Overview

Building Management Systems (BMS) and digital twins have significantly improved how HVAC systems are monitored and controlled. Yet despite decades of advancement, these systems remain fundamentally reactive. They detect faults after performance has already degraded, alarms after damage has begun, and trends after efficiency has been lost. This book is written from the premise that today's HVAC intelligence stops too early in time. An HVAC system does not fail suddenly. Components age, fatigue accumulates, efficiency erodes, and stress patterns repeat long before a breakdown occurs. These slow, long-term processes are largely invisible to current BMS platforms and even to most digital twins, which are designed to mirror system behavior-not to understand its future. The concept introduced in this book, the Neural Twin(TM), goes beyond modeling and monitoring. It represents a future HVAC intelligence layer capable of learning how systems age, how components deteriorate, and how failure signatures emerge months before failure occurs. Rather than predicting faults, the Neural Twin(TM) predicts time. This is not a product description, nor a claim about what current systems can do. It is a forward-looking technical concept-grounded in real HVAC behavior-that challenges the industry to rethink how intelligence, maintenance, reliability, and energy performance are approached. The goal of this book is to bridge engineering reality with future AI capability, and to provide a clear vision of how HVAC systems could evolve from reactive machines into systems that truly understand their own lifespan.

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