Michael West, Ph.D., P.E.
Track M | Big Data and IoT
This is a case study of three installations of a cloud-driven IoT control optimization system. The system maximizes energy efficiency, provides remote fault detection diagnostics, and supports maintainability of DX air conditioners. Optimization systems were installed on package units at three DoD sites in diverse climate locations and operated for two years: Cape Canaveral, FL; Mojave Desert, CA; and Beaufort, SC. The systems use a cloud-driven relational control strategy to maximize the ratio of cooling delivered versus power consumed as operating conditions vary over a day and across seasons, and as components degrade over time. The systems successfully detected and compensated for faults such as low refrigerant charge or condenser coil fouling, and reported via Internet any problems with EER, pressures, temperatures, and efficiency degradation to service technicians in an actionable way. Energy engineers and service technicians typically use indirect indicators of energy performance and make adjustments according to manufacturer guidelines and standard field practice, which varies with their level of experience. The case study shows how cloud-based measurement and optimization of energy performance is a big improvement on current practice.
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