Big data GIS analysis for novel approaches in building stock modelling

[1]  Stefanie Hellweg,et al.  Tracking Construction Material over Space and Time: Prospective and Geo‐referenced Modeling of Building Stocks and Construction Material Flows , 2019 .

[2]  Oguz Akbilgic,et al.  An assessment framework to quantify the interaction between the built environment and the electricity grid , 2017 .

[3]  Christoph Hochenauer,et al.  Novel validated method for GIS based automated dynamic urban building energy simulations , 2017 .

[4]  Martin Raubal,et al.  GIS-based Decision Support System for Building Retrofit , 2017 .

[5]  Enrico Benetto,et al.  Life Cycle Assessment of building stocks from urban to transnational scales: A review , 2017 .

[6]  Pieter de Wilde,et al.  Predictability of occupant presence and performance gap in building energy simulation , 2017 .

[7]  Manuel Herrera,et al.  A review of current and future weather data for building simulation , 2017 .

[8]  Dirk Saelens,et al.  Heat pump and PV impact on residential low-voltage distribution grids as a function of building and district properties , 2017 .

[9]  Stefanie Hellweg,et al.  Assessing Space Heating Demand on a Regional Level: Evaluation of a Bottom�?Up Model in the Scope of a Case Study , 2017 .

[10]  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 .

[11]  Ben Koch,et al.  Heuristic approach for the economic optimisation of combined heat and power (CHP) plants: Operating strategy, heat storage and power , 2017 .

[12]  Vincent Lemort,et al.  Residential heat pump as flexible load for direct control service with parametrized duration and rebound effect , 2017 .

[13]  Jack Chin Pang Cheng,et al.  Estimation of the building energy use intensity in the urban scale by integrating GIS and big data technology , 2016 .

[14]  G. Clausen,et al.  Diurnal and seasonal variation in air exchange rates and interzonal airflows measured by active and passive tracer gas in homes , 2016 .

[15]  Gerhard Zucker,et al.  A new method for optimizing operation of large neighborhoods of buildings using thermal simulation , 2016 .

[16]  Kay W. Axhausen,et al.  The Multi-Agent Transport Simulation , 2016 .

[17]  Filip Johnsson,et al.  A differentiated description of building-stocks for a georeferenced urban bottom-up building-stock model , 2016 .

[18]  René Buffat,et al.  Feature-Aware Surface Interpolation of Rooftops Using Low-Density Lidar Data for Photovoltaic Applications , 2016, AGILE Conf..

[19]  Lieve Helsen,et al.  Comparison of load shifting incentives for low-energy buildings with heat pumps to attain grid flexibility benefits , 2016 .

[20]  Constantinos A. Balaras,et al.  Empirical assessment of calculated and actual heating energy use in Hellenic residential buildings , 2016 .

[21]  Daniel Müller,et al.  Dynamic type-cohort-time approach for the analysis of energy reductions strategies in the building stock , 2016 .

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

[23]  S. Grassi,et al.  Validation of CM SAF SARAH solar radiation datasets for Switzerland , 2015, 2015 3rd International Renewable and Sustainable Energy Conference (IRSEC).

[24]  Roberta Pernetti,et al.  Building-Stock Analysis for the Definition of an Energy Renovation Scenario on the Urban Scale , 2015 .

[25]  Pablo Sanchis,et al.  Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting , 2015 .

[26]  Alfonso P. Ramallo-González,et al.  Remote Facade Surveying of Windows Characteristics , 2015 .

[27]  York Ostermeyer,et al.  Environmental Impact of Buildings--What Matters? , 2015, Environmental science & technology.

[28]  Richard Müller,et al.  Digging the METEOSAT Treasure - 3 Decades of Solar Surface Radiation , 2015, Remote. Sens..

[29]  Peter Lund,et al.  Review of energy system flexibility measures to enable high levels of variable renewable electricity , 2015 .

[30]  Dennice F. Gayme,et al.  Grid-scale energy storage applications in renewable energy integration: A survey , 2014 .

[31]  F. Stazi,et al.  Estimating energy savings for the residential building stock of an entire city: A GIS-based statistical downscaling approach applied to Rotterdam , 2014 .

[32]  Juha Jokisalo,et al.  Calculation method and tool for assessing energy consumption in the building stock , 2014 .

[33]  C. Frei.  Interpolation of temperature in a mountainous region using nonlinear profiles and non‐Euclidean distances , 2014 .

[34]  Pascal Neis,et al.  Quality assessment for building footprints data on OpenStreetMap , 2014, Int. J. Geogr. Inf. Sci..

[35]  M. Larrañeta,et al.  Analysis of the Distribution of Measured and Synthetic DNI Databases and its Effect on the Expected Production of a Parabolic Trough Plant , 2014 .

[36]  G. Bekö,et al.  Indoor air quality in the Swedish housing stock and its dependence on building characteristics , 2013 .

[37]  A. Alessandri,et al.  Electricity demand forecasting over Italy: Potential benefits using numerical weather prediction models , 2013 .

[38]  Sylvain Robert,et al.  State of the art in building modelling and energy performances prediction: A review , 2013 .

[39]  S. Hellweg,et al.  Housing and mobility demands of individual households and their life cycle assessment. , 2013, Environmental science & technology.

[40]  Niko Heeren,et al.  A component based bottom-up building stock model for comprehensive environmental impact assessment and target control , 2013 .

[41]  M. Journée,et al.  Evaluation of different models to estimate the global solar radiation on inclined surfaces , 2013 .

[42]  Claudio Nägeli,et al.  SPATIAL BUILDING STOCK MODELLING TO ASSESS ENERGY- EFFICIENCY AND RENEWABLE ENERGY IN AN URBAN CONTEXT , 2013 .

[43]  Filip Johnsson,et al.  A modelling strategy for energy, carbon, and cost assessments of building stocks , 2013 .

[44]  Mark Jennings,et al.  A review of urban energy system models: Approaches, challenges and opportunities , 2012 .

[45]  C. Dimitroulopoulou.  Ventilation in European dwellings: A review , 2012 .

[46]  Dejan Mumovic,et al.  A review of bottom-up building stock models for energy consumption in the residential sector , 2010 .

[47]  V. Ismet Ugursal,et al.  Modeling of end-use energy consumption in the residential sector: A review of modeling techniques , 2009 .

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

[49]  J. Jenness.  Calculating landscape surface area from digital elevation models , 2004 .

[50]  P. Ineichen,et al.  A new operational model for satellite-derived irradiances: description and validation , 2002 .

[51]  M. Goodchild,et al.  Geographic Information Systems and Science (second edition) , 2001 .

[52]  Baruch Givoni,et al.  Climate considerations in building and urban design , 1998 .

[53]  David E. Burmaster,et al.  Residential Air Exchange Rates in the United States: Empirical and Estimated Parametric Distributions by Season and Climatic Region , 1995 .

[54]  P. Ineichen,et al.  A new simplified version of the perez diffuse irradiance model for tilted surfaces , 1987 .

[55]  Berthold K. P. Horn,et al.  Hill shading and the reflectance map , 1981, Proceedings of the IEEE.

[56]  W. Beckman,et al.  Solar Engineering of Thermal Processes , 1985 .

[57]  Fred J. Damerau,et al.  A technique for computer detection and correction of spelling errors , 1964, CACM.

[58]  J. Munkres.  ALGORITHMS FOR THE ASSIGNMENT AND TRANSIORTATION tROBLEMS* , 1957 .