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Green building applications need efficient and fine-grained determination of power consumption pattern of a wide variety of consumer-grade appliances through non-intrusive load monitoring (NILM) techniques for an effective adaptation and percolation of demand response model down to the consumer level appliances. A key inhibitor to the widespread adoption of such demand response policy at the consumer grade appliances for intelligent building energy management, is the inability of smart plug to efficiently determine, control or infer the power consumption pattern of multiple devices in tandem. In practice, deploying smart plug based NILM and acquiring the low-level power measures of a large number of devices is often difficult or impossible due to the deployment complexity and varying characteristics of devices and thus must instead be employed at the circuit-level and inferred through the incorporation of novel usage-based measurement and probabilistic level-based disaggregation algorithm. But the challenges in deploying non-intrusive load monitoring algorithm involve disaggregating individual device?s consumption from the aggregate power measurement, as well as modeling and incorporating the usage based prediction. Thus in this project we will focus on advanced machine learning and data analytics algorithms that capture the measurement based approach and circuit level NILM with the autonomous profiling and prediction logic to enable the deployment of flexible and fungible smart plug and the evolvability of future DR model in green building applications.