Identifying key variables and interactions in statistical models of building energy consumption using regularization

Hsu, David

doi:10.1016/j.energy.2015.02.008

Title: Identifying key variables and interactions in statistical models of building energy consumption using regularization

Journal Article · Wed Apr 01 00:00:00 EDT 2015 · Energy

DOI:https://doi.org/10.1016/j.energy.2015.02.008· OSTI ID:1209829

View Journal Article

Cite

Export

Save

Sponsoring Organization:: USDOE

Grant/Contract Number:: EE0004261

OSTI ID:: 1209829

Journal Information:: Energy, Journal Name: Energy Vol. 83 Journal Issue: C; ISSN 0360-5442

Publisher:: ElsevierCopyright Statement

Country of Publication:: United Kingdom

Language:: English

Citation Metrics:

Cited by: 61 works

Citation information provided by
Web of Science

References (42)

A review on the prediction of building energy consumption Zhao, Hai-xiang; Magoulès, Frédéric Renewable and Sustainable Energy Reviews, Vol. 16, Issue 6 https://doi.org/10.1016/j.rser.2012.02.049	journal	August 2012
A decoupled whole-building simulation engine for rapid exhaustive search of low-carbon and low-energy building refurbishment options Rysanek, A. M.; Choudhary, R. Building and Environment, Vol. 50 https://doi.org/10.1016/j.buildenv.2011.09.024	journal	April 2012
Unequal access to energy efficiency in US multifamily rental housing: opportunities to improve Pivo, Gary Building Research & Information, Vol. 42, Issue 5 https://doi.org/10.1080/09613218.2014.905395	journal	May 2014
Building energy parameter investigations based on multivariate analysis Olofsson, Thomas; Andersson, Staffan; Sjögren, Jan-Ulric Energy and Buildings, Vol. 41, Issue 1 https://doi.org/10.1016/j.enbuild.2008.07.012	journal	January 2009
Development of an RDP neural network for building energy consumption fault detection and diagnosis Magoulès, Frédéric; Zhao, Hai-xiang; Elizondo, David Energy and Buildings, Vol. 62 https://doi.org/10.1016/j.enbuild.2013.02.050	journal	July 2013
Review of building energy-use performance benchmarking methodologies Chung, William Applied Energy, Vol. 88, Issue 5 https://doi.org/10.1016/j.apenergy.2010.11.022	journal	May 2011
Modeling of end-use energy consumption in the residential sector: A review of modeling techniques Swan, Lukas G.; Ugursal, V. Ismet Renewable and Sustainable Energy Reviews, Vol. 13, Issue 8, p. 1819-1835 https://doi.org/10.1016/j.rser.2008.09.033	journal	October 2009
What are we doing here? Analyzing fifteen years of energy scholarship and proposing a social science research agenda Sovacool, Benjamin K. Energy Research & Social Science, Vol. 1 https://doi.org/10.1016/j.erss.2014.02.003	journal	March 2014
A Sparse-Group Lasso Simon, Noah; Friedman, Jerome; Hastie, Trevor Journal of Computational and Graphical Statistics, Vol. 22, Issue 2 https://doi.org/10.1080/10618600.2012.681250	journal	April 2013
Big meter data analysis of the energy efficiency potential in Stockholm's building stock Shahrokni, Hossein; Levihn, Fabian; Brandt, Nils Energy and Buildings, Vol. 78 https://doi.org/10.1016/j.enbuild.2014.04.017	journal	August 2014
Stable feature selection for biomarker discovery He, Zengyou; Yu, Weichuan Computational Biology and Chemistry, Vol. 34, Issue 4 https://doi.org/10.1016/j.compbiolchem.2010.07.002	journal	August 2010
Identifying important variables of energy use in low energy office building by using multivariate analysis Djuric, Natasa; Novakovic, Vojislav Energy and Buildings, Vol. 45 https://doi.org/10.1016/j.enbuild.2011.10.031	journal	February 2012
Handling uncertainty in housing stock models Booth, A. T.; Choudhary, R.; Spiegelhalter, D. J. Building and Environment, Vol. 48 https://doi.org/10.1016/j.buildenv.2011.08.016	journal	February 2012
