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结合侧壁涂装的公路隧道照明智能控制与节能分析

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为保证行车安全的同时降低隧道照明能耗,提出一种新的结合侧壁涂装的公路隧道照明智能控制方法.首先,基于隧道照明参数计算模型得到照明设计影响因素与路面平均亮度和灯具功率之间的关系;其次,以FRBF神经网络作为控制算法,以洞外亮度、车速和交通量作为输入变量,以结合侧壁涂装的灯具控制功率作为输出变量,基于隧道现场数据构建照明智能控制模型,测试结果表明其具有较好的泛化能力;再者,对该模型进行预期节能效果评价.研究结果表明:结合侧壁涂装的控制模型较一般控制模型可实现节能 9.08%,相较《公路隧道照明设计细则》(JTG/T D70/2-01-2014)的固定功率,能在满足照明需求的前提下实现节能效果最大化.该研究结果可为隧道照明设计提供理论依据.
Intelligent Control and Energy-saving Analysis of Highway Tunnel Lighting Combined with Side Wall Coating
In order to ensure driving safety and reduce tunnel lighting energy consumption,a new intelligent control method of highway tunnel lighting combined with side wall painting is proposed.Firstly the relationship between the influencing factors of lighting design,the average brightness of the road surface and the power of the luminaire is obtained,based on the calculation model of tunnel lighting parameters,.Secondly,the FRBF neural network is used as the control algorithm,the brightness outside the tunnel,the speed and the traffic volume are taken as the input variables,and the control power of the lamps combined with the side wall painting is taken as the output variables.The intelligent lighting control model is built based on the tunnel field data.The test results show that it has good generalization ability.Furthermore,the expected energy-saving effect of the model is evaluated.The results show that the control model combined with side wall painting can achieve 9.08%energy saving compared with the fixed power in the Guidelines for Design of Lighting of Highway Tunnels(JTG/TD70/2-01-2014).The energy saving effect can be maximized under the premise of meeting the lighting demand.The research results can provide theoretical basis for tunnel lighting design.

highway tunnelside wall coatingtunnel lightingintelligent controlFRBF neural network

石浩、文森、许茂林

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重庆广阳湾生态城投资发展集团有限公司,重庆 400060

招商局重庆交通科研设计院有限公司,重庆 400067

公路隧道 侧壁涂装 隧道照明 智能控制 FRBF神经网络

2024

公路交通技术
重庆交通科研设计院

公路交通技术

影响因子:0.552
ISSN:1009-6477
年,卷(期):2024.40(3)
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