# Intelligent Office Lighting Control Using Natural Light and a GA-BP Neural Network-Based System
## 3 Intelligent Lighting Control System Design
### Algorithm Validation  
During the daytime analysis, the local area was selected for testing between 12:00 to 13:00 on sunny days, when the Sun altitude is high. During this period, the layout of illumination exhibited a gradual decrease in illumination from the window towards the deeper sections of the room. This resulted in an uneven distribution of illumination, reducing the staff's visual comfort, thereby highlighting the significance of this research.  
In this analysis, it is assumed that work planes F1 and F6 are unoccupied, yielding a target illuminance of \(\mathrm{E}_{\mathrm{u1}}=\mathrm{E}_{\mathrm{u6}}=300\) lx. Conversely, for work planes F2, F3, F4, and F5, the expected illuminance is set at \(\mathrm{E}_{02}=\mathrm{E}_{03}=\mathrm{E}_{04}=\mathrm{E}_{05}=500\) lx. In the absence of any control system, all luminaires in the office are switched on. Data collected by the lighting sensor are input into Equations (4) and (5) to determine the illuminance \(\mathrm{E}_{\mathrm{n}}\) at the work planes, as illustrated in Tables 4 and 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test** & **F1** & **F2** & **F3** \\ \hline Point & 789 & 795 & 679 \\ \hline \multirow{3}{*}{(lx)} & F4 & F5 & F6 \\ \cline{2-4}  & 809 & 802 & 669 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test point illuminance on work plane at noon in daytime mode on 15 July 2023.  
The average illuminance E\({}_{\text{all}}\) of the office, without any control system, was calculated to be 639 lx, resulting in an illuminance uniformity of 0.51. This uniformity fails to meet the lighting standard of 0.7, indicating that the distribution of light across the work planes is insufficiently balanced. Notably, the illuminance at work planes F1 and F6 reached 500 lx, despite these areas being unoccupied, thereby increasing the overall lighting energy consumption unnecessarily. Following this analysis, the target illuminance of the six work planes, denoted as S\({}_{\text{n}}\), was determined using Equations (4) and (5).  
\[S_{n}=\begin{bmatrix}243\\ 402\\ 471\\ 395\\ 398\\ 287\end{bmatrix}, \tag{10}\]  
Next, the illuminance component values of neighboring luminaires were measured. This was accomplished by switching on a single luminaire with a dimming factor of K = 1 while keeping all other luminaires switched off (with a dimming factor of K = 0). The measured results for the luminaires, as displayed in Table 6, provide insight into the illuminance contributions from each neighboring luminaire. Utilizing these values, a correlation matrix for E\({}_{\text{i}}\) (x\({}_{\text{j}}\), y\({}_{\text{j}}\)) can be constructed.  
Taking this into Equation (9) reveals that the target illuminance of each work plane is linearly related to the dimming factor and the illuminance components of neighboring luminaires. The least squares method is a highly effective approach for solving linear functions, and using MATLAB r2020a software facilitates the optimal solution of the equation. This results in the following dimming coefficients K = [0, 0.40, 0.97, 0, 0.46, 0].  
At night, the illuminance of the work plane is generated solely by the luminaires, resulting in a decreased measurement value from the lighting sensor. The measured illuminance for the office during this period was found to be 428 lx, with an illuminance uniformity of 0.48. Assuming that the occupancy conditions are similar to those previously described, the dimming coefficients were similarly calculated, yielding K = [0.34, 1, 1, 1, 0.22].