Visual result representation

To incorporate the remaining small appliances considered in each case study and easily enable interpretation of the data, the results obtained from the Primary and Secondary SA have been reformed into chrome maps and presented in Figures 3.10 and 3.12, for the first case study, and Figures 3.11 and 3.13, for the second case study. These maps provide a visual indicator of the

performance of the model depending on the kind of appliances considered in the assessment and the initial information available (Tables 3.4 and 3.5) for each of the calculation models. The colour bar at the right of each map indicates the mapping of data values into the chrome maps, by a monotonically increasing color scale that goes from the minimum to the maximum numeric values of the map.

Primary SA:

The relative impact of the input factors for each model is indicated through the colour tonality of the Primary chrome maps, Figures 3.10 and 3.11, and quantified in the colour bar at the right of each map. This colour variable is calculated for each appliance’s Primary SA graph (e.g. Figure 3.9 for the PCs) as the Euclidean distance from the origin of the graph to the corresponding input factor point (Equation 3.51). Logarithmic values of d1have been used to allow visual recognition of the results in the graphical representation.

d1= q

µ∗2_{+ σ}2 _(3.51)

The Primary SA chrome maps can be used to identify the most suitable calculation model and relevant input information for an audit depending on the scenario or the case study. For the first case study, the typical office, the following four Primary SA chrome maps were obtained, one for each calculation model considered.

(a) Model A for all the appliances (b) Model B for all the appliances

(c) Model C for all the appliances (d) Model D for all the appliances

Figure 3.10: Primary SA chrome maps: impact of the different input factors (x-axis) for each of the small

power considered in the typical office case study (y-axis)

The analysis of Figure 3.10 resulted in the several relevant findings.

• Regarding the appliances targeted in the study: PCs and laptops have the major relevance in the final outputs across the four calculation models. Fridges, by contrast, are not very relevant in any of the model calculations, specially in model B.

• In terms of the different input factors feeding the calculation models: The number of Operational Hours, Oh, maintains a high impact in the energy estimation of the four

models. Others high impact factors are the nominal power rates Pl for models A, C and D and the energy wastage related inputs, Pw and Tw, for models B and D.

And the less influential input factors are, the number of Units, Un, for model A and B and the Occupancy density, Od, and number of Persons, Pr, for models C; and the ON Load factor, Fon, for model D.

For the second case study, the co-working office, the following four Primary SA chrome maps were obtained, one for each calculation model considered.

(a) Model A for all the appliances (b) Model B for all the appliances

(c) Model C for all the appliances (d) Model D for all the appliances

Figure 3.11: Primary SA chrome maps: impact of the different input factors (x-axis) for each of the small

power considered in the co-working office case study (y-axis)

Although each chrome-map presents a case dependent input ranking for each specific calcula- tion model, i.e. they cannot be directly compared between them, the contrast of Figures 3.10 and3.11 provides an overview of the models sensibility depending on the case studies. Being model A the most sensible and D the less affected by the change of scenario.

The analysis of Figure 3.11 and its contrast with Figure 3.10 resulted in the several relevant findings.

• Regarding the appliances targeted in the study: PCs and laptops continue to have a major relevance in the final outputs across the four calculation models, closely followed by the Printers. Again, Fridges are the less relevant across models, although this impact has notably increased for model C.

• In terms of the different input factors feeding the calculation models: he number of Operational Hours, Oh, continues to have a high impact over the four models, slightly decreasing for model A and increasing for model B. The energy wastage related inputs, Pw and Tw, remains as high impact factors for models B and D and the nominal power rates, Pl, impact slightly decrease for models A and C.

The less influential input factors continue to be the ON Load factor, Fon, for model D and the number of Units, Un, for model A and B, although the impact of this last factor has slightly decreased. For model C, only the Occupancy density, Od, remains as a low influential input factors, since the relative impact of the number of Persons, Pr, has notably increased.

Secondary SA:

The relative complexity (e.i., the degree of non-monotonicity and deflection from the mean trend) of the input factors for each model is indicated through the colour tonality in the Sec- ondary chrome maps, Figures 3.12 and 3.13, and quantified in the colour bar at the right of each map. This colour variable is calculated for each appliance’s secondary SA graph, as the Euclidean distance from the origin of the graph to corresponding input factor point (Equation 3.52). Log- arithmic values of d2have been used to allow visual recognition of the results in the graphical representation.

For the first case study, the typical office, the following four Secondary SA chrome maps were obtained, one for each calculation model considered.

(a) Model A for all the appliances (b) Model B for all the appliances

(c) Model C for all the appliances (d) Model D for all the appliances

Figure 3.12: Secondary SA chrome maps: complexity of the different input factors (x-axis) for each of the

small power considered in the typical office case study (y-axis)

Once the most influential inputs have been detected through the Primary SA method, the Secondary SA chrome maps in Figure 3.12 can be used to detect additional information regarding asymmetries on the output due to the input effects. This helps to understand the significance of the different input factors on the final energy estimations, as stated in some of the relevant

findings below.

• PCs, laptops and screens have the most general asymmetric behavior and fridges the most symmetric behavior across the four calculation models.

• Regarding the input factor impact on the asymmetric of the model response: the Averaged ON Power, Pon, have the higher impact for model B; the Occupancy Density, Od, and the Number of Persons, Pr, for model C; and the Number of Units, Un, for model D. The asymmetric behaviour is distributed between the different input factors for model A.

For the second case study, the co-working office, the following four Secondary SA chrome maps were obtained, one for each calculation model considered.

(a) Model A for all the appliances (b) Model B for all the appliances

(c) Model C for all the appliances (d) Model D for all the appliances

Figure 3.13: Secondary SA chrome maps: complexity of the different input factors (x-axis) for each of the

small power considered in co-working office case study (y-axis)

The analysis of Figure 3.13 and its contrast with Figure 3.12 resulted in the several relevant findings. Regarding the appliances targeted in the study:

• PCs, laptops and screens continue to have a high asymmetric behavior models A and B, but this behaviour has notably decreased for model C and D.

• The low asymmetric behavior of the fridge across the four calculation models has decreased even more.

Regarding the input factor impact on the asymmetric of the model response: the Averaged ON Power, Pon, has the higher impact for model B; the Occupancy Density, Od, and the Number of Persons, Pr, for model C; and the Number of Units, Un, for model D. The asymmetric behaviour is distributed between the different input factors for model A.

• Regarding the input factor impact on the asymmetric of the model response: the high impact of Averaged ON Power, Pon, for model B has notably increased, as well as the impact of the Load factor, Fl, for model A and the impact of the Number of Units, Un for model D. The Occupancy Density, Od, and the Number of Persons, Pr, remain with similar high impact for model C.