The combination of the measured consumption data with the consumption estimates made it possible to identify the meal periods as having the greatest consumption and demonstrated the correlation between attendance and energy consumption in a company. The analysis of the performance of the estimations revealed errors ranging from underestimations of 16% to overestimations of 7%, with a predominance of the first type.
Introduction
- Context
- Problem definition
- Research questions
- Research methodology
- Document structure
One of the main purposes of this study is to determine how accurately energy ratings can be developed in these types of commercial facilities based on the development of a bottom-up demand model developed with high-level data collected on-site and data already available for review literature. The methodology used in this study was: a review of existing literature to gain an understanding of the field; conducting energy audits, monitoring consumption and surveying employees in plants; development of a bottom-up analysis for consumption estimation to complement the findings of the analysis of collected readings; treatment of collected data due to existing errors and irregularities; analysis of collected data for the entire plant and for devices, search.
State of the art
- Overview of energy consumption
- Energy saving potential
- Categorisation of energy consumption
- Factors influencing energy use
- Metrics of energy consumption
- Bottom-up analysis
- Missing data
However, they provide insight into the importance of the sector in the global energy market. This allows for a better understanding of the energy consumption within the most energy-intensive areas and activities.
Case Study
Site description
22 The dining room area, which includes an area with a counter and bathrooms, contains the following equipment: ice machine, refrigerated display cases, countertop refrigerator, display refrigerator, refrigerated buffet display, chest freezer, beer dispenser, coffee machine, coffee grinder, orange juicer, orange press, two electric heated buffets, under counter glass washer, computer, cash register, card machine, television, projector, set-top box, wi-fi router, sound system, air conditioning, electric fan, CFL and LED lighting. The kitchen has the following white goods: worktop fridge, gas oven, gas stove, gas grill, microwave, two deep fryers, dishwasher, glass washer, kitchen hood, gas water heater and fluorescent tubes. The dining area, including the counter area and the bathrooms, has the following equipment: two refrigerated cases, glass door freezer, countertop refrigerator, ice maker, beer dispenser, coffee machine, coffee maker, coffee grinder, orange press, two air conditioners, two televisions, set-top box, Wi-Fi router, Wi -Fi extender, card machine, computer, cash register, electric insect killer and CFL lighting.
The kitchen has the following white goods: under-counter fridge, microwave, gas stove, electric oven, under-counter electric dishwasher, kitchen hood, electric water heater and fluorescent tubes. It has the following appliances: fridge, three chest freezers, three under counter freezers, two double temperature fridges, ice maker, washing machine and fluorescent lights. The kitchen has the following white goods: gas oven, gas stove, electric fryer, door dishwasher, kitchen hood, microwave, portable air conditioner, countertop refrigerator, dishwater heater, gas water heater and fluorescent tubes.
Methodology
- Electricity consumption monitoring
- Data handling
- Bottom-up analysis
- Energy tariff plans analysis
- Constraints
In addition to the daily amount of missing data, another distinguishing parameter is the day of the week on which the errors occurred and its frequency. Average consumption for the corresponding day of the week: Missing values were replaced with the equivalent time values from the average profile for the same day of the week. Monitored consumption for the previous relevant day of the week: Missing data are imputed with readings from the previous relevant day of the week.
The absolute values of the relative error were calculated with the same time resolution for the 10 mean profiles. One of the main limitations of this work was the installation process of the measurement equipment. One of the panels was located in the dining room and included only the main circuit breaker that controlled the power supply to the entire establishment.
Results
Monitored consumption
The process of monitoring the beer machine in this plant resulted in very different power load profiles, of which the profile presented in Figure 18 is one example. The profile shown in Figure 24 is the result of the process of monitoring one phase of the three-phase circuit breaker supplying this device. The power load profile of this appliance, shown in Figure 29, is the result of the process of monitoring one phase of the three-phase circuit breaker supplying the fryer.
After site inspection and observation of power load profiles, it was concluded that the circuit breaker was mislabeled. The energy load profile of this device for a typical day, shown in Figure 42, resulted from the process of monitoring one phase of the three-phase circuit breaker that supplies it. The profile shown in Figure 45 resulted from the process of monitoring one phase of the three-phase circuit breaker supplying the air conditioner.
