Forecasting Renewable Energy Production Using AI-Based Weather Prediction Models

Author(s)	Onkar Mane, Ashish Zagade, Sohan Sonpatki, Soumitra Chavan, Kaustubh Nimbalkar
Country	India
Abstract	This research paper focuses on Forecasting Renewable Energy Production, particularly solar and wind power, plays a crucial role in transitioning towards sustainable energy sources. Accurate forecasting of renewable energy production is essential for efficient integration into the power grid. In this paper, we propose an AI-based approach leveraging weather prediction models to forecast renewable energy production. Specifically, we employ deep learning techniques, including Long Short-Term Memory (LSTM) networks, to predict solar irradiance and wind speed, which are key factors influencing renewable energy generation. We evaluate the performance of our proposed framework using various quality metrics, including mean absolute error (MAE), root mean square error (RMSE), normalized metrics (nMAE, nRMSE), and coefficient of determination (R2).
Keywords	Renewable Forecasting, AI, Weather Prediction Model, Sustainability.
Field	Engineering
Published In	Volume 6, Issue 3, May-June 2024
Published On	2024-06-02
Cite This	Forecasting Renewable Energy Production Using AI-Based Weather Prediction Models - Onkar Mane, Ashish Zagade, Sohan Sonpatki, Soumitra Chavan, Kaustubh Nimbalkar - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.21917
DOI	https://doi.org/10.36948/ijfmr.2024.v06i03.21917
Short DOI	https://doi.org/gtxrn5

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