Air-conditioning control method, air-conditioning control apparatus, and storage medium
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Solution Overview
Problem
Existing air-conditioning control systems face challenges in accurately predicting in-room temperature changes and energy consumption, leading to inefficient heating or cooling and increased power usage, especially when the air-conditioner is remotely controlled with a large time difference between setting and return-home times.
Innovation Solution
An air-conditioning control method that stores in-room temperature history and operation data to predict future temperatures, determining control parameters to achieve a target temperature while minimizing energy consumption, using a cloud server and machine learning to analyze past data and adjust settings based on user behavior and environmental factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the air-conditioning apparatus is remotely controlled with a large time difference between setting and return-home times, then the user can set the temperature in advance, but the accuracy of temperature prediction and control deteriorates due to environmental changes and performance degradation
Solution Approach 1:
The system performs preliminary actions by storing temperature history information and operation history information before the target time, and predicts the off-state temperature in advance to determine optimal control parameters, enabling accurate temperature control even with large time differences between setting and return-home times
Solution Approach 2:
The system implements feedback by continuously storing and analyzing temperature history information and operation history information, using the predicted off-state temperature to adjust control parameters, thereby improving temperature prediction accuracy and adapting to environmental changes and performance degradation over time
2Ease of operation
If the air-conditioning apparatus operates to maintain target temperature, then comfort is improved, but energy consumption increases
Solution Approach 1:
The system performs preliminary temperature adjustment before the user returns home by determining control parameters based on predicted off-state temperature, achieving the target temperature just in time while minimizing unnecessary operation time and reducing energy consumption
Solution Approach 2:
The system dynamically adjusts control parameters based on the predicted off-state temperature and target temperature requirements, optimizing the operation timing and intensity of the air-conditioning apparatus to balance comfort and energy efficiency
Data Source
AI summary
A cloud server includes an environment history DB storing in-room temperature history information representing a history of an in-room temperature change in a living room whose temperature is adjusted by an air-conditioner in relation to operation history information representing an operation history of the air-conditioner, an in-room environment predictor that predicts, as a predicted off-state in-room temperature, a future in-room temperature of the living room based on the in-room temperature history information and the operation history information for a case where the temperature is not adjusted by the air-conditioning apparatus, and an air conditioning setting unit that determines, based on the predicted off-state in-room temperature, a control parameter of the air-conditioner used to control the in-room temperature so as to reach a target temperature at a target time.