International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 6 November-December 2024 Submit your research before last 3 days of December to publish your research paper in the issue of November-December.

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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