Demand-Responsive Vending Machine Energy Management
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Solution Overview
Problem
Vending machines and similar equipment face inefficiencies in energy management due to preset temperature and lighting adjustments not accounting for daily demand variations, leading to suboptimal energy conservation.
Innovation Solution
Implementing a system that retrieves and analyzes demand data from vending machines and other equipment to generate and load instruction sets that dynamically adjust operational modes, including temperature and lighting settings, based on demand patterns, allowing for energy conservation opportunities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If preset temperature and lighting adjustments are used to conserve energy during non-business hours, then energy consumption is reduced, but the adjustments do not account for daily demand variations leading to suboptimal energy conservation
Solution Approach 1:
The system dynamically adjusts temperature and lighting settings based on real-time demand data rather than using fixed preset adjustments. The controller continuously monitors sales transactions and modifies operational parameters to match actual demand patterns, enabling the vending machine to adapt flexibly to varying consumer needs throughout different days and time periods while optimizing energy conservation.
Solution Approach 2:
The system implements a feedback mechanism where demand data from sales transactions is continuously collected and analyzed, then used to adjust temperature and lighting settings. This closed-loop control ensures that energy management decisions are based on actual consumer behavior patterns, allowing the system to learn and adapt to demand variations automatically without manual reprogramming.
2Adaptability or versatility
If preset adjustments are manually reprogrammed by service technicians or route drivers, then temperature and lighting settings can be modified, but there are no incentives for them to do so and the process is time-consuming
Solution Approach 1:
The vending machine performs self-adjustment of temperature and lighting settings automatically based on analyzed demand data. The controller autonomously modifies operational parameters without requiring manual intervention from service technicians or route drivers, eliminating the time-consuming reprogramming process while maintaining full adaptability to demand patterns.
Solution Approach 2:
The system pre-calculates optimal temperature and lighting settings based on historical demand patterns and consumer behavior analysis. By preparing adjustment schedules in advance based on data trends, the system can automatically implement appropriate settings without requiring last-minute manual reprogramming when technicians or drivers arrive at locations.
3Reliability
If beverages are maintained at optimum serving temperature during business hours, then customer satisfaction is improved, but energy consumption increases during non-business hours
Solution Approach 1:
The system dynamically adjusts beverage temperature based on real-time demand data, maintaining optimum serving temperature during high-demand periods and allowing temperature to rise during low-demand periods when energy conservation is prioritized. This dynamic temperature management ensures service quality when needed while reducing energy expenditure during non-business hours.
Solution Approach 2:
The controller changes operational parameters including temperature setpoints and lighting intensity based on analyzed demand patterns. By adjusting these parameters according to actual consumer behavior rather than fixed schedules, the system optimizes the balance between maintaining service quality and reducing energy consumption across different time periods and days.
Data Source
AI summary
System, methods, and apparatuses are provided for energy management in store or dispense equipment for food and/or beverages. Such store or dispense equipment may allow for food and/or beverages to be heated, cooled, or maintained near the ambient temperature, or a combination thereof. Demand data may be retrieved from the store or dispense equipment. Demand data may include sales, usage, and/or occupancy information for the respective vending machines, appliances, coolers, dispensers, and other store or dispense equipment. This demand data may then analyzed to determine whether there are demand patterns such that there are opportunities for energy conservation or energy management. If there are opportunities for energy conservation or energy management, an instruction set may be prepared, where the instruction set is based at least in part on this analyzed data. The instruction set is loaded onto the store or dispense equipment, which operates in accordance with the instruction set.