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	<title>EnergyPlus &#8211; BIOENGINEER.ORG</title>
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		<title>AI Paints Building Schedules Out of Noise, Cutting Energy Use by Nearly 29 Percent</title>
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				<category><![CDATA[Technology]]></category>
		<category><![CDATA[building energy management]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[Diffusion models]]></category>
		<category><![CDATA[energy efficiency]]></category>
		<category><![CDATA[EnergyPlus]]></category>
		<category><![CDATA[generative AI]]></category>
		<category><![CDATA[graph neural networks]]></category>
		<category><![CDATA[HVAC control]]></category>
		<category><![CDATA[microgrids]]></category>
		<category><![CDATA[Multi-objective optimization]]></category>
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					<description><![CDATA[Researchers have combined conditional diffusion models with graph neural networks to generate coordinated energy schedules for building clusters, achieving a 28.6 percent energy saving and a 38.5 percent summer peak-load reduction in simulation.]]></description>
		
		
		
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