Solar Energy Optimisation Using IoT and Deep Learning- A Review

Energy is utilized in a different form across the globe, which includes electrical and mechanical energy. To increase the efficiency of renewable energy, which comes from a variety of sources, several researchers have tried. This research focuses on the internet of things (IoT), which develops a system that combines several sensors and IoT to produce power for business and home appliances. The power-generating circuit will link a solar panel, a humidity sensor, a temperature sensor, an air quality sensor, a photo resistance sensor, and a body heat sensor. The overall quantity of energy generated from renewable resources up until the projected fusion is calculated using AI models. These approaches are limited and only focus on data analysis. This study proposes a perfect energy forecasting architecture that blends IoT technologies and deep learning principles. Because of the quantity of renewable energy data, the Deep Learning layer is employed to predict renewable energy. A distributed LSTM method is frequently utilized by researchers and tested with various models to demonstrate predictability and execution time improvement.