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In order to save energy in supermarkets, technical solutions need to be supported by appropriate maintenance and operation tools. These tools should provide sufficient information to detect unusual levels of energy consumption. Therefore this paper presents an explorative study on a well sub-metered grocery supermarket in the UK Yorkshire and Humber region. The data collected for this study included electricity consumption, footfall data, inside and outside climate data, as well as settings of all relevant building timers. Thereafter the meaningfulness of these predictors was evaluated with the ‘stepwise’ option in the linear regression section of SPSS. The results generally show a very good fit between the mathematical regression model and the measured data (r > 0.95). The only exception was the refrigeration model for all five days. Upon further investigation it was found that the current reading for one of these five days was unusually low (proving the effectiveness of the method to detect abnormalities). Based on these results it can be argued that it should be possible to use data routinely gathered by supermarkets or otherwise easily obtained to detect greater abnormalities and thus keep energy consumption to a minimum.

Using IBM SPSS statistics to identify predictors of electricity consumption in a UK supermarket