Artificial Neural Network based Smart and Energy Efficient Street Lighting System: A Case Study for Residential area in Hosur

•Street lighting in smart cities and its efficient use to reduce the power consumption were presented.•Street lighting information analysis made on real time data collected from the residential area.•Fuzzy Logic Controller used in efficient decision making process for demand based utilisation.•We reduced the utilisation of street lights by 34% and power consumption rate of 13.5%. Smart city is the term described to integrate all facilities to the people in a frequently accessible manner. Street lighting system is one of the part of the facility provided in smart cities. The unwanted utilisation of the street lighting affects the economic status of the country indirectly. Power consumption through street lighting is major problem, hence action plan is taken to promote the reduction policies of the power consumption. Reducing the unnecessary power consumption is not a simple task, but with soft computing approaches power consumption can be reduced. The objective of this article is to present an ANN based energy efficient smart street lighting systems. The proposed design were implemented and executed in a residential area, Hosur and the results are carried out at different scenarios and various seasons. The decision making module exploits the analysis factors obtained via lighting sensor, motion sensor, PIR sensor, etc. artificial neural network and fuzzy logic controller makes an efficient decision making process for demand based utilisation and to avoid the unnecessary utilisation of street lights. The five levels of scenarios are tested and implemented in a real time. Through this work, the smart and energy efficiency street lighting system reduced the unwanted utilisation by 34% and reduced the power consumption rate of 13.5%.