Energy disaggregation analysis of a supermarket chain using a facility-model

Abstract 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.

[1]  R. Judkoff,et al.  Methodology for Modeling Building Energy Performance across the Commercial Sector , 2008 .

[2]  Haimonti Dutta,et al.  NILMTK: an open source toolkit for non-intrusive load monitoring , 2014, e-Energy.

[3]  Anand Sivasubramaniam,et al.  Automatic generation of energy conservation measures in buildings using genetic algorithms , 2011 .

[4]  Eliot Crowe,et al.  California's Commercial Building Energy Asset Rating System (BEARS): Technical Approach and Design Considerations , 2012 .

[5]  William Chung,et al.  Benchmarking the energy efficiency of commercial buildings , 2006 .

[6]  Theofilos A. Papadopoulos,et al.  Pattern recognition algorithms for electricity load curve analysis of buildings , 2014 .

[7]  Qiang Zhang,et al.  Model-based benchmarking with application to laboratory buildings , 2002 .

[8]  Lee Siew Eang,et al.  Benchmarking energy use and greenhouse gas emissions in Singapore's hotel industry , 2010 .

[9]  Fu Xiao,et al.  A simplified energy performance assessment method for existing buildings based on energy bill disaggregation , 2012 .

[10]  Suman Giri,et al.  An energy estimation framework for event-based methods in Non-Intrusive Load Monitoring , 2015 .

[11]  M. Deru,et al.  Using DOE Commercial Reference Buildings for Simulation Studies: Preprint , 2010 .

[12]  Ian Beausoleil-Morrison,et al.  Disaggregating categories of electrical energy end-use from whole-house hourly data , 2012 .

[13]  Anand Sivasubramaniam,et al.  Watts in the basket?: Energy Analysis of a Retail Chain , 2013, BuildSys@SenSys.

[14]  Wen-Shing Lee,et al.  Benchmarking the energy efficiency of government buildings with data envelopment analysis , 2008 .

[15]  Bing Liu,et al.  U.S. Department of Energy Commercial Reference Building Models of the National Building Stock , 2011 .

[16]  Roberto Lamberts,et al.  Developing energy consumption benchmarks for buildings: Bank branches in Brazil , 2014 .

[17]  Xiwang Li,et al.  Building energy consumption on-line forecasting using physics based system identification , 2014 .

[18]  Lucio Soibelman,et al.  User-Centered Nonintrusive Electricity Load Monitoring for Residential Buildings , 2011 .

[19]  David E. Claridge,et al.  Statistical modeling of the building energy balance variable for screening of metered energy use in large commercial buildings , 2014 .

[20]  G. W. Hart,et al.  Nonintrusive appliance load monitoring , 1992, Proc. IEEE.

[21]  Rodger Edwards,et al.  Developing figures for ‘gain’ in relation to refrigerated cabinets in a supermarket building , 2014 .

[22]  Rodger Edwards,et al.  Influence of display cabinet cooling on performance of supermarket buildings , 2014 .

[23]  Fred Popowich,et al.  AMPds: A public dataset for load disaggregation and eco-feedback research , 2013, 2013 IEEE Electrical Power & Energy Conference.

[24]  Steven B. Leeb,et al.  Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms , 1996 .

[25]  Radu Zmeureanu,et al.  Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses , 1999 .

[26]  Michael Baldea,et al.  Nonintrusive disaggregation of residential air-conditioning loads from sub-hourly smart meter data , 2014 .

[27]  Savvas A. Tassou,et al.  Energy consumption and conservation in food retailing , 2011 .

[28]  Malcolm J. Cook,et al.  An empirical study of electricity and gas demand drivers in large food retail buildings of a national organisation , 2014 .