Adaptive selection of machine learning/deep learning model with optimal hyper-parameters for anomaly detection of connected chillers

A method for managing chiller models for a building, the method comprising:

• determining whether chiller fault data exists in chiller data used to generate a plurality of chiller shutdown prediction models;
• generating a first performance evaluation value for each of the plurality of chiller shutdown prediction models using a first evaluation technique in response to a determination that chiller fault data exists in the chiller data;
• generating a second performance evaluation value for each of the plurality of chiller shutdown prediction models using a second evaluation technique in response to a determination that chiller fault data does not exist in the chiller data;
• and
• selecting one of the plurality of chiller shutdown prediction models based on the first performance evaluation value and the second performance evaluation value.

A chiller model management system for a building, the system comprising:

• one or more processors configured to:
• determine whether chiller fault data exists in chiller data used to generate a plurality of chiller shutdown prediction models;
• generate a first performance evaluation value for each of the plurality of chiller shutdown prediction models using a first evaluation technique in response to a determination that chiller fault data exists in the chiller data;
• generate a second performance evaluation value for each of the plurality of chiller shutdown prediction models using a second evaluation technique in response to a determination that chiller fault data does not exist in the chiller data;
• and
• select one of the plurality of chiller shutdown prediction models based on the first performance evaluation value and the second performance evaluation value.