Residential electricity consumption behavior: Influencing factors, related theories and intervention strategies

2018 ◽  
Vol 81 ◽  
pp. 399-412 ◽  
Author(s):  
Zhifeng Guo ◽  
Kaile Zhou ◽  
Chi Zhang ◽  
Xinhui Lu ◽  
Wen Chen ◽  
...  
2021 ◽  
Vol 257 ◽  
pp. 02011
Author(s):  
Mingliang Liang ◽  
Wenxuan Li ◽  
Jie Ji ◽  
Lili Liu ◽  
Shiying Zhang ◽  
...  

With the growth of residential electricity consumption and the development of power energy conservation, exploring the factors that affect residential electricity consumption is of great significance for promoting the sustainable development of the regional economy-power system. This paper examines the influencing factors of residential electricity consumption according to the data in 6 provinces in North China over 2008-2018, and two panels named urban panel and rural panel are constructed. Three conventional influencing factors are selected in this paper, namely, population (POP), per capita disposable income (DI) and per capita consumption expenditure (PCCE). Furthermore, considering that household characteristics have an impact on residential electricity consumption, this paper adds the number of household appliances (HA) and the per capita housing area (LS) into the factor set. Heterogeneous panel analysis techniques are applied to achieve the analysis, finding that household characteristics have significant impacts on electricity consumption of urban and rural residents, and the impact on electricity consumption of urban residents is greater than that on rural residents. Based on the empirical results, this paper puts forward several policy recommendations to effectively improve the residential electricity consumption and reduce the gap between urban and rural residential electricity consumption.


2013 ◽  
Vol 860-863 ◽  
pp. 2513-2517 ◽  
Author(s):  
Dong Xiao Niu ◽  
Ting Ting Chen ◽  
Peng Wang ◽  
Yan Chao Chen

This paper puts forward a residential electricity forecasting method based on FOAGMNN. Correlation analysis was adopted to select the key influencing factors of residential electricity forecasting. Finally, annual disposable income, population, households, per capita floor space, preceding electricity consumption are choosed as the key influencing factors. Through simulation example using the data of Hangzhou residential electricity consumption from 2000 to 2011, the results showed that the proposed model outperformed the other models and is suitable for residential electricity prediction.


Sign in / Sign up

Export Citation Format

Share Document