Abstract
The goal of this work is to predict solar panel power output by utilizing machine learning techniques, such as neural networks and regression, and by examining variables such as panel orientation, temperature, and sun irradiation. Models with high forecast accuracy help with grid integration and solar energy management. The article also outlines an innovative LED street light system that runs on solar power and uses Internet of Things (IoT) technology for intelligent control. By adjusting brightness in response to motion detection and current conditions, the device improves both urban safety and energy economy. For continuous operation, it runs on a backup battery, encouraging intelligent and sustainable street lighting
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