Vending Machine Energy Control Using Demand-Based Temperature and Lighting
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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
A system that retrieves demand data from vending machines, analyzes it to identify energy-saving opportunities, and generates an instruction set to optimize the operation of components like condenser fans and lighting systems based on usage patterns, which is then loaded onto the equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Temperature
If preset temperature adjustments are used to maintain beverages at optimum serving temperature during business hours, then beverage quality is improved, but energy consumption increases during non-business hours
Solution Approach 1:
The system dynamically adjusts temperature setpoints based on real-time demand data and predicted customer arrival patterns. During high-demand periods, beverages are maintained at optimum serving temperature. During low-demand periods, the system proactively pre-cools or pre-heats beverages in anticipation of upcoming demand, then allows temperature to drift during low-demand intervals, eliminating the need for continuous energy-intensive temperature maintenance while ensuring readiness when customers arrive.
Solution Approach 2:
The system analyzes historical and real-time demand data to predict future customer arrivals and proactively adjusts temperature and inventory levels in advance. This preliminary action ensures beverages are ready at the correct temperature before customers arrive, rather than maintaining constant temperature regardless of demand, thereby reducing energy consumption during periods when no customers are present.
2Illumination intensity
If preset lighting adjustments are used to advertise beverages during business hours, then product visibility is improved, but energy consumption increases during non-business hours
Solution Approach 1:
The system dynamically controls lighting intensity based on real-time demand data and predicted customer arrival patterns. During high-demand periods, lighting is maintained at high intensity to maximize product visibility and advertising effectiveness. During low-demand periods, the system proactively dims or turns off lighting in anticipation of reduced customer traffic, thereby maintaining product visibility when needed while eliminating unnecessary energy consumption during low-demand intervals.
3Ease of operation
If demand variations are not accounted for in energy management, then equipment operation is simplified, but energy conservation opportunities are lost
Solution Approach 1:
The system automatically collects demand data from sensors and point-of-sale systems, analyzes patterns using machine learning algorithms, and generates optimized control instructions without human intervention. This self-service capability enables the system to account for demand variations and capture energy conservation opportunities automatically, eliminating the need for manual programming while achieving significant energy savings through data-driven dynamic adjustment of temperature and lighting.
Data Source
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AI summary
The present invention provides sy stems, methods, and apparatuses 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. The store or dispense equipment may include vending machines, appliances, coolers, dispensers such as food dispensers and beverage dispensers (e.g., fountain drink dispenser), and other iike electrical equipment. Demand data may be retrieved from the vending machine, appliance, cooler, dispenser, or other store or dispense equipment. Demand data may include saies. usage, and/or occupancy information for the respect 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 vending machine, appliance, or other equipment, w hich operates in accordance with the instruction set. While the following embodiments of the present invention maybe discussed w ith respect to a vending machine for illustrative purposes, they are equally applicable to appliances, coolers, dispensers, and other store or dispense equipment.