State of the art in building modelling and energy performances prediction: A review Foucquier, Aurélie; Robert, Sylvain; Suard, Frédéric Renewable and Sustainable Energy Reviews, Vol. 23 https://doi.org/10.1016/j.rser.2013.03.004	journal	July 2013
The composite absolute penalties family for grouped and hierarchical variable selection Zhao, Peng; Rocha, Guilherme; Yu, Bin The Annals of Statistics, Vol. 37, Issue 6A https://doi.org/10.1214/07-AOS584	journal	December 2009
Calibration and uncertainty analysis for computer models – A meta-model based approach for integrated building energy simulation Manfren, Massimiliano; Aste, Niccolò; Moshksar, Reza Applied Energy, Vol. 103 https://doi.org/10.1016/j.apenergy.2012.10.031	journal	March 2013
Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques Fan, Cheng; Xiao, Fu; Wang, Shengwei Applied Energy, Vol. 127 https://doi.org/10.1016/j.apenergy.2014.04.016	journal	August 2014
Improving energy benchmarking with self-reported data Hsu, David Building Research & Information, Vol. 42, Issue 5 https://doi.org/10.1080/09613218.2014.887612	journal	February 2014
Feature Selection for Predicting Building Energy Consumption Based on Statistical Learning Method Zhao, Hai-Xiang; Magoulès, Frédéric Journal of Algorithms & Computational Technology, Vol. 6, Issue 1 https://doi.org/10.1260/1748-3018.6.1.59	journal	March 2012
Calibrating whole building energy models: Detailed case study using hourly measured data Raftery, Paul; Keane, Marcus; Costa, Andrea Energy and Buildings, Vol. 43, Issue 12 https://doi.org/10.1016/j.enbuild.2011.09.039	journal	December 2011
Beyond the technical: a snapshot of energy and buildings research Schweber, Libby; Leiringer, Roine Building Research & Information, Vol. 40, Issue 4 https://doi.org/10.1080/09613218.2012.675713	journal	July 2012
Regularization and variable selection via the elastic net Zou, Hui; Hastie, Trevor Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 67, Issue 2 https://doi.org/10.1111/j.1467-9868.2005.00503.x	journal	April 2005
A review on buildings energy consumption information Pérez-Lombard, Luis; Ortiz, José; Pout, Christine Energy and Buildings, Vol. 40, Issue 3 https://doi.org/10.1016/j.enbuild.2007.03.007	journal	January 2008
Validation of building energy modeling tools under idealized and realistic conditions Ryan, Emily M.; Sanquist, Thomas F. Energy and Buildings, Vol. 47 https://doi.org/10.1016/j.enbuild.2011.12.020	journal	April 2012
Calibration of building energy models for retrofit analysis under uncertainty Heo, Y.; Choudhary, R.; Augenbroe, G. A. Energy and Buildings, Vol. 47 https://doi.org/10.1016/j.enbuild.2011.12.029	journal	April 2012
Model selection and estimation in regression with grouped variables Yuan, Ming; Lin, Yi Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 68, Issue 1 https://doi.org/10.1111/j.1467-9868.2005.00532.x	journal	February 2006
Two-to-one discrepancy between measured and predicted performance of a ‘low-energy’ office building: insights from a reconciliation based on the DOE-2 model Norford, L. K.; Socolow, R. H.; Hsieh, E. S. Energy and Buildings, Vol. 21, Issue 2 https://doi.org/10.1016/0378-7788(94)90005-1	journal	January 1994
Building commissioning: a golden opportunity for reducing energy costs and greenhouse gas emissions in the United States Mills, Evan Energy Efficiency, Vol. 4, Issue 2 https://doi.org/10.1007/s12053-011-9116-8	journal	February 2011
Contrasting the capabilities of building energy performance simulation programs Crawley, Drury B.; Hand, Jon W.; Kummert, Michaël Building and Environment, Vol. 43, Issue 4, p. 661-673 https://doi.org/10.1016/j.buildenv.2006.10.027	journal	April 2008
