• Vandana Rupnar
• Vaishnavi Ghawate
• Avani Mande
• Sharanya Nair
• Sanjana Jangnure

It has become extremely necessary to reduce the consumption of electricity due to the increasing needs and limited resources for its generation. Streetlights place a heavy demand on electricity usage. Conventionally, the streetlights remain lit during their operational hours even when they are not required or when full brightness is not necessary; for example, during periods of low traffic volume or even during summer season. Thus, the focus of this system is to control the intensity and pattern of street lights. This controlling will be done with the help of ML algorithms used for recognising the need of electricity for a particular area depending on given input parameters like traffic volume of that area, climate in the area, time and day of the week. Use of this system will not just save electricity but ensure the presence of required light intensity for safety purposes. There is a need to just connect the existing streetlights with the proposed system thus, reducing the cost of investment like building all together new street lights or even installation of sensors. The system will operate on its own without the need of human intervention, though a special access is granted to the administrator.

Machine Learning, Adaptive lighting , energy consumption

"Energy efficient Street Lighting System Using ML", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 12, page no.251-254, December-2018, Available :http://www.jetir.org/papers/JETIR1812733.pdf

"Energy efficient Street Lighting System Using ML", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 12, page no. pp251-254, December-2018, Available at : http://www.jetir.org/papers/JETIR1812733.pdf