Apparatus, computer program product, and method for evaluating course of efficiency degradation in chillers

A computer-implemented method, comprising:

• receiving a first data set comprising chiller operations data and chiller efficiency data over a first time interval;
• receiving a second data set comprising chiller operations data and actual chiller efficiency data over a second time interval;
• generating, with a machine learning model, a data prediction based upon the first data set and the chiller operations data of the second data set, wherein the data prediction comprises expected chiller efficiency data over the second time interval;
• determining a chiller efficiency degradation level based on a difference between the data prediction and the actual chiller efficiency data of the second data set for the second time interval wherein the data prediction comprises expected chiller efficiency data;
• determining a predicted coefficient of performance based on the data prediction and a recorded coefficient of performance based on the actual chiller efficiency data of the second data set;
• determining a coefficient of performance degradation level based on a difference between the predicted coefficient of performance and the recorded coefficient of performance;
• and
• determining a coefficient of performance prediction error based at least on the coefficient of performance degradation level, wherein the coefficient of performance prediction error is shifted such that its initial value corresponds to a baseline of zero degradation, thereby enabling a normalized degradation profile over time.

A computer program product comprising at least one non-transitory computer-readable storage medium having computer program code stored thereon that, in execution with at least one processor, is configured for:

• receiving a first data set comprising chiller operations data and chiller efficiency data over a first time interval;
• receiving a second data set comprising chiller operations data and actual chiller efficiency data over a second time interval;
• generating, with a machine learning model, a data prediction based upon the first data set and the chiller operations data of the second data set, wherein the data prediction comprises expected chiller efficiency data over the second time interval;
• determining a chiller efficiency degradation level based on a difference between the data prediction and the actual chiller efficiency data of the second data set for the second time interval wherein the data prediction comprises expected chiller efficiency data;
• and
• determining a predicted coefficient of performance based on the data prediction and a recorded coefficient of performance based on the actual chiller efficiency data of the second data set;
• determining a coefficient of performance degradation level based on a difference between the predicted coefficient of performance and the recorded coefficient of performance;
• and
• determining a coefficient of performance prediction error based at least on the coefficient of performance degradation level, wherein the coefficient of performance prediction error is shifted such that its initial value corresponds to a baseline of zero degradation, thereby enabling a normalized degradation profile over time.