A framework for simulation-based real-time whole building performance assessment Pang, Xiufeng; Wetter, Michael; Bhattacharya, Prajesh Building and Environment, Vol. 54 https://doi.org/10.1016/j.buildenv.2012.02.003	journal	August 2012
Regression shrinkage and selection via the lasso: a retrospective: Regression Shrinkage and Selection via the Lasso Tibshirani, Robert Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 73, Issue 3 https://doi.org/10.1111/j.1467-9868.2011.00771.x	journal	April 2011
A lasso for hierarchical interactions Bien, Jacob; Taylor, Jonathan; Tibshirani, Robert The Annals of Statistics, Vol. 41, Issue 3 https://doi.org/10.1214/13-AOS1096	journal	June 2013
Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy Jain, Rishee K.; Smith, Kevin M.; Culligan, Patricia J. Applied Energy, Vol. 123 https://doi.org/10.1016/j.apenergy.2014.02.057	journal	June 2014
Stabilization Wedges: Solving the Climate Problem for the Next 50 Years with Current Technologies Pacala, S. Science, Vol. 305, Issue 5686 https://doi.org/10.1126/science.1100103	journal	August 2004
The gap between predicted and measured energy performance of buildings: A framework for investigation de Wilde, Pieter Automation in Construction, Vol. 41 https://doi.org/10.1016/j.autcon.2014.02.009	journal	May 2014
Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso) Wainwright, Martin J. IEEE Transactions on Information Theory, Vol. 55, Issue 5 https://doi.org/10.1109/TIT.2009.2016018	journal	May 2009
Optimum building energy retrofits under technical and economic uncertainty Rysanek, A. M.; Choudhary, R. Energy and Buildings, Vol. 57 https://doi.org/10.1016/j.enbuild.2012.10.027	journal	February 2013
Near-ideal model selection by ℓ1 minimization Candès, Emmanuel J.; Plan, Yaniv The Annals of Statistics, Vol. 37, Issue 5A https://doi.org/10.1214/08-AOS653	journal	October 2009
A review of bottom-up building stock models for energy consumption in the residential sector Kavgic, M.; Mavrogianni, A.; Mumovic, D. Building and Environment, Vol. 45, Issue 7 https://doi.org/10.1016/j.buildenv.2010.01.021	journal	July 2010
Stability selection: Stability Selection Meinshausen, Nicolai; Bühlmann, Peter Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 72, Issue 4 https://doi.org/10.1111/j.1467-9868.2010.00740.x	journal	July 2010
Thermal comfort and building energy consumption implications – A review Yang, Liu; Yan, Haiyan; Lam, Joseph C. Applied Energy, Vol. 115 https://doi.org/10.1016/j.apenergy.2013.10.062	journal	February 2014
Energy intelligent buildings based on user activity: A survey Nguyen, Tuan Anh; Aiello, Marco Energy and Buildings, Vol. 56, p. 244-257 https://doi.org/10.1016/j.enbuild.2012.09.005	journal	January 2013

Similar Records

Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters

Journal Article · Wed Feb 01 00:00:00 EST 2023 · Renewable and Sustainable Energy Reviews · OSTI ID:1209829

Kapp, Sean; Choi, Jun-Ki; Hong, Taehoon

Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data

Journal Article · Tue Dec 01 00:00:00 EST 2015 · Applied Energy · OSTI ID:1209829

Hsu, David

Impacts of building operational schedules and occupants on the lighting energy consumption patterns of an office space

Journal Article · Wed Dec 28 00:00:00 EST 2016 · Building Simulation · OSTI ID:1209829

Delgoshaei, Payam; Heidarinejad, Mohammad; Xu, Ke; +3 more

Title: Identifying key variables and interactions in statistical models of building energy consumption using regularization

Citation Formats

References (42)

Similar Records

Related Subjects