• Prof. Prashant Ahire
• Prof. Atul Deshpande
• Dhruv Bhosle
• Mrujas Wagle
• Harsh Shinde

IoT-based Smart Street Light systems are one of the key infrastructures of a smart highway and are important for safe driving and safety of the pedestrians. The increase in new high- ways and the introduction of electric vehicles account for high energy consumption and thus a significant cost to the utilities. Smart street light is a mixture of enabling control and monitoring, transforming these systems into quick-witted and energy-efficient systems, ensuring huge savings in the consumption of power. This paper presents the automation of smart street lights, charging stations for electric vehicles working on the renewable energy source, solar energy. Machine Learning model LSTM-RNN is used to compare predicted and resulting power consumption.

Solar Energy, Consumption of Energy, Node MCU(ESP8266), Machine Learning, LSTM-RNN.

"Solar Energy Based Smart Highway with Energy Prediction using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.a472-a475, June-2022, Available :http://www.jetir.org/papers/JETIR2206053.pdf

"Solar Energy Based Smart Highway with Energy Prediction using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. ppa472-a475, June-2022, Available at : http://www.jetir.org/papers/JETIR2206053.pdf