International Journal of Science, Engineering and TechnologyAuthors: Jadhav Sanchita Chaitanyakumar, Professor Bangar Abhishek P., Professor S.B.Bhosale, Dr.A.A.Khatri
Abstract: Street lighting consumes substantial electricity, and photovoltaic (PV) street lamps are increasingly promoted as a way to cut energy cost and emissions. Their performance, however, depends on weather, the dimming strategy, battery state of charge, and lamp condition, which makes prediction and monitoring central to reliable operation. This paper reviews three recent IEEE Access studies that address complementary parts of the problem: a comparison of five machine learning algorithms for predicting the operating status of PV streetlights, a year-long field trial and artificial-intelligence-assisted techno-economic analysis of a PV-integrated pedestrian streetlamp, and a vehicle-mounted Internet of Things device for mapping light intensity. The studies are summarised, compared on data, methods, and outcomes, and examined for internal consistency and transferability. The review finds that deep models give the highest reported accuracy but at much higher computational cost and on very small datasets, that compact neural networks combined with dimming-aware energy management achieve large grid-energy savings in field conditions, and that mobile sensing offers a scalable route to compliance checking but has not yet been validated against regulatory illuminance standards. Open gaps and research directions are outlined.
