Intelligent control and energy saving evaluation of highway tunnel lighting: Based on three-dimensional simulation and long short-term memory optimization algorithm
Intelligent control and energy saving evaluation of highway tunnel lighting: Based on three-dimensional simulation and long short-term memory optimization algorithm
复制标题
公路隧道照明智能控制与节能评估：基于三维仿真和长短期记忆优化算法
DOI：
10.1016/j.tust.2020.103768
复制
发表时间：
2021-03
期刊：
影响因子：
--
通讯作者：
Cai Yang
中科院分区：
文献类型：
--
作者：
Ji;ong Zhao;Yingzi Feng;Cai Yang
传统的隧道照明亮度是根据设计小时交通量和设计速度计算的。该方法首先确定入口段的折减系数和中间段的亮度，然后通过查表法按比例计算出各段的亮度。由于设计值往往高于实际交通流量值，计算的亮度值偏高，可能导致隧道能耗。另外，该方法的设计值是分段离散的，不能满足LED照明连续调光的要求。在这种情况下，在满足运营安全的前提下，如何进行基于实际交通量值的连续智能节能控制评价，是隧道照明亟待解决的问题。and.energy本文首先提出了一种各照明段的亮度计算模型，该模型可实现连续调光。利用最小二乘法拟合折减系数和中间段亮度，求解任意车速和交通流量条件下的入口段和中间段亮度，实现亮度计算的连续优化。其次，针对传统的分级调光控制的不足，提出了一种以隧道外交通流量、速度和亮度为输入矩阵的高速公路隧道照明智能控制算法，该算法满足LSTM（Long Short-Term Memory）神经网络的要求，并结合隧道照明特点和tunnel.lighting栏目。针对LSTM优化问题的目标函数，分别引入了三种梯度下降法对模型进行优化。第三，本文采用等比例布置灯具和配光，并基于DIALux.to开发了满足运营隧道节能评估要求的隧道照明仿真环境。选取巷道亮度及其总均匀度作为节能评价指标。最后，以金顶湖2#隧道为例，建立了真实的三维隧道模型，分别在晴天和阴天两种天气条件下对智能照明控制算法进行了仿真和实验验证，验证了智能照明控制算法在三种优化器下的性能。节能评价结果表明，与传统照明设计相比，该算法在晴天可节能23.61%，在阴天可节能31.40%。
Traditional tunnel lighting luminance is calculated based on the designed hourly traffic volume and designed.speed. This approach first determined the entrance section reduction factor and middle section’s luminance.through the look-up table method, then calculated each section’s luminance in proportion. Since the designed.value is often higher than the actual traffic flow value, and the calculated brightness value is so high which might.lead to tunnel energy consumption. What’s more, the designed value in this method is the piecewise discrete.value, which does not meet the requirement of continuous dimming for LED lighting. In this case, under the.premise of satisfying operation safety, how to carry out continuous intelligent energy saving control research and.energy saving evaluation based on actual traffic value is an urgent problem to be solved in tunnel lighting. This.paper first proposed a luminance calculation model for each lighting section which can realize continuous.dimming as needed. Using the least squares method fitting the reduction coefficient and the luminance of the.middle section to solve the luminance of the entrance and middle sections under the condition of any speed and.traffic flow, and realizing the continuous optimization of the luminance calculation. Second, in terms of the.shortcomings of the traditional graded dimmer control, this paper offered a highway tunnel lighting intelligent.control algorithm with traffic flow, speed and luminance out of the tunnel as the input matrix, which meets the.requirement of LSTM (Long Short-Term Memory) neural networks, tunnel lighting characteristics and tunnel.lighting section. Considering the objective function of optimization problem in LSTM, three gradient descent.methods were introduced to optimize the model respectively. Third, this paper adopted equal proportion to.arrange lamps and light distribution, and developed a tunnel lighting simulation environment based on DIALux.to meet the energy conservation assessment requirements in operating tunnels. Roadway luminance and its total.uniformity are selected as energy saving evaluation indexes. Finally, we established a three-dimensional tunnel.model with the real environment of Jinding Lake 2# tunnel, then conducted simulations and experimental.verifications of the intelligent control algorithm under sunny and cloudy weather conditions, and verified the.performance of the intelligent lighting control algorithm under the three optimizers. The result of energy conservation.evaluations shows that compared with traditional lighting design, the tunnel lighting energy conservation.algorithm can save 23.61% of energy in sunny days and 31.40% in cloudy days.
登录
查看更多内容
影响因子：
3.6
作者：
Jiandong Zhao;Yuan Gao;Z.M. Bai;Hao Wang;Shuhan Lu
通讯作者：
Jiandong Zhao;Yuan Gao;Z.M. Bai;Hao Wang;Shuhan Lu
DOI：
10.3390/su8111092
发表时间：
2016-10
期刊：
--
影响因子：
--
作者：
F. Salata;Iacopo Golasi;Alessandro Poliziani;A. Futia;Emanuele de Lieto Vollaro;M. Coppi;A. D. L. Vollaro
通讯作者：
F. Salata;Iacopo Golasi;Alessandro Poliziani;A. Futia;Emanuele de Lieto Vollaro;M. Coppi;A. D. L. Vollaro
影响因子：
6.1
作者：
Alan T. Murray;Xin Feng
通讯作者：
Alan T. Murray;Xin Feng
DOI：
--
发表时间：
--
期刊：
--
影响因子：
--
作者：
D. L. Carní;D. Grimaldi;F. Lamonaca;L. Martirano;G. Parise
通讯作者：
D. L. Carní;D. Grimaldi;F. Lamonaca;L. Martirano;G. Parise
DOI：
10.1109/irsec.2014.7059842
发表时间：
2014-10
期刊：
2014 International Renewable and Sustainable Energy Conference (IRSEC)
影响因子：
--
作者：
Ahmed O. Abdul Salam;K. Mezher
通讯作者：
Ahmed O. Abdul Salam;K. Mezher