# Bottom-Up Model of Random Daily Electrical Load Curve for Office Building
## 3 Model Validation and Analysis
### Simulation Results and Analysis
#### 3.2.3 Simulation Results and Analysis  
According to the appliances selected above, the classification of appliances can be determined. The base appliances are security monitoring, water dispensers, network-related equipment, and gallery lights; single-use appliances are computers; communal appliances are printers, projectors and scanners; other appliances are elevators and office lighting. Selecting one day of the working day for electrical load simulation and obtaining the electrical load distribution as shown in Figure 10:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Printer** & **Projector** & **Scanner** & **Computer** \\ \hline standby & 10 & 10 & 10 &  
It can be seen that the distribution of the electrical load is similar to the distribution of the effective number of people, and both are bimodal distributions. During working hours, single-use appliances like computers account for most of the electrical load, and lighting accounts for a relatively high proportion. During non-working hours, since communal appliances maintain standby load when none are in use, the proportion is relatively high. Carrying out the simulation for a continuous week working day, the comparison between the simulation results and the measured results is shown in Figure 11. The figure illustrates the hourly changes in simulation and measured electrical load data during a normal working day. On the whole, the hourly load changes of the simulated value and the measured value are similar. The model reproduces the low base load at night (0:00 to 6:00) and the increase in consumption in the morning (7:00 to 9:00). The load change during working hours (from 9:00 to 18:00) is very limited, but the load value during lunch time (around 12:00 to 13:00) will decrease. In the late afternoon (after 18:00), the power consumption will drop rapidly and stabilize at the base load level. The model accurately reproduces these types of load changes. The probability density of the simulated and the measured hourly electrical load in the office building within a week are compared, as shown in Figure 12. It can be seen that  
Figure 11: Comparison result of simulated value and measured value for one consecutive week. Figure 10: Electrical load distribution of a certain working day in the office building. these two functions seem to come from the same distribution family, that is, a distribution with two different peaks. The phase difference between the low load peak and the high load peak is also detected, where the simulated value lags the measured value. The maximum, minimum, and average values of simulated value and measured value in each day of a week are compared to verify the effectiveness of the model. As shown in Table 3, the maximum relative error is 11.63%. Within the acceptable range, the simulated value has a high consistency with the measured value, so the model is more accurate. Figure 13 shows the comparison of the total load change between the simulated value and the measured value within a week. It can be seen that the load variability of the simulated value during the working day is higher than the measured value. The above shows a fitting linear regression model based on total simulation value and measured value of electrical load of a week. The linear model outputs the following statistical measures: (a) model slope coefficient value \(\beta\), (b) statistical significance \(p\)-value, (c) determination coefficient \(R^{2}\), and (d) root mean square error (RMSE). Table 4 lists the results of the regression analysis, which show the correlation between the simulated value and the measured value. Both \(\beta\) and \(R^{2}\) are high, which shows that the simulation model reproduces the changing law of the electrical load in the office building well. The RMSE is 1.558 kW, which proves that the simulated value is largely consistent with the measured value.