Implement machine learning-based renewable energy forecasting

[Roshan23R]

Predicting the output of renewable energy sources like solar and wind is challenging due to their variability. Machine learning models can analyze real-time data from weather forecasts, location and other factors to predict renewable energy generation, enabling better integration into the grid. Example Refer Link - https://towardsdatascience.com/predicting-solar-power-output-using-machine-learning-techniques-56e7959acb1f

Note - Go with any dummy data which could be used to predict output.

Submit it in the ml-part folder as yourname_forecast.py file with dummy data yourname_data.csv used.

[ashishlal2003]

Hi! From the initial overview of the resource link that you shared, what I understood is that we would be predicting the Power Output based on certain features. As a solution to this issue, do we have to come up with the best model for this scenario only or come up with a Jupyter Notebook file as well containing an overall analysis of the features and how we came to a final conclusion of an ML model?

[Roshan23R]

Hi, @ashishlal2003 !! You have to come up with the best possible model, also if feature analysis is there then it's better. It could be any ML or DL model.

[ashishlal2003]

Okay. Will work on it!

[Roshan23R]

Have you selected columns based on solar or wind energy prediction??

[ashishlal2003]

Based on the dataset taken from Kaggle, the outcome is mainly for Solar energy prediction. All columns that have been selected for the model are based on the correlation plot that I have created which are closely related to the power output variable.

[ashishlal2003]

@Roshan23R will a new issue be opened for the Flask backend or the details will be carried on in this issue itself?

[Roshan23R]

created a new issue for the same