Supermarkets are energy intensive. To optimize their energy consumption, major subcomponents need to be identified through disaggregation. Disaggregation can be challenging in supermarkets due to lack of instrumentation at sufficient resolution, especially in developing economies. Also, the diversity in the space cooling systems across stores adds to the complexity. This paper presents a novel approach to disaggregate store level energy into weather-dependent and weather-independent components using facility energy models. The novelty lies in the fact that the approach does not require sensory minutiae or extensive sub-metering. The approach identifies the key drivers of a supermarket's energy consumption, and also estimates gaps in the performance with possible root causes. A case study on using the proposed approach to analyze 94 stores from a supermarket chain is presented. Key findings include: (i) weather-independent loads can dominate weather-dependent loads. (ii) Refrigeration cases contribute to space cooling too. Cooling requirement of these cases can be more than that of space cooling. (iii) For these reasons, occupancy may not appreciably influence a store's energy consumption. (iv) In the supermarket chain studied, various reasons for poor performance spanning both design and operations were discovered, viz., higher deadloads, oversized HVAC systems, and additional load turn-on hours. (C) 2015 Elsevier B.V. All rights reserved.