# Modelling Electricity Consumption in Cambodia Based on Remote Sensing Night-Light Images
## 1 Introduction  
Electricity is the most dynamic and efficient form of energy [1]. It is one of the main drivers of scientific, technological, and economic development, and is key to the quality of life and economic development in poor countries [2]. The United Nations Sustainable Development Goal 7 (SDG7) roadmap aims to provide affordable, reliable, sustainable, and modern energy services for all by 2030. To achieve this, accurate and timely estimates of electricity consumption and its spatial patterns are important for energy-efficient electricity planning in the region and for achieving SDG7. When Earth is observed from above at night, the dazzling glow of electric lights in human settlements and economic zones can be easily ascertained. When the sky is cloudless, remote sensing satellites can capture various visible sources of radiation, such as town lights, fishing boat lights, and fires. Images of the Earth's visible lights acquired at night are known as night-light images. From a historical point of view, the development of nighttime light imagery began in the 1970s with the Defense Meteorological Satellite Program (DMSP), which was carried out by the Operational Linescan System (OLS). This system was designed to enable effective weather forecasting using the night imaging capability of its embedded sensors. These sensors can effectively capture the weak moonlight reflected from cloudsand thus obtain the cloud cover distribution at night. However, it was inadvertently discovered that in the absence of clouds, these images can clearly capture light emitted by human activity on the Earth's surface. This prompted a new field of scientific research based on nighttime light data [3]. The current mainstream nighttime light data originate from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) night-lights imagery system and the DMSP/OLS [4]. It is also interesting to note that the OLS sensors on DMSP satellites can capture a variety of light spectra of different intensities, such as small residential areas, traffic lights, fires, boat lights, etc. As a result, changes in the intensity of human economic and social activities can be estimated. However, the OLS sensor acquires night-light images with a digital number (DN) range of only 0-63, which leads to the oversaturation of light values in areas with high light levels. In addition, DMSP data are provided by satellites of different ages with different sensors, leading to inconsistent data that require continuity correction. The VIIRS/DNB, which is considered a second-generation night-light imagery system, offers significant improvements in terms of extracted data quality while inheriting the characteristics of DMSP data. Moreover, the spatial resolution of VIIRS/DNB imagery is much better than that of DMSP/OLS data, allowing for more accurate representations of the nighttime lights without oversaturation issues. Many studies have explored techniques for handling these two types of night-light images and rendering them more suitable for practical use [5; 6; 7]. Unlike traditional remote sensing imagery, which is commonly used for land feature classification [8; 9; 10], the night-light imagery approach is currently widely employed for various applications, including the estimation of gross domestic product [11], populations [12], poverty [13], and carbon emissions [14; 15]. Furthermore, it has been actively used to map urbanization processes [16; 17; 18], monitor disasters [19], monitor armed conflicts [20; 21], examine ecological light pollution [22], map fires [23], monitor fisheries [24], and monitor power outages [25] and grid reliability [26]. Night-light data have been also employed to determine electrification processes [27; 28; 29]. Night-light images can reflect the density and usage of lighting infrastructure. Therefore, they can also be used as an index of electricity consumption in an area, which is a subject that has been studied extensively.