RETRRAC (Retrofittable Efficiency Technology for Resilient Refrigeration with AI-based Controls)

Commercial refrigeration systems are the largest energy consumers in food retail stores, constituting up to 62% of their electrical energy usage. Around 20% of this energy going to refrigeration is wasted due to the machines’ low energy efficiency. This results in over $4.4 billion lost annually by domestic food businesses. This low efficiency is coupled with low resilience in commercial refrigeration systems. Most food stores lack sufficient backup power or energy storage to continue running their entire refrigeration system in an outage, so they stand to lose $367,000 to over $900,000 in food inventory at each extended power outage. These problems will be addressed with non-invasive retrofit hardware that collects data and hosts the proposed artificial-intelligence-based software to run each building’s commercial refrigeration system in three modes. In fully-powered, normal operation (mode 1), the technology will measure refrigeration temperature and case traffic in real time. With this data the software will generate operational commands that manipulate temperature signals to the refrigeration thermostat, in this way shifting compressor cycles to reduce energy consumption. The technology will also be equipped to pre-cool units when sent a command that harsh weather and potential power outages are predicted (mode 2). For these situations, the proposed solution will preemptively adjust the temperature of certain refrigeration cases to keep food below the industry-critical temperature of 41◦F during impending outages. Finally, during unexpected outages (mode 3), the technology will supply the highest-value assets with backup power (e.g., from battery storage or an onsite generator). Given the typically limited capacity of backup power, the technology will enable food retailers to optimally use backup power and maintain temperature in essential chillers (e.g., meat and dairy cases) while letting less sensitive cases (e.g., soda coolers) heat up gradually. Phase I will design and do initial testing on a proof of concept with machine learning software and hardware that hosts the control software locally. The company will develop software that cleans and analyzes operational data from commercial refrigeration systems and then derives optimized temperature signals from this data. Finally, Phase I will produce hardware that implements these signals by updating temperature settings in a commercial refrigeration unit in real time. To alleviate barriers to adoption and manufacturing challenges, Phase II will involve developing a prototype with a form factor similar to existing commercial refrigeration thermometers. In Phase II, the technology will also be tested and updated to receive wireless commands from a central server, a necessary step for commercialization. At the conclusion of Phase II, the technology will be available to sell as a Software as a Service package to food retail chains.