h i g h l i g h t sEnergy consumption of supermarket depends more on temperature than humidity. Multiple regression analysis is a flexible tool to consider for energy use prediction. Results show dramatic reduction in gas use and modest increase in electricity use.
a b s t r a c tThe change in climate has led to an interest in how this will affect the energy consumption in buildings. Most of the work in the literature relates to offices and homes. However, this paper investigates a supermarket in northern England by means of a multiple regression analysis based on gas and electricity data for 2012.The equations obtained in this analysis use the humidity ratio derived from the dry-bulb temperature and the relative humidity in conjunction with the actual dry-bulb temperature. These equations are used to estimate the consumption for the base year period and for the predicted climate period 2030-2059.The findings indicate that electricity use will increase by 2.1% whereas gas consumption will drop by about 13% for the central future estimate. The research further suggests that the year 2012 is comparable in temperature to the future climate, but the relative humidity is lower. Further research should include adaptation/mitigation measures and an evaluation of their usefulness.
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.
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