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      Leveraging Machine Learning for Prediction and Optimizing Renewable Energy Systems

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      Renewable energy, Machine Learning, Energy forecasting, Optimization, Sustainable
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            Abstract

            Renewable energy systems play a critical role in the transition to a more sustainable future. However, these systems are often characterized by significant fluctuations in energy output due to changes in weather and other environmental factors. In recent years, machine learning algorithms have emerged as a powerful tool for predicting and optimizing renewable energy systems. This paper provides an overview of the latest research in this area, including techniques for predicting solar radiation and wind power output, as well as algorithms for optimizing energy storage and grid stability. The paper also explores the potential of machine learning to revolutionize the way we generate, distribute, and consume energy, paving the way for a cleaner, more sustainable future. By leveraging the power of artificial intelligence, we can unlock the full potential of renewable energy systems and create a more resilient, secure, and efficient energy infrastructure.

            Content

            Author and article information

            Journal
            ScienceOpen Preprints
            ScienceOpen
            14 March 2023
            Affiliations
            [1 ] Department of Energy Engineering and Industry, Science and Research Branch, Islamic Azad University, Tehran, Iran;
            Author notes
            Author information
            https://orcid.org/0000-0002-3754-0161
            Article
            10.14293/PR2199.000003.v1
            01a9ff9f-20e1-4c19-89c3-95d5ef0f8401

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

            History
            : 14 March 2023
            Categories

            All data generated or analysed during this study are included in this published article (and its supplementary information files).
            Earth & Environmental sciences,Computer science,Engineering
            Renewable energy, Machine Learning, Energy forecasting, Optimization, Sustainable

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