Cooling Data Center Podcast
What if the biggest barrier to maximizing cooling capacity and efficiency isn't the hardware in your data hall, but the static set points and manual control strategies you've been running since day one? In this episode, Clifford Federspiel, founder of Vigilent and former Johnson Controls and UC Berkeley engineer, joins Robert for a wide-ranging conversation about what happens when you hand cooling control to a machine learning system that can predict what's coming next, and why the data centre industry's dependence on fixed, conservative control is leaving real savings untouched. From building sensor-driven efficiency solutions for "lost and forgotten" buildings in 2004, to deploying predictive cooling optimisation across over a thousand sites worldwide, Cliff brings two decades of practical AI and controls experience to an industry still debating whether to trust automation. Together, they explore: * Why Vigilent's system can learn a facility's cooling dynamics in as little as a few hours * How predictive "look ahead" control differs from the basic PID and fuzzy logic embedded in most OEM equipment * The self-driving car analogy: why operators can always take control back, and why they rarely need to * How saving cooling power in AI data centres directly translates to more compute capacity and revenue * Why the common practice of "even wear" on equipment actually maximises the risk of simultaneous failure * The growing complexity of dual cooling plants as liquid and air systems coexist at the rack * Why fixed set points become exponentially more expensive as rack densities climb from 10kW to 100kW+ * How Vigilent's operational data helps teams make informed decisions about when hardware investment is truly needed If you've ever wondered whether your cooling plant could be doing more with what it already has, or wanted someone to explain AI-driven optimisation without the hype, Cliff delivers straight talk backed by over a thousand deployments. His take? The industry is driven by fear, but the proof is already there, and a system watching every minute and adjusting dynamically will always outperform static setpoints and manual controls.
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