5. Results
5.3. Estimation of global hourly consumption
From the gathered data, consumption profiles were estimated for each establishment and compared with the hourly monitored consumption profiles.
5.3.1.1. Establishment A
The estimated hourly consumption profile, resulting from the bottom-up analysis, is displayed in Figure 46, where it is compared to the mean hourly profile of monitored consumption.
64 Figure 46 - Comparison between estimated and monitored hourly consumption profiles (Establishment A)
The hourly profile obtained from the estimation of consumption was similarly shaped to the mean consumption profile, which could indicate the adequateness of the estimation process. However, some differences between the two curves were observable:
• The estimated profile displayed a deviation from the mean profile, generally being between 1 and 2 kWh lower that the mean profile. This mainly indicated an underestimation of the base load consumption, whose largest contribution came from refrigeration appliances. These could have been underestimated in two specifications: nominal power and utilisation factor. Another aspect that could underestimate consumption for all appliances was the fact that, while estimations were done for active power (except when based on measure consumption), the mean consumption referred to apparent power.
• Usage patterns gathered from the surveys did not always match the measured consumption, causing the two curves to trend differently for some periods. To reduce this effect, estimations suffered changes according to measured values and observed consumption trends.
The estimation process allowed to categorise the consumption based on physical space and end-use.
In terms of physical space, the dining room (40%) was the largest energy user, followed by the kitchen (30%) and the storage rooms containing refrigeration (30%). The distribution of energy consumption by end-use is displayed in Figure 47.
0 2 000 4 000 6 000 8 000 10 000 12 000 14 000 16 000 18 000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Energy consumption (Wh)
Hour
Bottom-up
Mean consumption
65 Figure 47 - Distribution of energy consumption by end-use (Establishment A)
Refrigeration, which as mentioned before, was assumed to be underestimated, was the largest user.
Contributing to this were the inefficient appliances located in the storage rooms, as the one shown in Figure 20. Warewashing, cooking and serving of food and beverages (which included the coffee-related appliances and buffet heated and cooled displays) were the following largest energy users. Lighting, media (including office and entertainment appliances) and ventilation and air extraction (ventilation, in this case, was reportedly used to cool the ice maker) were the least significant consuming end-uses.
Due to the fact that these results were based on estimations, there is the possibility of this distribution not being entirely representative.
5.3.1.2. Establishment B
The comparison between the estimated and mean hourly consumption of this establishment is shown in Figure 48.
Figure 48 - Comparison between estimated and monitored hourly consumption profiles (Establishment B) Refrigeration
53%
Cooking 12%
Media 5%
Warewashing 13%
Lighting 2%
Food and Beverages
Serving 12%
Ventilation and Air Extraction
3%
0 1 000 2 000 3 000 4 000 5 000 6 000 7 000 8 000 9 000 10 000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Energy consumption (Wh)
Hour
Mean consumption Bottom-up
66 As it was observed in the case of Establishment A, although the two curves were similarly shaped, the estimated profile had a lower base load consumption, which as before was assumed to result mainly from underestimating the consumption of refrigeration appliances.
Different from the case of Establishment A was, however, the estimation of consumption in the second and fourth periods. In those, the estimations are generally closer (sometimes even above) to the mean consumption, meaning that if the base load was adjusted to the measured values there would be more often overestimations. This was due to the difficulty in predicting the hourly utilisation factors of the different appliances, especially for those without measured data in any of the establishments.
The energy consumption was estimated to be well distributed in this establishment, with the dining room area accounting for 52% and the kitchen for 48% of the total energy consumed in the restaurant. If including the estimated consumption of the adjacent storage, the dining room would account for 48% of the total energy consumption, the kitchen for 44% and the storage space for 8%.
In Figure 49 it is displayed the distribution of energy consumption by end-use. This pie chart refers solely to consumption in the restaurant, as that was the monitored space. If including the consumption of the storage space, there would be the following distribution: Refrigeration (45%); Cooking (7%); Media (2%);
Warewashing (18%); Lighting (2%); Air Extraction (14%); Food and Beverages Serving (11%); Others (1%).
Figure 49 - Distribution of energy consumption by end-use (Establishment B)
In this establishment, as for the previous one, refrigeration was the largest energy user, followed by warewashing, air extraction and serving of food and beverages. The lower significance of cooking could be explained by the absence of the existence of a gas oven, instead of an electric one. Media, lighting and others were the estimated least significant energy users. Climatization is not included in this chart because the monitoring process occurred in the Winter, when it is reportedly not used.
