Air Conditioning Set-Temperature Control for Comfort-Energy Balance
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Solution Overview
Problem
Air conditioning devices face a trade-off between energy-saving effects and user comfortability, as prioritizing energy savings often compromises comfort, and existing systems struggle to optimize set temperatures based on user behavior and space occupancy.
Innovation Solution
An air conditioning control device that acquires behavior information and set temperature history to calculate set-temperature duration times and features, determining comfortable and energy-efficient temperatures by learning user preferences and adjusting set points accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the set temperature is adjusted to prioritize energy-saving effects, then energy consumption is reduced, but user comfortability is deteriorated
Solution Approach 1:
The system performs preliminary learning of user temperature preferences during a learning period by collecting and analyzing historical temperature setting data. This preliminary action enables the system to determine optimal set temperatures in advance that balance energy savings with user comfort, rather than making adjustments reactively
Solution Approach 2:
The system implements feedback by continuously monitoring user temperature adjustments and occupancy patterns, then using this information to refine future temperature settings. The control device learns from past user behavior and adjusts set temperatures accordingly, creating a closed-loop system that improves energy efficiency while maintaining comfort
2Ease of operation
If the set temperature is adjusted to prioritize user comfortability, then comfort is improved, but energy-saving effects are difficult to achieve
Solution Approach 1:
The system determines optimal set temperatures in advance during the learning period by analyzing historical data, so that comfortable temperatures are established before the cooling or heating season begins. This preliminary determination allows the system to maintain user comfort while optimizing for energy savings from the start of operation
Solution Approach 2:
The system dynamically changes the set temperature parameter based on learned user preferences and occupancy patterns. Instead of using fixed high or low temperature settings, the control device adjusts the set temperature within an optimal range that balances comfort and energy efficiency, transforming a static parameter into a dynamic one
3Ease of operation
If historical temperature data is used to calculate average preferred temperatures, then user comfort is maintained, but energy-saving opportunities are lost
Solution Approach 1:
Instead of simply averaging historical temperatures, the system performs excessive analysis by calculating duration times for each temperature setting and determining temperature preferences based on occupancy periods. This partial focus on duration and timing rather than just average values reveals energy-saving opportunities that simple averaging would miss
Solution Approach 2:
The system transitions from static average temperature calculation to dynamic temperature preference determination that considers duration times and occupancy patterns. The optimal set temperature is determined based on when users are actually present and what temperatures they prefer during those periods, making the system adaptive rather than static
4Measurement precision
If the learning period is extended to gather more temperature history data, then temperature preference accuracy is improved, but time consumption increases
Solution Approach 1:
The system replaces simple data collection with intelligent data analysis by calculating duration times for each temperature setting and using this temporal information to determine preferences. This substitution of mechanical averaging with analytical processing based on duration and occupancy patterns achieves accurate temperature preference determination more efficiently
Solution Approach 2:
The system performs preliminary determination of temperature preferences during the learning period by analyzing duration times and occupancy patterns, rather than waiting for the learning period to end and then processing data. This preliminary analysis accelerates the determination process while maintaining accuracy
Data Source
Figure 1
Figure 2A~2B
Figure 3A~3D
AI summary
According to one approach, an air conditioning control device includes: a measurement information acquirer, a duration time calculator, a feature calculator and a determiner. The measurement information acquirer acquires history information related to a set temperature of an air conditioning device. The duration time calculator calculates, for each of a plurality of set temperatures, a plurality of set-temperature duration times that are respective duration times of the set temperature based on the history information. The feature calculator calculates features related to respective duration times of the set temperatures based on the respective calculated set-temperature duration times. The determiner calculates respective evaluation values for the plurality of set temperatures based on the features and determines a set temperature to be instructed to the air conditioning device based on the evaluation values.