## 3 Data Analysis
### Temperature's Effect on Load  
Multiple studies show that electricity demand is highly affected by variations due to various environmental factors [22; 24; 30]. Among multiple indicators, this paper focuses on load variation due to temperature. Figure 5a shows the relation between the electricity demand and temperature for the case being studied. From Figure 5b, a seasonal variation of the temperature and its effect on the load can be observed. Based on the plot, the positive as well as the negative effects on the load can be observed. From the first month of the year to the fourth month, the temperature had a positive impact on the load. As summer in Nepal starts in late March and lasts until early October, this indicates an increase in the use of cooling appliances as the mean temperature reached 25 \({}^{\circ}\)C, with maximum temperatures close to 35 \({}^{\circ}\)C. This showed the first spike in electricity demand up to 1.4 MW on average in the area. Likewise, when the year entered the winter season with a mean temperature of 2.5 \({}^{\circ}\)C, a second spike in demand occurred of up to 1.7 MW on average. Thus, the minimum temperature could go below 0 \({}^{\circ}\)C, and higher use of heating appliances could be observed throughout that period. For observing a mean based correlation plot of rgw monthly mean temperature with the mean load, Figure 5c shows a nonlinear parabolic relation between them. Based on the relation plot of rgw temperature and load, Baneshwor has a base temperature region between 10 and 15 \({}^{\circ}\)C. Such an increase or decrease in temperature from this region triggers a rise in electricity demand. The base temperature allows for the calculation of the hot degree days and cold degree days [31], and a study conducted for the load in South Korea showed the difference in sensitivity of the load with hot degree days and cold degree days. The results showed that the load response was larger during hot days than during cold days. The variation in electricity demand around the  
Figure 4: Box plot of electricity demand for each hour.  
Figure 3: (**a**) Boxplot of electricity demand for each day. (**b**) Weekdays vs. weekends.  
base temperature shows the dependency of the load on the air temperature. A nonlinear regression plot of the electric load of Baneshwor showed a parabolic nonlinear relation with the temperature, as can be seen in Figure 5a. In observing the change in demand below and above the base temperature region, the cold temperatures had a greater effect on the load than the hot temperatures. The rate of demand variation per degree of temperature was 0.07 MW/\({}^{\circ}\)C during the cold degree days and 0.02 MW/\({}^{\circ}\)C during the hot degree days. Thus, the sensitivity of the electricity demand with respect to the temperature was greater in the winter than in the summer. This is due to the power demand gap between heating and cooling appliances, since in developing countries, heaters are the common mode of heating, and fans are used for cooling purposes; very few households rely on air conditioning systems to maintain their indoor temperatures. Similarly, every household owns their own personal boiler and electric geyser for hot water, which further increases the load.