Overview

The global demand for cooling is no longer a luxury; it is a critical component of urban survival and economic productivity. As our cities grow denser and the climate becomes more volatile, the traditional methods of managing district cooling systems (DCS) are being pushed to their physical and operational limits. For decades, we have relied on static hydraulic models and manual setpoint adjustments to manage the massive networks of pipes and pumps that keep our cities habitable. However, the complexity of modern urban cooling-characterized by fluctuating energy prices, variable occupancy patterns, and the urgent need for decarbonization-demands a more dynamic approach. This is where Artificial Intelligence and Machine Learning step in. Intelligent Districts: Optimizing District Cooling with AI explores the intersection of thermal engineering and data science. This book is born out of the necessity to bridge the gap between the mechanical ""iron"" of the plant and the digital ""intelligence"" of the algorithm. We move beyond simple automation to examine how machine-learning algorithms can predict demand, optimize chilled-water distribution in real-time, and transform a passive infrastructure into a self-optimizing grid. The goal of this work is to provide a roadmap for the next generation of district cooling: one that is leaner, smarter, and significantly more sustainable.

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