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    <title><![CDATA[The Big Share- AI in Solar Energy]]></title>
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    <description><![CDATA[<p>AI has become and increasingly popular technology and has proven to have a significant impact in the energy industry. Listen to learn more!</p><p>sources:</p><p>Warren. (2025, July 25). AI forecasting makes solar and wind power more reliable than ever. Sustainable Future Australia. <a target="_blank" rel="noopener noreferrer nofollow" href="https://biomassproducer.com.au/alternative-renewable-energy/ai-forecasting-makes-solar-and-wind-power-more-reliable-than-ever/">https://biomassproducer.com.au/alternative-renewable-energy/ai-forecasting-makes-solar-and-wind-power-more-reliable-than-ever/</a></p><p>Abisoye, B. O., Sun, Y., &amp; Zenghui, W. (2024). A survey of artificial intelligence methods for Renewable Energy Forecasting: Methodologies and insights. <em>Renewable Energy Focus</em>, <em>48</em>, 100529. <a target="_blank" rel="noopener noreferrer nofollow" href="https://doi.org/10.1016/j.ref.2023.100529">https://doi.org/10.1016/j.ref.2023.100529</a></p><p>How ai can help clean energy meet growing electricity demand. <a target="_blank" rel="noopener noreferrer nofollow" href="http://Energy.gov">Energy.gov</a>. (2024, August 16). <a target="_blank" rel="noopener noreferrer nofollow" href="https://www.energy.gov/policy/articles/how-ai-can-help-clean-energy-meet-growing-electricity-demand">https://www.energy.gov/policy/articles/how-ai-can-help-clean-energy-meet-growing-electricity-demand</a></p><p>Ejiyi, C. J., Cai, D., Thomas, D., Obiora, S., Osei-Mensah, E., Acen, C., Eze, F. O., Sam, F., Zhang, Q., &amp; Bamisile, O. O. (2025). Comprehensive Review of Artificial Intelligence Applications in renewable energy systems: Current implementations and emerging trends. <em>Journal of Big Data</em>, <em>12</em>(1). <a target="_blank" rel="noopener noreferrer nofollow" href="https://doi.org/10.1186/s40537-025-01178-7">https://doi.org/10.1186/s40537-025-01178-7</a></p><p>Erdiwansyah, Mamat, R., Syafrizal, Ghazali, M. F., Basrawi, F., &amp; Rosdi, S. M. (2025). Emerging role of Generative AI in Renewable Energy Forecasting and system optimization. <em>Sustainable Chemistry for Climate Action</em>, <em>7</em>, 100099. <a target="_blank" rel="noopener noreferrer nofollow" href="https://doi.org/10.1016/j.scca.2025.100099">https://doi.org/10.1016/j.scca.2025.100099</a></p><p>cover image:</p><p><a target="_blank" rel="noopener noreferrer nofollow" href="https://blog.otthydromet.com/en/the-future-of-solar-power-2/">https://blog.otthydromet.com/en/the-future-of-solar-power-2/</a></p>]]></description>
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      <title><![CDATA[The Big Share- AI in Solar Energy]]></title>
      <itunes:title><![CDATA[The Big Share- AI in Solar Energy]]></itunes:title>
      <description><![CDATA[<p>Artificial intelligence has become an increasingly popular form of technology and has become immersed in the energy sector. Listen to learn more!!</p><p>Sources:</p><p>Abisoye, B. O., Sun, Y., &amp; Zenghui, W. (2024). A survey of artificial intelligence methods for Renewable Energy Forecasting: Methodologies and insights. Renewable Energy Focus, 48, 100529.<a target="_blank" rel="noopener noreferrer nofollow" href="https://doi.org/10.1016/j.ref.2023.100529">https://doi.org/10.1016/j.ref.2023.100529</a></p><p> </p><p>Birol, F., Cozzi, L., Spencer, T., Singh, S., D’Ambrosio, D., Hopewell, H., Jacamon, V., Martinos, A., Salmon <em>Ai is set to drive surging electricity demand from data centres while offering the potential to transform how the Energy Sector Works - News - IEA</em>. International Energy Association. (2025). <a target="_blank" rel="noopener noreferrer nofollow" href="https://www.iea.org/news/ai-is-set-to-drive-surging-electricity-demand-from-data-centres-while-offering-the-potential-to-transform-how-the-energy-sector-works">https://www.iea.org/news/ai-is-set-to-drive-surging-electricity-demand-from-data-centres-while-offering-the-potential-to-transform-how-the-energy-sector-works</a></p><p> </p><p>Chatterjee, J., &amp; Dethlefs, N. (2022, June 10). <em>Facilitating a smoother transition to renewable energy with ai</em>. Patterns (New York, N.Y.). <a target="_blank" rel="noopener noreferrer nofollow" href="https://pmc.ncbi.nlm.nih.gov/articles/PMC9214339/">https://pmc.ncbi.nlm.nih.gov/articles/PMC9214339/</a></p><p> </p><p>Ejiyi, C. J., Cai, D., Thomas, D., Obiora, S., Osei-Mensah, E., Acen, C., Eze, F. O., Sam, F., Zhang, Q., &amp; Bamisile, O. O. (2025). Comprehensive Review of Artificial Intelligence Applications in renewable energy systems: Current implementations and emerging trends. <em>Journal of Big Data</em>, <em>12</em>(1). <a target="_blank" rel="noopener noreferrer nofollow" href="https://doi.org/10.1186/s40537-025-01178-7">https://doi.org/10.1186/s40537-025-01178-7</a></p><p> </p><p>Erdiwansyah, Mamat, R., Syafrizal, Ghazali, M. F., Basrawi, F., &amp; Rosdi, S. M. (2025). Emerging role of Generative AI in Renewable Energy Forecasting and system optimization. <em>Sustainable Chemistry for Climate Action</em>, <em>7</em>, 100099. <a target="_blank" rel="noopener noreferrer nofollow" href="https://doi.org/10.1016/j.scca.2025.100099">https://doi.org/10.1016/j.scca.2025.100099</a></p><p>makes-solar-and-wind-power-more-reliable-than-ever/</p><p> </p><p>Warren. (2025, July 25). AI forecasting makes solar and wind power more reliable than ever.  Sustainable Future Australia.<a target="_blank" rel="noopener noreferrer nofollow" href="https://biomassproducer.com.au/alternative-renewable-energy/ai-forecasting-">https://biomassproducer.com.au/alternative-renewable-energy/ai-forecasting-</a></p><p>Cover Image:</p><p><a target="_blank" rel="noopener noreferrer nofollow" href="https://blog.otthydromet.com/en/the-future-of-solar-power-2/">https://blog.otthydromet.com/en/the-future-of-solar-power-2/</a></p>]]></description>
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