# Intelligent Office Lighting Control Using Natural Light and a GA-BP Neural Network-Based System
## 5 Conclusions  
This study is grounded in the guidelines established by the GB-50034 document, which outlines the standards for office lighting, including illuminance levels, uniformity, and LPD thresholds. The primary objective was to develop a dynamic, optimized lighting mathematical model that integrates natural daylight and occupancy patterns with artificial intelligence, culminating in an intelligent lighting system tailored for office environments. To address the challenge of a low illuminance uniformity caused by inadequate lighting, this paper proposes a functional model for determining the optimal angle of slats, \(\alpha\), and \(\gamma\) in window blinds.  
A key component of this study was the optimization of the BP neural network model through the application of genetic algorithms. The results from experiments indicate a strong correlation between the predicted values generated by the model and the actual observed values, demonstrating the efficacy and reliability of the proposed predictive approach.  
This study emphasizes the significance of optimizing artificial lighting to complement natural light, thereby enhancing energy savings and improving comfort in office environments. A functional model was developed to establish a relationship between user-targeted illuminance, natural light levels, occupancy, and the dimming coefficients of luminaires. This model was subsequently reformulated into a standard linear programming problem. The intelligent lighting control system was implemented utilizing MATLAB's least squares method and controlling electronic ballasts through the ZigBee wireless communication technology.  
The results demonstrated that, under the control system, the illuminance of occupied work planes reached 500 lx, while unoccupied work planes maintained levels above 300 lx. Additionally, the illuminance uniformity exceeded the required threshold of 0.7, effectively addressing the issue of the low illuminance uniformity during daytime. The LPD values were measured at 1.53 W/m2 during the day and 3.8 W/m2 at night, both well below the specified limit of 6 W/m2. Therefore, the intelligent lighting control system proposed in this paper successfully meets user comfort requirements and saves energy.  
The comfort of the lighting environment is obtained through people's subjective evaluation. In future studies, different scenes are identified, the color temperature adjustment function of brightness is increased, and the form of mixed lighting is adopted to avoid the uniformity of indoor lighting.  
Conceptualization, Z.W. and R.Z.; methodology, Z.W. and R.Z.; software, R.Z.; validation, R.L., R.Z. and H.E.; formal analysis, Z.W. and R.Z.; investigation, R.Z.; resources, Z.W.; data curation, R.Z.; writing--original draft preparation, R.Z.; writing--review and editing, Y.G. and R.Z.; visualization, R.Z.; supervision, R.Z., J.L., R.L. and H.W.; project administration, R.Z., R.L., X.Z. and Y.G.; funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript. Funding is received from the 2023 Basic Scientific Research Project of Higher Education Department of Liaoning Province (JYTMS20230404). The authors declare no conflicts of interest.