Research On Energy Saving Control System Of Intelligent Street Lamp Based On Neural Network

In the past two decades,with the rapid development of social economy and the increasing demand in the field of transportation,urban road construction has entered a period of vigorous development,and the total amount of urban roads has maintained a high-speed growth trend.With the increase of urban roads,the energy consumption caused by road lighting is increasing.In order to ensure the road safety of night travel,reduce the probability of traffic accidents,municipal departments have to light the city lights with the maximum brightness throughout the night.And blindly pursuing the lighting brightness will lead to excessive consumption of energy and increase the operating costs of the city.In order to respond to the call of national strategic goals of energy saving and emission reduction,harmonious development and sustainable development,municipal construction departments put forward the concept of intelligent city construction planning.Balancing the relationship between urban road lighting effect and city operation cost has become the focus of urban road intelligent lighting control system.Therefore,the design of an efficient,"smart" intelligent street lamp control system is the key content of intelligent city construction.This paper studies the development status of urban road lighting control systems in recent decades,and combines the existing road street lighting control systems to point out the shortcomings in the traditional road street lighting control strategies.By expounding the control principle,architecture and urban lighting standards of the intelligent road lamp control system,the key points of the intelligent,humanized and energy-saving street lamp control system architecture are designed.Research on neural network algorithm in the field of intelligent control at present,and analysis the intelligent street lamp energy-saving control system based on distributed neural network,this paper proposes an improved scheme of intelligent street lamp energy-saving control system based on centralized neural network.The neural network control system has the advantages of parallel processing,self-learning,self-organization and nonlinear mapping.Since the streetlight control system is a multivariable and complex nonlinear control system,the application of the neural network to the urban streetlight control system can meet the requirements of intelligent and energy-saving control systems.The study of street lamp control system architecture and neural network algorithm can provide an architectural and theoretical basis for the design of neural network-based intelligent street lamp control system.According to the requirements of the Standard of Urban Road Lighting Design Standard,the traffic flow,speed and luminance of street lamp are collected as sample data.The PyTorch neural network learning architecture is used to train the neural network model.The experimental results show that the neural network model training can be completed by using ReLU as the activation function.Finally,compared with the traditional street lamp control system,the neural network-based street lamp control system saves energy by 43.9%,and the centralized control method is significantly lower than the distributed control scheme in the construction cost.The intelligent and energy-saving performance index of the system is greatly improved.In order to improve the reliability of the system,the multi-sensor fusion algorithm is used to pre-process the data.