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<oembed><version>1.0</version><provider_name>PSR Energy</provider_name><provider_url>https://www.psr-inc.com/en/</provider_url><title>Issue #03 - PSR Energy</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="mX2eko5coN"&gt;&lt;a href="https://www.psr-inc.com/en/analytics-report/edicao/issue-03/"&gt;Issue #03&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.psr-inc.com/en/analytics-report/edicao/issue-03/embed/#?secret=mX2eko5coN" width="600" height="338" title="&#x201C;Issue #03&#x201D; &#x2014; PSR Energy" data-secret="mX2eko5coN" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script&gt;
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</html><thumbnail_url>https://www.psr-inc.com/wp-content/uploads/2026/06/edicao-1024x768.png</thumbnail_url><thumbnail_width>1024</thumbnail_width><thumbnail_height>768</thumbnail_height><description>Artificial intelligence is rapidly becoming a core capability in energy analytics, not only as a tool for automation, but as a new layer for modeling, reasoning, software development, and large-scale computation. This issue of the Analytics Report highlights how PSR is positioning itself at the forefront of AI applied to energy systems, combining climate scenario generation, stochastic optimization, reinforcement learning, agentic workflows, MCP-based interaction with analytical tools, AI-assisted coding, and GPU-based optimization. Together, the articles show how AI and advanced computing can support more robust methodologies, faster studies, and new ways of developing, operating, and interpreting complex energy models.</description></oembed>
