International Journal For Multidisciplinary Research
E-ISSN: 2582-2160
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Impact Factor: 9.24
A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal
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Volume 6 Issue 6
November-December 2024
Indexing Partners
Forecasting Renewable Energy Production Using AI-Based Weather Prediction Models
Author(s) | Onkar Mane, Ashish Zagade, Sohan Sonpatki, Soumitra Chavan, Kaustubh Nimbalkar |
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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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E-ISSN 2582-2160
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