Estimation of Residential Energy Consumption Accounting for Occupant-Behavior and Building Characteristics: A Coupled Statistical Approach with Reduced Survey and Simulation Tool

2021 ◽  
pp. 164-171
Author(s):  
Jinela González ◽  
Miguel Chen Austin ◽  
Dafni Mora
Keyword(s):  
Energy Consumption ◽  
Statistical Approach ◽  
Simulation Tool ◽  
Occupant Behavior ◽  
Residential Energy ◽  
Residential Energy Consumption ◽  
Building Characteristics

A method for modeling household occupant behavior to simulate residential energy consumption

ISGT 2014 ◽  
2014 ◽  
Author(s):  
Brandon J. Johnson ◽  
Michael R. Starke ◽  
Omar A. Abdelaziz ◽  
Roderick K. Jackson ◽  
Leon M. Tolbert
Keyword(s):  
Energy Consumption ◽  
Occupant Behavior ◽  
Residential Energy ◽  
Residential Energy Consumption

scholarly journals Spatiotemporal Patterns and Influencing Mechanism of Urban Residential Energy Consumption in China

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3864
Author(s):  
Qiucheng Li ◽  
Jiang Hu ◽  
Bolin Yu
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Decomposition Analysis ◽  
Theil Index ◽  
Residential Energy ◽  
Residential Energy Consumption ◽  
Index Method ◽  
Inequality Measures ◽  
Influencing Mechanism ◽  
Per Capita

The residential sector has become the second largest energy consumer in China. Urban residential energy consumption (URE) in China is growing rapidly in the process of urbanization. This paper aims to reveal the spatiotemporal dynamic evolution and influencing mechanism of URE in China. The spatiotemporal heterogeneity of URE during 2007–2018 is explored through Kernel density estimation and inequality measures (i.e., Gini coefficient, Theil index, and mean logarithmic deviation). Then, with several advantages over traditional index decomposition analysis approaches, the Generalized Divisia Index Method (GDIM) decomposition is employed to investigate the impacts of eight driving factors on URE. Furthermore, the national and provincial decoupling relationships between URE and residential income increase are studied. It is found that different provinces’ URE present a significant agglomeration effect; the interprovincial inequality in URE increases and then decreases during the study period. The GDIM decomposition results indicate the income effect is the main positive factor driving URE. Besides, urban population, residential area, per capita energy use, and per unit area energy consumption positively influence URE. By contrast, per capita income, energy intensity, and residential density have negative effects on URE. There is evidence that only three decoupling states, i.e., weak decoupling, strong decoupling, and expansive negative decoupling, appear in China during 2007–2018. Specifically, weak decoupling is the dominant state among different regions. Finally, some suggestions are given to speed up the construction of energy-saving cities and promote the decoupling process of residential energy consumption in China. This paper fills some research gaps in urban residential energy research and is important for China’s policymakers.

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