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Central plant equipment such as chillers, pumps and cooling towers have all seen huge gains in efficiency over the last 30 years with the adoption of new technologies like variable speed drives and magnetic bearing compressors. This new equipment, coupled with the proliferation of direct digital controls, has seen efficiency gains of >50% across the HVAC industry, however, it has also made the problem of identifying and deploying optimal control sequences exponentially more complex.
The fine-tuning of central plants for efficiency, stability and mechanical performance in the age of variable speed everything is now too complex for even the best control engineers or plant operators using conventional tools. This is where the AI Digital Twin approach can help, by leveraging historical Building Management System (BMS) data and machine learning to simulate the operational performance of all equipment within the system, Exergenics software can run millions of scenarios in the cloud to determine the thermodynamically optimal controls based on real-world performance and actual load and weather profiles.