高速公路隧道漫反射材料照明节能仿真

Energy Saving Simulation of Illumination of Diffuse Reflective Materials in Expressway Tunnels

当前照明节能方法无法将照明能耗控制在较低范围、稳定性系数低,提出基于神经网络的高速公路隧道漫反射材料照明节能方法。对采集到的照明节能数据进行分组,分别计算日照明耗能采样平均值、加强段照明与基本段照明全功率等效运行时间等预测相关参数,将天气因素引入照明能耗预测中,利用所得参数预测隧道漫反射材料照明能耗。将隧道漫反射材料照明能耗预测值引入照明节能控制中,得到照明子回路光通量和灯具的调光功率。将神经网络输入定义为隧道外实际检测到的亮度、隧道外交通量、隧道内灯具光通量、隧道内各段照明实际需求亮度,通过Sigmoid型函数对输入层各节点的输出实行非线性变换操作,获取高速公路隧道漫反射材料照明节能结果。实验结果表明,上述方法照明节能控制稳定性系数最高约为0. 99,照明能耗控制效果优于当前方法。所提方法具有较强的稳定性和照明能耗控制性能,可拓展性较高。

A lighting energy - saving method for diffuse reflection material of expressway tunnel based on neural network was presented. Firstly, the collected lighting energy - saving data was divided into groups. The sampling average value of daily lighting energy consumption, the full - power equivalent running time in intensive segment lighting and base segment lighting and other prediction parameters were calculated, respectively. Then, the weather factor was introduced into the prediction of lighting energy consumption and the obtained parameters were used to predict the lighting energy consumption of diffuse reflection material in the tunnel. The predicted value of lighting energy consumption of tunnel diffuse reflective material was introduced into the lighting energy saving control to get the luminous flux of lighting sub - loop and the dimming power of lamp. Moreover, the neural network input was defined as the actual brightness detected from outside of tunnel, the traffic outside the tunnel, the luminous flux inside the tunnel, the actual brightness required by each segment of tunnel. Finally, the nonlinear transformation operation was performed on the output of each node at input layer by Sigmoid function. Thus, the lighting energy saving result for diffuser material in expressway tunnel was obtained. Simulation result shows that the proposed method has the maximum stability coefficient of energy - saving control is about 0. 99, and the effect of lighting energy consumption control is better than that of current methods. The proposed method has strong stability and good performance of lighting energy consumption control. Meanwhile, it has high scalability.