In order to study the selected supermarkets, a number of different energy analysis methods are possible. The aim of the method selection process was to identify as simple an approach as possible which still yielded meaningful results. Li et al (2012) suggest that the two deterministic approaches, simulating buildings with software and the degree days
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method, are the most popular research approaches for the impact assessment of climate change on energy use in buildings. Section 4.4 discussed simulation software models and showed in Figure 4.6 that there is a considerable amount of data from various sources to be collected before such a model can be built. For instance, in order to calculate the energy use in a building, hourly weather files and data regarding casual gains, e.g. from people, need to be available. However, how the number of customers will develop over the next couple of decades is not easy to predict and therefore may require a considerable amount of time to establish. Once the model has been constructed it needs to be calibrated to achieve more accurate and reliable results or else simulation results may deviate from the true value by as much as 100% (Coakley et al, 2014). The end product is a model which will be dealt with as a “black box”, because the content of the model is not so important.
What is of importance is that the model yields credible results. Depending on the level of detail this modelling and calibration can require a significant amount of time per building (Rivalin et al, 2014). Therefore this approach was not pursued further, because it was considered to not be time efficient.
The degree day method, also explained in Section 4.5, requires that a balance point temperature can be established. However, the data shows (see for instance Figure 5.8 on page 79) that heating is required all year round and, therefore, a balance point temperature (i.e. when heating is no longer required) cannot be established. The degree day approach can also be used for comfort cooling, but the purpose of refrigeration in a supermarket is to preserve food and not to provide comfort cooling. Hence, this method was also deemed unsuitable.
The data-driven approach uses measurements to establish a relationship between weather variables and energy consumption. According to Belcher et al (2005), there are 14 weather variables which could be considered for building simulations, some of which are derived from other variables. In order to achieve the objective of producing a simple, but relevant model, the automatic weather station Davis Vantage Pro2 was considered, which also received favourable reviews (Burt, 2009; Bell et al, 2015). However, the sponsoring company found this approach impractical. Therefore whether temperature alone would suffice or whether it should be combined with relative humidity was studied. The literature review supported using only temperature, because it showed that some researchers successfully applied a temperature change point regression to a supermarket (Schrock and
Claridge, 1989; Ruch and Claridge, 1992; Kissock et al, 1998). Some deterministic analysis tools also use only outside temperature as their input parameter (e.g. the degree day method discussed in Section 4.5). Furthermore a pilot study showed that the humidity ratio (a function of the relative humidity and temperature) had a strong correlation with temperature (greater than 0.9). This strong correlation, which is called multicollinearity, can be problematic for MLR since it can produce predictions which are overly sensitive to small changes in the data (Montgomery et al, 2006, pp 109-111). Therefore it was deemed acceptable to use the outside temperature as the only weather variable.
Figure 5.5: Method flowchart for the pilot study
The pilot study referred to in the previous paragraph was based on the supermarket in Hull and used the methodology sketched out in Figure 5.5. When comparing this method flowchart with the major steps depicted in Figure 5.1 it is evident that both approaches are very similar. Some of the minor differences are that the pilot study used relative humidity data for a multiple regression analysis and that the climate considered was for the 2040s rather than for the 2030s.
The scatter plot matrices in Figure 5.6 and Figure 5.7 relate to the original data for the pilot study, which is to say that outliers had not been removed. Both matrices plot the temperature against the humidity ratio and confirm the strong relationship between the two variables. The panels in which these variables are used as predictors for the electricity and gas consumption also show little difference in their predictive power. Further examining the relationship between electricity use and temperature (or ) shows that it is
Construct
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non-linear. On the other hand, the gas use and outside temperature (or ) exhibits more of a linear relationship.
Figure 5.6: Scatter plot matrix for the original electricity data of pilot study
Figure 5.7: Scatter plot matrix for the original gas data of pilot study
In view of the issues raised above it was felt that simple regression should be used where possible and change point regression models where necessary. To decide when to apply a
change point regression model, a second order polynomial regression model was evaluated and, if the coefficient of determination improved over a simple regression model by more than 10%, a change point regression model was used, which was an approach also used for similar previous studies (e.g. Schrock and Claridge (1989); (Ruch and Claridge, 1992)).
Although this threshold was somewhat arbitrary, it took the shape of the graphs into consideration. It was believed that using these relatively simple models satisfied the goal of simplicity with meaningful results in order to assess the impact of climate change on supermarket energy use.