Using IBM SPSS Statistics To Identify
Predictors Of Electricity Consumption
In A UK Supermarket
Author(s)
M. Braun, H. Altan & S. Beck
Abstract
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.
Keywords: supermarket, electricity consumption, regression analysis, SPSS.
Keywords
Keywords: supermarket, electricity consumption, regression analysis, SPSS.
Related Book
Other papers in this volume
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