Consumption metrics
Some assumptions were made: global consumption was extrapolated to a 365-day period, as the observed periods included both normal consumption days and lower consumption days such as days off and holidays; financial turnover and number of meals were extrapolated to 365 days, as they were based on rough weekly estimates; the number of operating hours extrapolated to 363 days for plants C and D, and for plants A and B the number of days off was also taken into account. Establishment Total area (kWh/m2) Internal area (kWh/m2 ) Kitchen area (kWh/m2 ) Number of roofs (kWh/roof) Annual financial turnover (kWh/€) Annual number of meals (kWh/meal) Annual operating hours (kWh /operating hour) Number of employees (kWh/employee) Observations. However, for the rest of the measurements, the differences in consumption are much more noticeable, with Plant D being the largest energy user, even when considering the estimated consumption of Plant B's additional cooling equipment.
The small reported number of meals served at facility D had the highest consumption per meal, even exceeding facility A. Following the conclusions for the best metrics from the bibliography presented in 2.5: Number of Meals, facility C was the best energy-active facility to followed by institution B, institution A and finally institution D; financial traffic defined plant C as the most successful, followed by plant B, plant D and finally plant A; the size of the kitchen highlighted plant B as the most successful, followed by plant D, plant C and finally plant A. Total area (kWh/m2 ) Internal area (kWh/m2 ) Kitchen area (kWh/m2) Number of covers (kWh/cover) Estimated annual financial turnover (kWh/€) Annual number of meals (kWh/meal) Annual operating hours (kWh/operating hour) Number of employees (kWh/employee).
Estimation of global hourly consumption
The energy consumption was assessed to be well distributed in this establishment, with the dining area accounting for 52% and the kitchen for 48% of the total energy consumption in the restaurant. If you include the consumption of storage space, there will be the following distribution: Cooling (45%); Cooking (7%); Media (2%);. In this establishment, the kitchen accounted for 59% of the total consumption in the restaurant, while the dining area accounted for 41%.
In this enterprise, one major difference from the previous ones was the greater importance of cooking in global consumption, one of the reasons being the existence of an electric oven. These were some of the uncertainties involved in estimating consumption from any of the establishments. 69 Based on the estimated consumption, the kitchen accounted for 58% of the total consumption, while the dining room accounted for 42%.
Energy plans
As previously shown in the analysis of consumption, the current contracted power is sufficient for the power load peaks. Institution B presented an unusual situation: although monitored consumption showed power load peaks approaching 20 kVA, the tariff plan was a single tariff tariff with a contracted power of 6.9 kVA. Considering this, tariff plans for a contracted power of 6.9 kVA and above have been analysed.
72 A change of supplier, contracted power, cycle and time period proved to be the best option, delivering savings of up to 15% by increasing the contracted power by 5 levels. Changes to the tariff plan within the same supplier, for different cycles, time periods and contracted energy levels, resulted in estimated savings between 2% and 6%, while switching to another supplier with the same conditions as currently would allow savings of up to 5%. %. Other options that maintained current contracted power included a single change to the rate plan within the same supplier (which meant a saving of approximately 2%) and a change of supplier (which meant a saving of 5% for calculations based on the global average profile and of 6% when using seasonal averages). average profiles).
Collected data accuracy
A simultaneous change of the contracted power, supplier and cycle turned out to be the option with the largest estimated savings, up to approximately 23%. Although the monitored consumption validated a contractual power reduction, its feasibility would need to be inspected as the three electrical phases may not have properly distributed loads. The next best option would be a change of supplier and cycle, which would yield savings of up to 21%.
The deviation values shown in the table are also subject to uncertainty, especially if they are based on estimates. The conclusions drawn from this study are generally acceptable, as they were not only based on absolute values, but also to a large extent on relationships between parameters. A good example of this is the analysis of energy tariff plans: the conclusions about the correct tariff plans should hold, because all calculations were based on the same data sample; However, the calculated monthly costs for the current tariff plan turned out to be excessively high compared to the electricity bill, which also caused the calculated savings to be an overestimate.
Conclusion
Contributions
This work is an important contribution to the perceived non-existent consumption database in the Portuguese foodservice sector, providing data on both global and appliance consumption. It also had practical effects, such as proposing changes to tariff plans that were expected to result in real savings on energy bills and the identification of underperforming appliances.
Future work
Kolokotroni, Environmental impacts, energy and emission reductions from food catering in the UK, 29th EFFOST Int. 20] Building Technologies Office, US Department of Energy, Energy Conservation Potential and Demonstration Opportunities for Commercial Building Heating, Ventilation, and Air Conditioning Systems. 21] United States Department of Energy, KITCHEN APPLIANCE UPGRADE IMPROVES WATER EFFICIENCY AT DOD EXCHANGE FACILITIES Best Management Practice Case Study.
