System and method for non-linear signal extraction and structural-drift detection

A method for optimizing energy performance evaluation, the method comprising:

• determining, using an energy consumption data unit, an energy consumption data associated with an entity in different cycles;
• classifying, using a structural drift identifier unit, the energy consumption data into multiple individual drift classes, wherein the individual drift classes are defined on the basis of a set of parameters;
• extracting, using a non-linear signal extractor unit, a set of non-linear signals from the energy consumption data by identifying cycles based on the frequency of energy consumption data and evaluating the variance in the energy consumption data;
• refining, using an individual drift class refinement unit, the individual drift classes by eliminating first weak drifts using set of non-linear signals resulting into refined individual drift classes;
• collating and collectively, using a collective drift refinement unit, the refined individual drift instances of all the refined individual drift classes into refined collective drift classes by eliminating second weak drifts;
• and
• analyzing, using a performance analyzer, insights derived based on refined collective drift classes.

A computer readable medium comprising one or more processors and a memory coupled to the one or more processors, the memory storing instructions executed by the one or more processors, the one or more processors configured to:

• determine an energy consumption data associated with an entity in different cycles and pre-process the energy consumption data;
• classify the energy consumption data into multiple individual drift classes, wherein individual drift classes are defined on basis of a set of parameters;
• extract a set of non-linear signals from the energy consumption data by identifying cycles based on the frequency of energy consumption data and evaluating the variance in energy consumption data;
• refine the individual drift classes by eliminating first weak drifts using the set of non-linear signals resulting into refined individual drift classes;
• collate and collectively refine the refined individual drift instances of all the classes into refined collective drift classes by eliminating second weak drifts;
• and
• analyze insights derived based on refined collective drift classes.