# Modelling Electricity Consumption in Cambodia Based on Remote Sensing Night-Light Images
## 5 Discussion
### Estimated Annual Electricity Consumption in Cambodia  
This study demonstrates a strong correlation between electricity consumption and total annual night-light values in Cambodia, with an adj-\(R^{2}\) of 0.97 using month-specific substitution data. This effect clearly demonstrates the feasibility of using night-light data to estimate annual electricity consumption. With the estimated electricity consumption in Cambodia from 2012 to 2019 using the exponential regression model, Table 9 was obtained.  
Table 9 shows that electricity consumption in Cambodia from 2012 to 2019 estimated from night-light data had \(RE\)-values of 0.16-10.95% with a mean of 5.47%, which is a good overall result.  
Electricity consumption and demand are influenced by several factors, which is a focus of research. Recent research on national electricity consumption and electricity demand prediction has focused on the development of complex models. [PERSON] et al.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Image Composition Method**} & \multicolumn{2}{c}{**Linear Model**} & \multicolumn{2}{c}{**Exponential Model**} & \multicolumn{2}{c}{**Power Model**} \\ \cline{2-7}  & **Adj-\(R^{2}\)** & \(p\) & **Adj-\(R^{2}\)** & \(p\) & **Adj-\(R^{2}\)** & \(p\) \\ \hline Annual average & 0.840 & 0.001 & 0.835 & 0.001 & 0.886 & 0.000 \\ Annual cloud-free average & 0.839 & 0.001 & 0.871 & 0.000 & 0.890 & 0.000 \\ Month-specific substitution & 0.908 & 0.000 & 0.967 & 0.000 & 0.936 & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results of the regression models of total night-light data vs. electricity consumption.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Year** & **Electricity Consumption (GWh)** & \(RE\) \\ \hline
2012 & 3472.81 & 1.54\% \\
2013 & 4266.32 & 5.32\% \\
2014 & 5056.83 & 7.30\% \\
2015 & 5678.27 & 5.20\% \\
2016 & 6417.40 & 10.56\% \\
2017 & 8956.86 & 10.95\% \\
2018 & 9754.11 & 0.16\% \\
2019 & 11,682.16 & 2.78\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Estimated annual electricity consumption in Cambodia, 2012-2019.  
modelled Ecuador's electricity consumption and demand until 2040 using the Long-range Energy Alternatives Planning (LEAP) system [40]. Pakistan's electricity consumption and demand were investigated by [PERSON] et al., using the LEAP system [41]. [PERSON] et al., forecasted day-ahead electricity demand with nonparametric functional models [42] and subsequently used a component-estimation technique to forecast short-term electricity demand [43]. Other researchers have recently used deep learning for energy time series forecasting [44]. With the development of night-light remote sensing technology, research on electricity consumption based on night-light data is gradually increasing. Unlike data obtained from traditional surveys, satellite imagery records data that are more objective, have wider coverage, and cost less to obtain. The use of night-light images for electricity consumption prediction only requires a simple one-dimensional regression model. The required data are easy to obtain, and the predictive accuracy is high, providing a novel and simple method of electricity consumption prediction.