An encouraging development is the quick expansion of renewable energy extraction. Harnessing renewable energy is economically feasible at the current rate of technological advancement. Traditional energy sources, such as coal, petroleum, and hydrocarbons, which have negative effects on the environment, are coming under more social and financial pressure. Companies need more solar and wind power because this calls for a well-balanced mix of renewable resources and a higher proportion of alternative energy sources. Sustainable energy can be captured using a variety of techniques. Massive scale and small-sized are the two most prevalent techniques. No renewable energy source possesses an inherent property that restricts how it may be managed or how it can be planned to produce electricity. A number of factors have contributed to a growth in the use of alternative sources, one of which is to mitigate the effects of rising temperatures. To improve the ability to estimate renewable energy, various modeling approaches have been created. This region might use an HRES to give many sources with the inclusion of different energy sources. The inventiveness of solar and wind power and the brilliant ability of neural networks to handle complex time-series data signals have both aided in the prediction of sustainable energy. Therefore, this research will examine the numerous information models in order to determine which proposed models can provide accurate projections of renewable energy output, such as sunlight, wind, or pumped storage. In the fields of sustainable energy predictions, a number of machine learning methods, such as multilayer perceptions MLP, RNN CNN, and LSTM designs, are frequently utilized. This form of modeling uses historical data to predict potential values and can predict short-term patterns in solar and wind generation.
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