Research On Energy Consumption Statistical Modeling Of Intelligent Street Lamp Control System

With the continuous increase of the number and mileage of roads in China,the total energy consumption of road lighting has increased year by year;however,China lacks resources.Therefore,how to reduce the energy consumption of road lighting while ensuring the brightness of road lighting has become one of the research topics for Chinese scholars.Based on the statistics of 2019,the annual power consumption of street lamps accounts for 30% of lighting consumption in China,10% of the total power consumption of the whole country,which is equivalent to the annual power generation of the Three Gorges Hydroelectric Power Station.Nevertheless,about 35% of the electric energy is wasted every year.It can be seen from the other perspective that there is potential in energy conservation with regard to road lighting.On this premise,the paper adopts three different lighting modes as the research object,and examines the energy consumption of street lamps of different brightness and lighting rate by means of on-site measurement,and discovers the lighting mode with the lowest energy consumption.Lastly,with the energy consumption of street lamps in Ankang Road from 22:00 to 1:00 a.m.as the research object,the ARIMA model and the wavelet ARIMA model were used to analyze and predict the energy consumption of street lamps in certain modes.This paper draws the following three conclusions:(1)Dimming mode and interval mode can reduce the energy consumption of road lighting,but with different reduction proportion,the reduction range of different street lamps(ordinary street lamps and LED)of the same mode is different;(2)If the road is illuminated by ordinary street lamps,and the interval lighting mode is adopted,the brightness of the street lamps may drop by 30% or40% in the middle of the night(depending on the flow of local people);If the road is illuminated by LED lights,and the interval lighting mode is chosen,a brightness reduction of 10%,30% or 40% can be considered according to the local reality;(3)The fitting and prediction ability of the wavelet ARIMA model for energy consumption sequences are higher than those of the ARIMA model.