## 4 Experimental Results Analysis and Discussion  
### Energy Saving Rate (ESR) Calculation  
As there is a close relationship between the ESR of the proposed IoT-based intelligent LED light and the number of activations of the high-light mode per hour \(f\), this subsection will discuss the \(ESR\) and the number of triggers of the high-light mode per hour \(f\), the relationship between this two, and calculate the maximum and minimum \(ESR\).  
First, from Equation (4), it can be established that there is an inverse correlation between \(ESR\) and the \(E_{\text{ph}}\) of electric energy consumed by each proposed IoT-based intelligent LED light per hour. Equations (1) and (2), demonstrate that \(E_{\text{ph}}\) is directly proportional to the number of activations of the hourly high-light mode \(f\). Hence, it can be seen that the \(ESR\) is inversely proportional to the number of triggers of the high-light mode per hour \(f\).  
Next, we discuss the range of frequency the high-light mode is triggered per hour \(f\). The minimum value is 0, indicating that no one passes by within an hour, and the maximum value is \(3600/T\), which means that every time someone passes by is \(T\) seconds. Someone passes by again immediately, without interruption (that is, within one hour, the high-light mode is from beginning to end), so the range of \(f\) is \(0\leq f\leq 3600/T\). The default value of \(T\) for this work is 30 s, so \(0\leq f\leq 120\).  
We replace different _f_-values into the \(E_{\text{ph}}\) equation to calculate the values of \(E_{\text{py}}\), \(ESR\), \(D_{\text{py}}\), \(M_{\text{py}}\), and \(C_{\text{py}}\), compared with the general non-inductive lamp 15 W T8 LED tube, as shown in Table 7. Due to limited space, only the essential parts are listed (_f_-value interval six printed out).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(f\) & \(E_{\text{ph}}\) **(kW h)** & \(E_{\text{py}}\) **(kW h)** & _ESR (\%)_ & \(D_{\text{py}}\) **(  
Table 7 indicates that 90.00% of the maximum \(ESR\) occurs when \(f=0\) (indicating that no one passes by within 1 h). The same is true for other \(D_{\text{py}}\), \(M_{\text{py}}\), and \(C_{\text{py}}\) values. The \(ESR\) increases as the \(T\) value increases. With a linear decline, when \(f=120\) (means that people continuously pass by within 1 h), the \(ESR\) drops to 0%, but this is unlikely to happen because induction lights are usually installed in places where fewer people pass by. Figure 12 shows a graph of the relationship between the \(ESR\) and \(f\)-value.  
Figure 13 shows the measured hourly (H) \(ESR\) change chart of a parking lot. After recording the actual number of people per hour and referring to Table 4, we can obtain the \(ESR\) for that hour and draw the daily \(ESR\) change chart. Taking the first hour of Monday as an example, with \(f=1\), after referring to Table 5, we can see that the \(ESR\) is 89.25%. Some results from Figure 13a-h can be observed as follows:  
1. Most of the \(f\)-values at 0-5 o'clock every day are close to 0, so the \(ESR\) is very close to 90%, but Saturdays and Sundays are exceptions. A possible reason for this is that people are relatively late to bed because there are usually parties on weekends due to other events.
2. Most daily commuting hours or the noon hour have a high \(f\)-value, so the \(ESR\) is close to 80%, but typically people are not working on Saturdays and Sundays. Only nontime is more frequent, and the ESR value is around 80%.
3. This community's daily garbage disposal time is 22:00, so the \(f\)-value is high at 22:00 every day, and the \(ESR\) value is low, except on Sundays, because the community does not collect garbage on Sundays.  
Figure 12: The relationship between ESR and \(f\)-value.  
Figure 13: _Cont._