Updating the PECAS Modeling Framework to Include Energy Use Data for Buildings

This study investigates the consumption of electricity and natural gas for building operations for several categories of residential and non-residential buildings. The study updates the Production Exchange Consumption Allocation System (PECAS) land use modeling framework to include energy components. An energy database was assembled to study energy consumption in buildings. The authors conducted statistical analysis of utility data and estimated linear regression models to predict energy consumption in buildings. Results are validated using data from independent sources, including the California Residential Appliance Saturation Study (RASS) and the Commercial End-Use Survey (CEUS). Results are used to update PECAS and form part of the baseline study to estimate energy and greenhouse gas balances in an urban metabolism framework for the analysis of the environmental impacts of complex urban regions. The results also allow the total energy consumption and greenhouse gas emissions for residential and commercial building operations to be estimated through the application to the total residential and commercial building inventory in the region. These results are then useful for the evaluation of possible energy savings in buildings.

[1]  C. Kennedy,et al.  The Changing Metabolism of Cities , 2007 .

[2]  Ian Beausoleil-Morrison,et al.  Synthetically derived profiles for representing occupant-driven electric loads in Canadian housing , 2009 .

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

[4]  L. D. Shorrock,et al.  The physically-based model BREHOMES and its use in deriving scenarios for the energy use and carbon dioxide emissions of the UK housing stock , 1997 .

[5]  K. Steemers.  Energy and the city: density, buildings and transport , 2003 .

[6]  James Keirstead,et al.  Evaluating the applicability of integrated domestic energy consumption frameworks in the UK , 2006 .

[7]  John M. Darley,et al.  Energy conservation techniques as innovations, and their diffusion , 1978 .

[8]  S. Iniyan,et al.  A review of energy models , 2006 .

[9]  Robert Lowe,et al.  An exploration of the technical feasibility of achieving CO2 emission reductions in excess of 60% within the UK housing stock by the year 2050 , 2005 .

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

[11]  Carlos Henggeler Antunes,et al.  Energy behaviours as promoters of energy efficiency: A 21st century review , 2012 .

[12]  M. Saliari,et al.  Development of a model for urban heat island prediction using neural network techniques , 2011 .

[13]  Virginia Stovin,et al.  Green roofs; building energy savings and the potential for retrofit , 2010 .

[14]  Aris Tsangrassoulis,et al.  On the energy consumption in residential buildings , 2002 .

[15]  Maria Kolokotroni,et al.  A validated methodology for the prediction of heating and cooling energy demand for buildings within the Urban Heat Island: Case-study of London , 2010 .

[16]  Soteris A. Kalogirou,et al.  Artificial neural networks for the prediction of the energy consumption of a passive solar building , 2000 .

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

[18]  Eike Musall,et al.  Zero Energy Building A review of definitions and calculation methodologies , 2011 .

[19]  Aruna Sivakumar,et al.  Using Activity‐Based Modeling to Simulate Urban Resource Demands at High Spatial and Temporal Resolutions , 2012 .

[20]  Franco Chingcuanco,et al.  A microsimulation model of urban energy use: Modelling residential space heating demand in ILUTE , 2012, Comput. Environ. Urban Syst..

[21]  R. Judkoff,et al.  Assessment of the Technical Potential for Achieving Net Zero-Energy Buildings in the Commercial Sector , 2007 .

[22]  V. Lo Brano,et al.  Short-term prediction of household electricity consumption: Assessing weather sensitivity in a Mediterranean area , 2008 .

[23]  Luis Pérez-Lombard,et al.  A review of benchmarking, rating and labelling concepts within the framework of building energy certification schemes , 2009 .

[24]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[25]  Francis W.H. Yik,et al.  Assessing energy performance in the latest versions of Hong Kong Building Environmental Assessment Method (HK-BEAM) , 2007 .

[26]  W. F. V. Raaij,et al.  A behavioral model of residential energy use , 1983 .

[27]  Erling Holden,et al.  Household Consumption and Ecological Footprints in Norway – Does Urban Form Matter? , 2003 .