Research On Highway Tunnel Lighting Energy-saving Technologies And Control Methods

With the rapid development of transportation, the number and scale of tunnel are increasing greatly. At the same time, with the completion of the high-grade highways, management and maintenance costs also increasing. Tunnel lighting takes a large proportion in the costs of tunnel operating, and becomes a huge burden on operation departments. In the existing tunnel illumination system, waste of energy is serious, and could not fit in with the needs of safety and energy conservation. The study and improve of energy efficient lighting technologies and control methods have an effect on saving energy, so there have importance sense of carrying out research work in this area.This article, starting from the influencing factors of tunnel lighting, predicted the traffic flow in the tunnel accurately; established a tunnel lighting dimming surface based on the flow, speed, the outside brightness and other factors; and optimized for tunnel lighting illumination adjustment to ensure its optimal running state, and play a good role in saving energy; and its feasibility and validity were proved by putting the model into simulation of tunnel lighting:1) This paper probes into the various influencing factors on the highway tunnel lighting such as traffic volume, speed, and the outside brightness, especially emphasized on the predict of the important traffic flow parameter in the tunnel. Put forward the concept of pre-processing to improve the signal-to-noise ratio of the combination forecast, based on data by using the knowledge of time series analysis and machine learning. This method is applied to forecast short-term traffic flow, design a highway tunnel traffic flow forecasting model. Both from the theoretical and experimental, we can prove the method is simple, practical and good prediction accuracy. Paving the way for the follow-up work to do;2) After determining these three factors, established the model of brightness variations in the tunnel. Through the establishment of a fuzzy rule base, designed needs surfaces of tunnel lighting. So that tunnel brightness can be adjusted in real time, and achieve the purpose of energy saving;3) On the premise that the brightness requirement is met, the tunnel lighting brightness adjustment optimization problem can not only improve efficiency and saving energy, but also obtain optimal operating state. The simulation results show that the model can acquire the optimal brightness according to different illumination needs, and play a deep role in the save energy.The innovation of this article is to propose a tunneling traffic flow combination forecasting method. After a data processing, combined the traditional ARIMA forecast method and intelligent LS-SVM prediction method. They play their respective advantages and improve the prediction accuracy, and establish the optimal lighting demand model. Simplify it become a problem of solving multivariate equations. Enable them to make the optimal solution, achieve better energy-saving purpose.