Comparative Analysis of Machine Learning Algorithms for Building Archetypes Development in Urban Building Energy Modeling

2018 Building Performance Modeling Conference and SimBuild co-organized by ASHRAE and IBPSA-USA Chicago, IL, 26-28 September 2018

[1]  S. C. Johnson.  Hierarchical clustering schemes , 1967, Psychometrika.

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

[3]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[4]  Sinan Saraçli,et al.  Comparison of hierarchical cluster analysis methods by cophenetic correlation , 2013, Journal of Inequalities and Applications.

[5]  S. Corgnati,et al.  Use of reference buildings to assess the energy saving potentials of the residential building stock: the experience of TABULA Project , 2014 .

[6]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

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

[8]  James O'Donnell,et al.  Definition of a useful minimal-set of accurately-specified input data for Building Energy Performance Simulation , 2018 .

[9]  Fu Xiao,et al.  A short-term building cooling load prediction method using deep learning algorithms , 2017 .

[10]  Enedir Ghisi,et al.  Method for obtaining reference buildings , 2016 .

[11]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[12]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Manuel Duarte Pinheiro,et al.  A Portuguese approach to define reference buildings for cost-optimal methodologies , 2015 .

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

[15]  Paul Strachan,et al.  Developing archetypes for domestic dwellings: An Irish case study , 2012 .

[16]  Hans-Peter Kriegel,et al.  LOF: identifying density-based local outliers , 2000, SIGMOD '00.

[17]  Henk Visscher,et al.  Theoretical vs. actual energy consumption of labelled dwellings in the Netherlands: Discrepancies and policy implications , 2013 .

[18]  Usman Ali,et al.  Households electricity consumption analysis with data mining techniques , 2016, IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society.

[19]  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 .

[20]  Paulo Ferrão,et al.  A METHOD FOR THE GENERATION OF MULTI-DETAIL BUILDING ARCHETYPE DEFINITIONS: APPLICATION TO THE CITY OF LISBON , 2015 .

[21]  Andrea Gasparella,et al.  Selection of Representative Buildings through Preliminary Cluster Analysis , 2014 .

[22]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[23]  Guglielmina Mutani,et al.  GIS-Based Energy Consumption Model at the Urban Scale for the Building Stock , 2016 .