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		<title>A 40-Year-Old Learning Algorithm Slashes Office HVAC Energy Use by Nearly 40 Percent</title>
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		<pubDate>Wed, 07 Oct 2026 02:30:34 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[building energy management]]></category>
		<category><![CDATA[decentralized control]]></category>
		<category><![CDATA[energy optimization]]></category>
		<category><![CDATA[EnergyPlus simulation]]></category>
		<category><![CDATA[HVAC control]]></category>
		<category><![CDATA[multi-zone buildings]]></category>
		<category><![CDATA[open-plan offices]]></category>
		<category><![CDATA[Reinforcement Learning]]></category>
		<category><![CDATA[SARSA]]></category>
		<category><![CDATA[smart buildings]]></category>
		<category><![CDATA[tabular learning]]></category>
		<category><![CDATA[thermal comfort]]></category>
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					<description><![CDATA[Researchers at Tanta University show that a decentralized, coupling-aware SARSA reinforcement learning controller cut simulated HVAC energy use in a six-zone open-plan office by nearly 40 percent while preserving thermal comfort.]]></description>
		
		
		
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