## 3 Research Results
### Development of Prediction Method for CO\({}_{2}\) Emission Factor by Time of the Day  
In calculating the CO\({}_{2}\) emission factor by time of the day, this study analyzed the supply and demand performance of power company A (April 2016 to September 2020). Figure 14 shows the actual power supply on 1 April 2020, as an example of the breakdown of the power supply. Power company A is one of the largest power generation companies in Japan and owns multiple power sources. Therefore, in calculating the CO\({}_{2}\) emission factor by time of the day, the CO\({}_{2}\) emission factor at the transmission end of each power source is shown in Table 8. The emission factor of thermal power generation was calculated based on the composition ratio of coal and petroleum liquid natural gas (LNG). In addition, core emission factors and adjusted emission factors are relevant when calculating the CO\({}_{2}\) emission factor. However, since this section does not evaluate the environmental value of power company A, this study uses the concept of core emission factors. The calculation method of the core emission factor is shown in Equation (4). From the actual data on area  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Weather** & **Sunny** & **Cloudy** & **Rainy** & **Cloudy but Occasionally Sunny** \\ \hline \multirow{6}{*}{Actual weather} & \multicolumn{4}{c}{A little cloudy} \\  & Sunny & A little cloudy at one point & Rainy at one point & \\ \cline{1-1}  & Clear at one point & A little cloudy in the morning & Rainy in the morning & Cloudy \\ \cline{1-1}  & Sunny in the morning & Sometimes a little cloudy & Sometimes raining & Sometimes raining & \\ \cline{1-1}  & Sometimes sunny & Cloudy at one point & Heavy rain & \\ \cline{1-1}  & Cloudy in the morning & Cloudy sometimes raining & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Weather classification.  
Figure 13: Comparison of solar radiation prediction methods.  
supply and demand, the CO2 emission factor in power company A for each time point was calculated using Equation (5).  
\[CEF=\frac{Q}{E} \tag{4}\]  
where \(CEF\) is the core emission factor (t-CO2/kWh), \(Q\) is the core CO2 emission amount (t-CO2), and \(E\) is the electric power sold (kWh).  
\[p=\frac{\sum t_{i}\cdot u_{i}}{k} \tag{5}\]  
where \(p\) is the CO2 emission factor by time of the day (kg-CO2/kWh), \(t_{i}\) is the amount of power generated by each power source (MWh), \(u_{i}\) is the CO2 emission factor of each power source (kg-CO2/kWh), and \(k\) is the area demand (MWh).  
It was found that only the solar power generation fluctuated significantly in the power supply composition by season and time of the day. Based on this finding, multiple regression analysis was performed to correct the correlation between solar power generation and CO2 emission factor by time of the day for power company A. As shown in Figure 15,there was a high correlation (R2 = 0.8115) between the coefficient of determination of solar power generation amount and CO2 emission factor with time of the day. Therefore, the present study decided to predict the CO2 emission factor by time of the day after predicting the solar power generation amount of power company A using the weather forecast of Nagoya, which is the power generation area of power company A.  
Figure 14: Power generation record of electric power company A.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Power Plant** & **CO2 Emission Factor, kg-CO2/kWh** \\ \hline Thermal power plant & 0.4997 \\ Solar power & 0.038 \\ Wind farm & 0.026 \\ Hydroelectric power plant & 0.011 \\ Adjustments (e.g., FIT) & 0.490 \\ Pumped-storage hydropower & 0.011 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The CO2 emission factor of each power source.