Providing itemized energy consumption in a utility bill is becoming a priority, and perhaps a business practice in the near term. In recent times, a multitude of systems have been developed such as smart plugs, smart circuit breakers etc., for non-intrusive load monitoring (NILM). They are integrated either with the smart meters or at the plug-levels to footprint appliance-level energy consumption patterns in an entire home environment. While deploying the existing technologies in a single home is feasible, scaling these technological advancements across thousands of homes in a region is not realized yet. This is primarily due to the cost, deployment complexity, and intrusive nature associated with these types of real deployment. Motivated by these shortcomings, in this paper we investigate the first step to address scalable disaggregation by proposing a disaggregation mechanism that works on a large dataset to accurately deconstruct the cumulative signals. We propose an iterative noise separation based approach to perform energy disaggregation using sparse coding based methodologies which work at the single ingress point of a home, i.e., at the meter level. We performed a ranked iterative signal removal methodology that effectively isolates appliances' individual signal waveform as noise on an aggregate energy datasets with moderate granularity (1 min). We performed experiments on real dataset and obtained approximately 94% energy disaggregation, i.e., disaggregated appliance-wise signal estimation accuracy.