Anomaly detection for refrigeration systems

A system, comprising:

• one or more processors;
• and
• a non-transitory computer-readable medium comprising instructions that when executed by the one or more processors cause the one or more processors to perform operations including:
• receiving telemetry data associated with one or more refrigeration systems over a first time interval, wherein the telemetry data includes a set of temperature measurements;
• receiving anomaly data including an identification of one or more anomaly events associated with the one or more refrigeration systems over the first time interval;
• generating a training dataset using the telemetry data;
• modifying the training dataset by removing, from the training dataset, a portion of the telemetry data that corresponds to an anomaly event based on the anomaly data;
• and
• training an anomaly detection machine learning model using the modified training dataset, wherein the anomaly detection machine learning model, once trained, is configured to predict an occurrence of an anomaly event occurring over a future time interval.

A non-transitory computer-readable medium comprising instructions that when executed by one or more processors cause the one or more processors to perform operations including:

• receiving telemetry data associated with one or more refrigeration systems over a first time interval, wherein the telemetry data includes a set of temperature measurements;
• receiving anomaly data including an identification of one or more anomaly events associated with the one or more refrigeration systems over the first time interval;
• generating a training dataset using the telemetry data;
• modifying the training dataset by removing, from the training dataset, a portion of the telemetry data that corresponds to an anomaly event based on the anomaly data;
• and
• training an anomaly detection machine learning model using the modified training dataset, wherein the anomaly detection machine learning model, once trained, is configured to predict an occurrence of an anomaly event occurring over a future time interval.