Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow

[1]  Burcin Becerik-Gerber,et al.  A model calibration framework for simultaneous multi-level building energy simulation , 2015 .

[2]  Charles Culp,et al.  Uncalibrated Building Energy Simulation Modeling Results , 2006 .

[3]  Richard E. Edwards,et al.  Supercomputer assisted generation of machine learning agents for the calibration of building energy models , 2013, XSEDE.

[4]  Rita Streblow,et al.  Low order thermal network models for dynamic simulations of buildings on city district scale , 2014 .

[5]  Shem Heiple,et al.  Using building energy simulation and geospatial modeling techniques to determine high resolution building sector energy consumption profiles , 2008 .

[6]  John E. Taylor,et al.  The impact of natural ventilation on building energy requirement at inter-building scale , 2016 .

[7]  John E. Taylor,et al.  Inter-building effect: Simulating the impact of a network of buildings on the accuracy of building energy performance predictions , 2012 .

[8]  Christoph F. Reinhart,et al.  Validation of a Bayesian-based method for defining residential archetypes in urban building energy models , 2017 .

[9]  Massimiliano Manfren,et al.  Calibration and uncertainty analysis for computer models – A meta-model based approach for integrated building energy simulation , 2013 .

[10]  Hamid Montazeri,et al.  CFD simulation and validation of urban microclimate: A case study for Bergpolder Zuid, Rotterdam , 2015 .

[11]  Mohammad Mottahedi,et al.  On the development of multi-linear regression analysis to assess energy consumption in the early stages of building design , 2014 .

[12]  S. Iniyan,et al.  Energy efficient fuzzy based combined variable refrigerant volume and variable air volume air conditioning system for buildings , 2010 .

[13]  Mary Ann Piette,et al.  Automatic generation and simulation of urban building energy models based on city datasets for city-scale building retrofit analysis , 2017 .

[14]  Christoph F. Reinhart,et al.  Urban building energy modeling – A review of a nascent field , 2015 .

[15]  Giuliano Dall'O',et al.  A methodology for the energy performance classification of residential building stock on an urban scale , 2012 .

[16]  Benjamin C. M. Fung,et al.  A decision tree method for building energy demand modeling , 2010 .

[17]  Qinglin Meng,et al.  An integrated simulation method for building energy performance assessment in urban environments , 2012 .

[18]  Frédéric Magoulès,et al.  A review on the prediction of building energy consumption , 2012 .

[19]  B. Dong,et al.  Applying support vector machines to predict building energy consumption in tropical region , 2005 .

[20]  Jonas Allegrini,et al.  A review of modelling approaches and tools for the simulation of district-scale energy systems , 2015 .

[21]  Kevin M. Smith,et al.  Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy , 2014 .

[22]  Yeonsook Heo,et al.  Calibration of building energy models for retrofit analysis under uncertainty , 2012 .

[23]  Holly Wasilowski Samuelson,et al.  Parametric Energy Simulation in Early Design: High-Rise Residential Buildings in Urban Contexts , 2016 .

[24]  Kelvin K. W. Yau,et al.  Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks , 2007 .

[25]  Xiuwen Yi,et al.  DNN-based prediction model for spatio-temporal data , 2016, SIGSPATIAL/GIS.

[26]  David J. Spiegelhalter,et al.  A hierarchical Bayesian framework for calibrating micro-level models with macro-level data , 2013 .

[27]  Kalani C. Dahanayake,et al.  Modelling the effect of tree-shading on summer indoor and outdoor thermal condition of two similar buildings in a Nigerian university , 2016 .

[28]  Vijay Modi,et al.  Spatial distribution of urban building energy consumption by end use , 2012 .

[29]  Fei-Yue Wang,et al.  Traffic Flow Prediction With Big Data: A Deep Learning Approach , 2015, IEEE Transactions on Intelligent Transportation Systems.

[30]  Yilong Han,et al.  Exploring mutual shading and mutual reflection inter-building effects on building energy performance ☆ , 2017 .

[31]  Ardeshir Mahdavi,et al.  Harnessing buildings’ operational diversity in a computational framework for high-resolution urban energy modeling , 2017 .

[32]  Burcin Becerik-Gerber,et al.  Why is the reliability of building simulation limited as a tool for evaluating energy conservation measures , 2015 .

[33]  Betul Bektas Ekici,et al.  Prediction of building energy consumption by using artificial neural networks , 2009, Adv. Eng. Softw..

[34]  Christopher Tull,et al.  A data-driven predictive model of city-scale energy use in buildings , 2017 .

[35]  Wided Medjroubi,et al.  GIS-based urban energy systems models and tools: Introducing a model for the optimisation of flexibilisation technologies in urban areas , 2017 .

[36]  J. Scartezzini,et al.  Multi-scale modelling to evaluate building energy consumption at the neighbourhood scale , 2017, PloS one.

[37]  Zhengwei Li,et al.  Methods for benchmarking building energy consumption against its past or intended performance: An overview , 2014 .

[38]  Dejan Mumovic,et al.  Improved benchmarking comparability for energy consumption in schools , 2014 .

[39]  Bing Liu,et al.  U.S. Department of Energy Commercial Reference Building Models of the National Building Stock , 2011 .

[40]  Eui-Jong Kim,et al.  Urban energy simulation: Simplification and reduction of building envelope models , 2014 .

[41]  Shikha Gupta,et al.  Identifying pollution sources and predicting urban air quality using ensemble learning methods , 2013 .

[42]  Steven K. Firth,et al.  INVESTIGATING CO 2 EMISSION REDUCTIONS IN EXISTING URBAN HOUSING USING A COMMUNITY DOMESTIC ENERGY MODEL , 2009 .

[43]  Tony N.T. Lam,et al.  Artificial neural networks for energy analysis of office buildings with daylighting , 2010 .

[44]  R. Britter,et al.  A resistance-capacitance network model for the analysis of the interactions between the energy performance of buildings and the urban climate , 2012 .

[45]  Fariborz Haghighat,et al.  Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network , 2010 .

[46]  Mohammad Heidarinejad,et al.  The impact of exterior surface convective heat transfer coefficients on the building energy consumption in urban neighborhoods with different plan area densities , 2015 .

[47]  Christian Inard,et al.  Fast method to predict building heating demand based on the design of experiments , 2009 .

[48]  Enrico Fabrizio,et al.  Methodologies and Advancements in the Calibration of Building Energy Models , 2015 .

[49]  John E. Taylor,et al.  Expanding Inter-Building Effect modeling to examine primary energy for lighting , 2014 .

[50]  Marjorie Musy,et al.  Microclimatic coupling as a solution to improve building energy simulation in an urban context , 2011 .

[51]  Kristina Orehounig,et al.  Integration of decentralized energy systems in neighbourhoods using the energy hub approach , 2015 .

[52]  Arno Schlueter,et al.  Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts , 2015 .

[53]  Filip Johnsson,et al.  Building-stock aggregation through archetype buildings: France, Germany, Spain and the UK , 2014 .

[54]  T. Agami Reddy,et al.  Calibrating Detailed Building Energy Simulation Programs with Measured Data—Part I: General Methodology (RP-1051) , 2007 .

[55]  Joseph Andrew Clarke,et al.  Simulation-assisted control in building energy management systems , 2002 .

[56]  Léon Bottou,et al.  Large-Scale Machine Learning with Stochastic Gradient Descent , 2010, COMPSTAT.