Prediction and IoT Based Solar Street Lights with Intensity Control

Abstract

Street lights are an essential part of road transportation network, and huge amount of money is incurred by the government in keeping them operational at all times. The operational cost includes generation of electricity and therefore electricity is a resource that should be utilized judiciously. We propose an IOT based solar street light monitoring and controlling system to ensure, low power consumption through the automatic dimming of lights as per external lighting conditions, consumption monitoring and instant faulty light detection. Our proposed system consists of smart street lights that automatically turns on at desired intensity based on amount of lighting needed. The solar output is predicted throughout the day and adjusts the lights at desired intensity enabling the monitoring person to estimate power consumptions as per the current intensity of light this can also be extended to predict monthly power consumptions. Also, each of the unit has load sensing functionality that allows it to detect if the light has a fault. It then automatically flags that light as faulty and this data is sent over to the IOT monitoring system so that necessary action can be taken to fix it. The status of the light can be monitored through an android app.

 Keywords: IoT, Street Lights, Arduino, ESP8266, IR Sensor, LDR Sensor, Solar Panel, Decision Tree Regressor, Android, Intensity Control.