Guo Jiayu, founder of Eneng Festival: Managing air conditioning well can save real profits from unseen places.

Ask AI · How can AI energy consumption management help chain brands reduce costs and increase efficiency?

On March 25, the 2026 China Catering Industry Conference and the 35th HCC Global Catering Industry Expo, jointly hosted by the World Chinese Cuisine Federation and Hongcan.com, was held at the Hangzhou Convention and Exhibition Center. Among them, at the “2026 China Catering AI Development Forum,” the founder of Aienengjie, Guo Jiayu, shared his views on the issue of hidden costs in the catering industry.

△ Guo Jiayu, founder of Aienengjie

Guo Jiayu mentioned that the catering industry is currently under widespread pressure to reduce costs and increase efficiency. In addition to obvious costs such as food ingredients, labor, and rent, there are also some costs that are easy to overlook, such as air-conditioning electricity bills.

How can we save a huge amount of profit from air-conditioning usage without changing equipment or laying off employees? Guo Jiayu worked out the numbers for everyone on site. He said, assuming there are 1,000 stores, in Southern stores the annual air-conditioning electricity bill is about 20,000 yuan; for 1,000 stores combined, that totals 20 million yuan. In day-to-day management, it was found that 15%—20% of this is waste, meaning 4 million yuan in electricity costs is lost away for nothing. If you use that money to buy rice, noodles, or improve table turnover rates, what kind of wealth would that be?

In his view, this part of the waste is not because companies don’t care, but because the management difficulty is higher.

He mentioned that first, standards are hard to unify—different employees perceive temperature differently, and in actual operation it is easy for arbitrary adjustments to occur; second, it depends on people to manage—training costs are high and hard to sustain, and once personnel turnover happens, both existing experience and it are lost; at the same time, electricity bills are covered by headquarters, but store employees tend to base decisions more on their own comfort level, which also brings a certain degree of energy-consumption deviation.

As store scale expands, this problem will be further amplified. He said that from a single store to multiple stores, and then to cross-regional layouts, operating differences are clearly evident under different cities and different climate conditions. Unified rules are often hard to adapt to, ultimately leading to management failure and uncontrolled energy consumption.

Against this background, Aienengjie’s solution is to complete energy-consumption management through AI. He described AI as an “energy-consumption store manager that doesn’t clock out for 24 hours and works year-round without rest.” It collects store data through intelligent hardware, and then, together with AI algorithm models, makes comprehensive judgments on factors such as customer flow, weather, and store environment, forming store-specific temperature standards and energy-saving strategies. These are then put into effect in real time and executed precisely through intelligent hardware, and continuously optimized and adjusted.

At the platform level, it is also possible to collect data from different stores in real time, enable unified management and intelligent analysis, and issue anomaly alerts. He said that in the past, problems that required relying on manual discovery—such as equipment failures—can now be identified by the system in advance and responded to quickly, thereby reducing energy-consumption waste and lowering maintenance and operations costs, and avoiding safety hazards caused by serious failures.

Compared with traditional scheduled energy-saving solutions, Guo Jiayu emphasized the advantages of AI energy saving: traditional solutions are disconnected from real operating scenarios, and when you encounter a sales surge or delivery peak, they cannot respond in time; nationwide stores use unified rules, and differences between North and South can’t be adjusted flexibly; any change requires the manufacturer to come on site, with high costs; and the energy-saving effect is hard to quantify—you can’t tell whether the air conditioner saved electricity or whether other equipment had an impact.

As for Aienengjie’s AI energy-saving model, it is “one store, one plan,” “24-hour real-time monitoring,” and “self-learning with continuous iteration.” It not only ensures energy-saving results, but also helps standardize the service environment for stores. For chain brands, they can accumulate brand energy-saving models; in the same city, if there is historical project data, new stores can directly draw on the model’s experience and quickly enter an efficient and energy-saving state. Guo Jiayu said that this is also why many chain brands choose Aienengjie.

In terms of the business model, Aienengjie launched a profit-sharing cooperation model: in the early stage, hardware costs are paid by Aienengjie, and in the later stage, a portion is shared based on the electricity cost saved—“as long as the electricity bill doesn’t drop, no share is taken.” Customers with a larger number of stores can also choose to purchase outright, and once the customer’s data volume reaches a certain scale, private deployment is supported.

Finally, Guo Jiayu proposed a new concept: energy-efficiency ratio, meaning the sales revenue contributed to the store per unit of electricity. He observed that among stores with the same business revenue in the same city, electricity bill differences are huge, but most operators don’t know exactly where the money is spent. “We hope to manage each unit of electricity, make it transparent, and turn it into value.”

Author: Hongcan editorial team