Intelligent Office Lighting Control Using Natural Light and a GA-BP Neural Network-Based System
Intelligent lighting control systems are essential for regulating office illumination. Both illuminance levels and uniformity are important factors influencing the comfort of the office lighting environment. Thus, designing automatic control systems to regulate lighting is essential. This study addr...
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2024-12-01
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| Online Access: | https://www.mdpi.com/2076-3417/14/23/11344 |
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| author | Rongmeng Zhang Ruiqi Li Junbai Lu Haiqian E Haotian Wang Xinyu Zhao Yingming Gao Zhisheng Wang |
| author_facet | Rongmeng Zhang Ruiqi Li Junbai Lu Haiqian E Haotian Wang Xinyu Zhao Yingming Gao Zhisheng Wang |
| author_sort | Rongmeng Zhang |
| collection | DOAJ |
| description | Intelligent lighting control systems are essential for regulating office illumination. Both illuminance levels and uniformity are important factors influencing the comfort of the office lighting environment. Thus, designing automatic control systems to regulate lighting is essential. This study addresses the issue of natural glare by proposing a method that uses a genetic algorithm (GA) to optimize a backpropagation (BP) neural network model. The model predicts the angle of window slats, with the Sun altitude and azimuth angles as inputs, and the slat angle as the output. For artificial lighting control, a linear function is proposed to manage the relationship between work plane illuminance, natural light intensity, occupancy rates, adjacent luminaire illuminance, and the dimming factor (K). The optimal K value for each luminaire is determined using the least squares method in MATLAB. The intelligent lighting system transmits dimming factors via a ZigBee tree network structure to achieve target illuminance levels. The system’s effectiveness is validated through simulations in DIAlux software, demonstrating that the workplace illuminance in occupied areas reaches 500 lx, while, in unoccupied areas, it reaches 300 lx, with an illuminance uniformity greater than 0.7. This addresses the issue of low illuminance uniformity during daytime. Additionally, the lighting power densities (LPDs) of 1.53 W/m<sup>2</sup> and 3.8 W/m<sup>2</sup> are well below the specified threshold of 6 W/m<sup>2</sup>, indicating significant energy savings while maintaining compliance with office lighting standards. |
| format | Article |
| id | doaj-art-9b39ba0798db448097cdece4914343eb |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-9b39ba0798db448097cdece4914343eb2024-12-13T16:23:37ZengMDPI AGApplied Sciences2076-34172024-12-0114231134410.3390/app142311344Intelligent Office Lighting Control Using Natural Light and a GA-BP Neural Network-Based SystemRongmeng Zhang0Ruiqi Li1Junbai Lu2Haiqian E3Haotian Wang4Xinyu Zhao5Yingming Gao6Zhisheng Wang7Research Institute of Photonics, Dalian Polytechnic University, Dalian 116034, ChinaResearch Institute of Photonics, Dalian Polytechnic University, Dalian 116034, ChinaResearch Institute of Photonics, Dalian Polytechnic University, Dalian 116034, ChinaResearch Institute of Photonics, Dalian Polytechnic University, Dalian 116034, ChinaResearch Institute of Photonics, Dalian Polytechnic University, Dalian 116034, ChinaResearch Institute of Photonics, Dalian Polytechnic University, Dalian 116034, ChinaResearch Institute of Photonics, Dalian Polytechnic University, Dalian 116034, ChinaResearch Institute of Photonics, Dalian Polytechnic University, Dalian 116034, ChinaIntelligent lighting control systems are essential for regulating office illumination. Both illuminance levels and uniformity are important factors influencing the comfort of the office lighting environment. Thus, designing automatic control systems to regulate lighting is essential. This study addresses the issue of natural glare by proposing a method that uses a genetic algorithm (GA) to optimize a backpropagation (BP) neural network model. The model predicts the angle of window slats, with the Sun altitude and azimuth angles as inputs, and the slat angle as the output. For artificial lighting control, a linear function is proposed to manage the relationship between work plane illuminance, natural light intensity, occupancy rates, adjacent luminaire illuminance, and the dimming factor (K). The optimal K value for each luminaire is determined using the least squares method in MATLAB. The intelligent lighting system transmits dimming factors via a ZigBee tree network structure to achieve target illuminance levels. The system’s effectiveness is validated through simulations in DIAlux software, demonstrating that the workplace illuminance in occupied areas reaches 500 lx, while, in unoccupied areas, it reaches 300 lx, with an illuminance uniformity greater than 0.7. This addresses the issue of low illuminance uniformity during daytime. Additionally, the lighting power densities (LPDs) of 1.53 W/m<sup>2</sup> and 3.8 W/m<sup>2</sup> are well below the specified threshold of 6 W/m<sup>2</sup>, indicating significant energy savings while maintaining compliance with office lighting standards.https://www.mdpi.com/2076-3417/14/23/11344GA-BPfeedback adjustmentillumination uniformitylighting controlsimulationwireless sensor |
| spellingShingle | Rongmeng Zhang Ruiqi Li Junbai Lu Haiqian E Haotian Wang Xinyu Zhao Yingming Gao Zhisheng Wang Intelligent Office Lighting Control Using Natural Light and a GA-BP Neural Network-Based System Applied Sciences GA-BP feedback adjustment illumination uniformity lighting control simulation wireless sensor |
| title | Intelligent Office Lighting Control Using Natural Light and a GA-BP Neural Network-Based System |
| title_full | Intelligent Office Lighting Control Using Natural Light and a GA-BP Neural Network-Based System |
| title_fullStr | Intelligent Office Lighting Control Using Natural Light and a GA-BP Neural Network-Based System |
| title_full_unstemmed | Intelligent Office Lighting Control Using Natural Light and a GA-BP Neural Network-Based System |
| title_short | Intelligent Office Lighting Control Using Natural Light and a GA-BP Neural Network-Based System |
| title_sort | intelligent office lighting control using natural light and a ga bp neural network based system |
| topic | GA-BP feedback adjustment illumination uniformity lighting control simulation wireless sensor |
| url | https://www.mdpi.com/2076-3417/14/23/11344 |
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