Comparative Analysis of Solar Energy Generation Techniques in Maharashtra Using Deep Learning Approaches

The growth of sustainable energy solutions around the globe is making solar energy one of the most preferred sources of energy than other conventional sources like coal especially in countries like India where coal is still the mainstay of electricity generation. Since the generation of solar energy is subject to weather, geolocation, and time, in order to assimilate solar energy into the power grid, its forecasting needs to be done with a high level of accuracy. This paper provides a methodological framework utilizing Machine Learning (ML) on solar energy generation prediction models for the state of Maharashtra. This research aims to enhance the accuracy of solar power generation forecasting by leveraging historical weather data and advanced Deep Learning (DL) techniques, including and comparing the performance of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Multilayer Perceptron (MLP) models. The analysis takes into account the influence of different weather factors and applies different ML techniques enabling the model to make accurate estimates that increase efficiency and facilitate the smooth introduction of solar energy into the power system.