5.3.1.3. Establishment C
The estimated and monitored consumption profiles, displayed in Figure 50, presented a different situation from the two previous establishments. In this case, there was a smaller deviation in the base
Refrigeration 41%
Cooking 7%
Media 3%
Warewashing 20%
Lighting 2%
Air extraction 15%
Food and Beverages
Serving 12%
Others 1%
67 load consumption but the second and fourth periods were overestimated and the third period was underestimated. The overestimations were assumed to be a result of the uncertainty in determining both nominal power and hourly utilisation factors, the latter being greatly dependent on the time of utilisation for appliances with great impact in consumption, such as the oven and warewashing machines. The fourth period of consumption, corresponding to dinner period, displayed the largest deviations (as previously mentioned, the survey indicated that both lunch and dinner were served on Sundays, which was not observed during the monitoring period, reducing the mean consumption of this period). The underestimations were caused by underestimating the number of appliances left on during the afternoon break.
Figure 50 - Comparison between estimated and monitored hourly consumption profiles (Establishment C)
In this establishment, the kitchen accounted for 59% of the total consumption in the restaurant, while the dining room area accounted for 41%. If the consumption in the adjacent storage space was considered, there would be the following distribution: Kitchen (47%); Dining Room (33%); Storage (20%).
Figure 51 displays the distribution of the energy consumed in the restaurant by end-use. If the energy consumed in the adjacent storage was included, there would be the following distribution: Refrigeration (28%); Cooking (23%); Media (6%); Warewashing (14%); Lighting (4%); Water Heating (3%); Air Extraction (16%); Food and Beverages Serving (7%); Others (1%).
0 1 000 2 000 3 000 4 000 5 000 6 000 7 000 8 000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Energy consumption (Wh)
Hour
Bottom-up
Mean consumption
68 Figure 51 - Distribution of energy consumption by end-use (Establishment C)
In this establishment, one major difference from the previous ones was the greater significance of cooking in the global consumption, one of the reasons being the existence of an electric oven.
5.3.1.4. Establishment D
The estimated and monitored hourly consumption profiles, displayed in Figure 52, shows a similar situation to that of Establishment B, with the two curves generally close, although with deviations in the base load. There were, however, additional deviations caused by the uncertainty in the determination of nominal power and hourly utilisation factors. One good example was the 11th point of consumption (meaning the consumption between 10:00 AM and 11:00 AM), which presented a significant deviation.
For that period, measured data from the dishwasher indicated a high utilisation factor, followed by a reduction in the 12th point. An overestimation of the nominal power of the appliance (only one phase was measured) or an inaccurate utilisation profile (it was based on less of one week of data, opposed to several months of global consumption monitoring) could explain this deviation, as also could the overestimation of another appliance working simultaneously. These were a few of the uncertainties involved in the estimation of consumption of any of the establishments.
Figure 52 - Comparison between estimated and monitored hourly consumption profiles (Establishment D) Refrigeration
22%
Cooking 23%
Media 6%
Warewashing 14%
Lighting 4%
Water heating 3%
Air extraction
16%
Food and beverages
9%
Others 1%
0 2 000 4 000 6 000 8 000 10 000 12 000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Energy consumption (Wh)
Hour
Mean consumption Bottom-up
69 Based on the estimated consumption, the kitchen accounted for 58% of the total consumption, while the dining room accounted for 42%. A distribution of energy consumption by end-use is displayed in Figure 53.
Figure 53 - Distribution of energy consumption by end-use (Establishment D)
As it was the case of Establishment B, cooking had a lower significance that expected due to having a gas fired oven, instead of an electric one. Climatization and air extraction presented high estimated significance, although both were based on data from the remaining establishments due to the impossibility of retrieving information regarding nominal power and utilisation factor.
5.3.1.5. Global sample
Following the individual analyses from the previous sections, it was possible to establish comparisons for the whole analysed sample. Table 16 displays the measured and estimated daily consumption, as well as the estimation error. It is possible to observe a predominance of underestimation of consumption, with exception to Establishment C, where consumption was overestimated. This shows that the parameter with the most significant impact on the estimation performance was the base load consumption, as that was the main cause for underestimations.
Table 16 - Comparison between measured and estimated consumption Measured Daily
Consumption (kWh)
Estimated Daily Consumption (kWh)
Estimation Error (%)
Establishment A 181 151 -16%
Establishment B 111 95 -14%
Establishment C 70 75 7%
Establishment D 107 92 -13%
Analysing the four sites combined, it was also possible to find the average distribution of consumption by the different end-uses, which is displayed in Figure 54. This distribution comes in line with the previously mentioned ones, with refrigeration being the most significant energy user.
Refrigeration 18%
Cooking 7%
Media 2%
Warewashing 13%
Lighting 4%
Air Extraction 18%
Climatizatio n 20%
Food and Beverages
14%
Others 5%
70 Figure 54 - Average distribution of consumption by end-use in the four sites