Window Air Conditioner Setpoint Control for Peak Demand Response
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Solution Overview
Problem
Household appliances, such as window air conditioners, face challenges in optimizing energy usage to align with variable utility rates, leading to potential energy cost savings, as existing solutions are not sophisticated enough to efficiently manage peak and off-peak demand periods.
Innovation Solution
A controller system for the air conditioner that adjusts its operation based on historical data, including compressor run time and temperature changes, to selectively adjust the set-point temperature and operational mode in response to energy demand signals, incorporating features like override functions and load reduction strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the air conditioner operates during peak demand periods to maintain comfort, then comfort level is improved, but energy cost increases
Solution Approach 1:
The system pre-cools the space before peak demand periods by lowering the setpoint temperature, storing cooling capacity in the building thermal mass. This allows the air conditioner to operate at reduced capacity or shut down during peak periods while maintaining comfort, thereby reducing energy costs during high-rate periods
Solution Approach 2:
The setpoint temperature is dynamically adjusted based on predicted demand prices and historical operation data. The system transitions from static setpoints to time-varying setpoints that optimize the trade-off between comfort and energy cost by pre-conditioning the space during low-cost periods
2Use of energy by moving object
If the air conditioner shuts down during peak demand to reduce energy cost, then energy cost is reduced, but comfort level deteriorates
Solution Approach 1:
The system builds up thermal energy storage in the building structure before peak demand periods by operating at lower setpoints. This thermal cushion acts as a buffer that maintains comfort during shutdown periods, allowing the system to reduce energy cost without sacrificing comfort
3Use of energy by moving object
If the controller uses historical operating data to adjust set-point, then energy savings are improved, but device complexity increases
Solution Approach 1:
The controller automatically analyzes historical operation data and demand price signals to generate optimized setpoint schedules without requiring manual programming or user intervention. The system self-adjusts based on learned patterns, reducing the need for complex user-configurable parameters while achieving energy savings
Data Source
AI summary
An appliance for conditioning air of an associated room and an associated method for controlling an air conditioner are disclosed, the controller selectively adjusting operation of the air conditioning appliance based on historical operating data. The controller adjusts a set-point of the appliance for a preselected period of time in response to the historical operating data of the appliance when the appliance operates in an energy savings mode. The controller is configured to receive and process data relating to the rate of change in the temperature. Further, an override feature may be included to maintain operation of the appliance in a normal operation mode if ambient temperature reaches a predetermined threshold value. Another feature is that the controller determines whether the compressor has been operational less than a preselected period of time and, if so, the compressor is operated until such time period has elapsed.