Modelling Electricity Consumption in Cambodia Based on Remote Sensing Night-Light Images

The accurate estimation of electricity consumption and its spatial distribution are important in electricity infrastructural planning and the achievement of the United Nations Sustainable Development Goal 7 (SDG7). Electricity consumption can be estimated based on its correlation with nighttime ligh...

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Bibliographic Details
Published in:Applied sciences 2022-04, Vol.12 (8), p.3971
Main Authors: Gao, Xumiao, Wu, Mingquan, Gao, Ju, Han, Li, Niu, Zheng, Chen, Fang
Format: Article
Language:English
Subjects:
Cloud cover
Economic development
Electric power distribution
Electricity
Electricity consumption
estimated electricity consumption
Estimation
GDP
Gross Domestic Product
Light
Modelling
night-light image processing in cloudy areas
Remote sensing
SDG7
Sensors
Simulation
Spatial distribution
spatial patterns of electricity consumption
Sustainable development
VIIRS/DNB
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Summary:The accurate estimation of electricity consumption and its spatial distribution are important in electricity infrastructural planning and the achievement of the United Nations Sustainable Development Goal 7 (SDG7). Electricity consumption can be estimated based on its correlation with nighttime lights observed using remote sensing imagery. Since night-light images are easily affected by cloud cover, few previous studies have estimated electricity consumption in cloudy areas. Taking Cambodia as an example, the present study proposes a method for denoising night-light images in cloudy areas and estimating electricity consumption. The results show that an exponential model is superior to linear and power function models for modelling the relationship between total night-light data and electricity consumption in Cambodia. The month-specific substitution method is best for annual night-light image synthesis in cloudy areas. Cambodia’s greatest electricity consumption occurs in its four most economically developed cities. Electricity consumption spreads outwards from these cities along the main transport routes to a large number of unelectrified areas.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12083971