# Modelling Electricity Consumption in Cambodia Based on Remote Sensing Night-Light Images
## 1 Introduction
Indeed, linear correlations between electricity consumption and light intensity (from DMSP/OLS night-light images) have been reported in a dozen Asian countries, providing preliminary evidence of the feasibility of modelling electricity consumption based on night-light data [30]. [PERSON] et al., simulated both the spatial and temporal patterns of total electricity consumption in India from 1993 to 2002 based on the correlation between light intensity from DMSP/OLS night-light images and electricity consumption [31]. Researchers using night-light imagery for electricity consumption estimates often construct one-dimensional regression models, such as linear regression, power function regression, or exponential regression, to relate electricity consumption to the total night-light value of images. A researcher will choose the regression model with the highest \(R^{2}\)-value to estimate electricity consumption. Early studies have tended to use DMSP/OLS night-light imagery due to its availability, which covers the time period of 1992-2013. With the advent of better quality VIIRS/DNB night-light images in 2012, researchers have increasingly used these images for their studies. In another interesting work, [PERSON] et al., studied the boundaries and electricity consumption of 12 cities in China using VIIRS/DNB night-light data. The results revealed that the VIIRS/DNB method is more accurate than the DMSP/OLS approach for estimating the total electricity consumption, based on simulations [32]. It has been also been found that VIIRS/DNB images, with their higher quality data, are more accurate in simulating electricity consumption [33; 34]. Both DMSP/OLS and VIIRS/DNB night-light images require denoising, the quality of which has a direct impact on subsequent analyses. However, previous simulations of electricity consumption based on night-light data have mainly focused on areas with low cloudiness. Associated denoising methods also focus on less cloudy areas, whereas few studies have been conducted on cloudy areas. In the denoising of cloudy areas, common denoisingmethods can mask useful information, such that the denoised images do not reflect the true night-light conditions. In response to the various problems encountered in previous studies, this study proposes a new method for denoising and making annual image syntheses of night-light images in cloudy areas. The country of Cambodia is used as an example, and VIIRS/DNB night-light images with high accuracy were used to simulate electricity consumption. Multiple one-dimensional regression models were used to explore the relationship between annual electricity consumption and total night-light values in Cambodia. The best model was used to estimate the annual electricity consumption in Cambodia and the changes in its spatial distribution pattern. Hence, a new method of annual electricity consumption estimation and spatial distribution modelling in Cambodia is provided. This work also provides a methodological reference for using night-light imagery to estimate annual electricity consumption in cloudy areas. The results are anticipated to contribute to the planning of electricity infrastructure in Cambodia and the realisation of SDG7.