How a Global Food Chain successfully attained a 6% reduction in Energy Consumption

How a Global Food Chain successfully attained a 6% reduction in Energy Consumption

 

Overview

Our client wanted to reduce their energy consumption. But they did not have any provision to monitor their power usage. They approached us with the intention of metering their energy consumption and subsequently finding opportunities that could lead to energy saving. Datakrew used MADS, our energy optimization solution to address their concerns.
 

Client Profile

Our client is a multi-national fast food chain outlet. They are one of the fastest-growing franchises in the world with over 37,000 locations in more than 100 countries and territories. They offer customizable food options in a made-to-order fashion from fresh ingredients.
 

Problem

To ensure the freshness of the ingredients, they are kept under specific temperature conditions. In order to maintain the particular temperature conditions, they had to utilize refrigerators, counter freezers, and air conditioning units.
 
Since our client did not have an energy savings system in place, they could not meter their energy consumption or collect data on their energy usage. This meant that they couldn't make use of opportunities that could potentially save electricity. They did not have a monitoring solution that could be used to analyze the energy usage over time. There was no data available on the energy consumption of individual appliances.
 
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Solution

Datakrew introduced MADS, our energy optimization solution which was used to monitor 4 individual electric appliances. The appliances that were monitored are:
 
  • 2 x Refrigerator
  • 1 x Counter freezer
  • 1 x Air conditioner
 
The parameters that were monitored were:
 
  • Power consumption per hour
  • Consumption during peak vs non-peak hours
  • Consumption Trends
  • Maximum and minimum consumption per appliance
 
With the data collected for a period of two weeks, curves were generated to display the energy consumption trends for individual appliances. Energy consumption (in Watts) is plotted against the date to denote peaks and averages. Peak hours of consumption were also identified.
 

Results

With monitoring and capturing of data from the individual appliances, the power consumption of each individual appliance was analyzed. With the help of the dashboard, it was easier to visualize data and identify peak hours of usage.
 
Our algorithm detected unusual cooling activity and abnormal power consumption for one of the refrigerators. The time was identified as 1 AM to 10 AM. It was discovered upon inspection that this unusual activity was due to a human error. The appliance was not shut off properly leading to increased power consumption during non-peak hours. Alerts were set up to notify the workers to avoid this in the future.
 

Benefits

  • The issues were detected along with the time of occurrence
  • Fixing the manual error led to energy savings of approximately 6%
  • Alerts were set up to notify workers of unusual activity