Predictive Temperature Control for HVAC Energy Optimization
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Solution Overview
Problem
Existing air conditioner control systems lack efficiency due to manual operation, leading to unnecessary power consumption and user discomfort caused by inappropriate temperature settings.
Innovation Solution
A method and apparatus for predicting the amount of temperature change in a target zone by collecting and analyzing base information on indoor and outdoor temperature differences during late night hours, using activity schedule, sunrise, and sunset times to set the late night time section.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the desired temperature of the air conditioner is set to be low to reduce high indoor temperature quickly, then the indoor temperature can be reduced rapidly, but the power consumption of the air conditioner is increased
Solution Approach 1:
The system performs preliminary actions by collecting base information during late night time sections and calculating base relationship information between indoor/outdoor temperature differences and temperature changes. This predictive model is established in advance to forecast temperature changes during active periods, enabling proactive temperature management that avoids excessive cooling and reduces power consumption while maintaining comfortable temperatures.
Solution Approach 2:
The system implements feedback by continuously collecting base information from temperature sensors and activity schedule information, processing this data to calculate temperature change predictions, and using these predictions to adjust air conditioner operation. This closed-loop feedback mechanism ensures temperature control optimizes both comfort and energy efficiency based on actual environmental conditions and occupancy patterns.
2Use of energy by moving object
If the manager sets the desired temperature of the air conditioner to be high, then the power consumption is reduced, but the users can feel the heat and feel uncomfortable
Solution Approach 1:
The system establishes predictive models during late night periods when the space is unoccupied, calculating base relationship information between temperature differences and thermal characteristics of the space. This preliminary analysis enables accurate temperature change predictions during occupied periods, allowing the system to maintain user comfort while optimizing power consumption by avoiding both excessive heating and cooling.
Solution Approach 2:
The system provides self-service by automatically collecting base information, calculating temperature change predictions, and controlling air conditioner operation without requiring manual intervention from managers or users. The predictive model autonomously adjusts temperature settings based on calculated relationships between indoor/outdoor temperature differences and observed temperature changes, ensuring both comfort and energy efficiency.
3Ease of operation
If the air conditioner is driven without direct manipulation by the person, then the ease of operation is improved, but the accuracy of temperature control deteriorates
Solution Approach 1:
The system performs preliminary data collection during late night time sections when the space is unoccupied, gathering base information about temperature changes in response to indoor/outdoor temperature differences. This advance data collection enables the development of accurate predictive models that capture the specific thermal characteristics of the space, ensuring high temperature control accuracy while maintaining full automation during occupied periods.
Solution Approach 2:
The system replaces manual temperature control operations with an automated intelligent control system that uses predictive algorithms. Instead of relying on manual thermostat adjustments, the system substitutes mechanical/manual control with computational prediction based on calculated relationships between temperature differences and observed temperature changes, achieving both high automation and precise temperature control.
Data Source
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AI summary
Disclosed are an apparatus and a method for predicting an amount of temperature change of a target zone. The disclosed method includes: collecting a plurality of base information; and calculating base relationship information between an indoor/outdoor temperature difference of the target zone and an amount of temperature change of the target zone on the basis of the plurality of base information, wherein each of the plurality of base information is information on the amount of temperature change of the target zone according to the indoor/outdoor temperature difference of the target zone during a late night time section, and the late night time section is set on the basis of at least one of activity schedule information, a sunrise time point, and a sunset time point of the target zone.