urban Syst..
[108] Frédéric Kuznik,et al. Modeling the heating and cooling energy demand of urban buildings at city scale , 2018 .
[109] Kim Bjarne Wittchen,et al. Estimating the energy-saving potential in national building stocks – A methodology review , 2018 .
[110] Filip Biljecki,et al. CityGML Application Domain Extension (ADE): overview of developments , 2018, Open Geospatial Data, Software and Standards.
[111] Tao Feng,et al. A review of urban energy systems at building cluster level incorporating renewable-energy-source (RES) envelope solutions , 2018, Applied Energy.
[112] Dirk Müller,et al. TEASER: an open tool for urban energy modelling of building stocks , 2018 .
[113] Luigi Marletta,et al. Weather data morphing to improve building energy modeling in an urban context , 2018, Mathematical Modelling of Engineering Problems.
[114] Giorgio Agugiaro,et al. The Energy Application Domain Extension for CityGML: enhancing interoperability for urban energy simulations , 2018, Open Geospatial Data, Software and Standards.
[115] Rasmus Elbæk Hedegaard,et al. Hierarchical calibration of archetypes for urban building energy modeling , 2018, Energy and Buildings.
[116] Jan Carmeliet,et al. CESAR: A bottom-up building stock modelling tool for Switzerland to address sustainable energy transformation strategies , 2018, Energy and Buildings.
[117] M. Liu,et al. Developing urban residential reference buildings using clustering analysis of satellite images , 2018, Energy and Buildings.
[118] Arno Schlueter,et al. Urban and building multiscale co-simulation: case study implementations on two university campuses , 2018 .
[119] Filip Biljecki,et al. Achieving Complete and Near-Lossless Conversion from IFC to CityGML , 2018, ISPRS Int. J. Geo Inf..
[120] Guglielmina Mutani,et al. A GIS-statistical approach for assessing built environment energy use at urban scale , 2018 .
[121] Ruth Kerrigan,et al. Identification of representative buildings and building groups in urban datasets using a novel pre-processing, classification, clustering and predictive modelling approach , 2018, Building and Environment.
[122] G. F. Garuma,et al. Review of urban surface parameterizations for numerical climate models , 2017, Urban Climate.
[123] M. Barthelemy,et al. Human mobility: Models and applications , 2017, 1710.00004.
[124] Yixing Chen,et al. Impacts of building geometry modeling methods on the simulation results of urban building energy models , 2018 .
[125] Arno Schlueter,et al. A review on occupant behavior in urban building energy models , 2018, Energy and Buildings.
[126] Andreas Donaubauer,et al. 3DCityDB - a 3D geodatabase solution for the management, analysis, and visualization of semantic 3D city models based on CityGML , 2018, Open Geospatial Data, Software and Standards.
[127] Jayashri Ravishankar,et al. Computational tools for design, analysis, and management of residential energy systems , 2018, Applied Energy.
[128] Gerald Schweiger,et al. Novel method to simulate large-scale thermal city models , 2018, Energy.
[129] G. Peronato. Urban planning support based on the photovoltaic potential of buildings: a multi-scenario ranking system , 2019 .
[130] Joakim Widén,et al. Towards Urban Building Energy Modelling: A Comparison of Available Tools , 2019 .
[131] Verena Weiler,et al. Renewable Energy Generation Scenarios Using 3D Urban Modeling Tools—Methodology for Heat Pump and Co-Generation Systems with Case Study Application † , 2019, Energies.
[132] Jan Hensen,et al. Building Performance Simulation for Design and Operation , 2019 .
[133] Tsuyoshi Murata,et al. {m , 1934, ACML.
[134] P. Alam. ‘K’ , 2021, Composites Engineering.
[135] P. Alam. ‘L’ , 2021, Composites Engineering: An A–Z Guide.
[136] Radiance , 2021, Computer Vision.
[137] P. Alam. ‘A’ , 2021, Composites Engineering: An A–Z Guide.