Air Conditioning Load Profile Matching for Energy-Efficient Control
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Solution Overview
Problem
Current air conditioning systems lack efficient methods for determining optimal load profiles and user group classification, leading to suboptimal energy usage and control strategies.
Innovation Solution
A method that involves determining performance parameters over time, calculating load characteristics, and using similarity measures with predefined profiles to adjust control signals based on dynamic time warping algorithms, allowing for the classification of air conditioning devices into user groups for improved energy management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If air conditioning systems use traditional control methods without load profile analysis, then the system operation is simple, but energy usage efficiency is suboptimal
Solution Approach 1:
The system performs preliminary analysis of load characteristics by comparing actual performance data with reference load profiles before making control adjustments. This advance preparation enables optimized energy usage without requiring complex real-time control algorithms, as the optimal control strategy is pre-determined through profile matching.
Solution Approach 2:
The system continuously monitors performance parameters, compares them against reference load profiles, and adjusts control signals based on the degree of similarity. This feedback mechanism enables the system to adapt to actual operating conditions while maintaining energy efficiency, resolving the contradiction between simple operation and optimal performance.
2Measurement precision
If air conditioning systems implement detailed load profile analysis and similarity measurements, then control precision is improved, but computational complexity increases
Solution Approach 1:
The system segments the load characteristic analysis into distinct time intervals and compares specific portions of performance data against corresponding sections of reference profiles. This segmentation approach enables precise measurement of load characteristics while keeping computational requirements manageable by focusing on relevant time periods and parameters.
Solution Approach 2:
The system performs similarity measurements only for relevant sections of load profiles rather than analyzing entire profiles in detail. By applying partial action to the most critical time intervals and parameters, the system achieves sufficient measurement precision without requiring excessive computational resources.
3Adaptability or versatility
If air conditioning systems use static control strategies without user group classification, then system operation is straightforward, but adaptability to different user patterns is reduced
Solution Approach 1:
The system automatically classifies users into groups based on their observed load patterns and performance data without requiring manual intervention or complex configuration. This self-service approach enables the system to adapt to different user patterns autonomously, improving versatility while maintaining straightforward operation from the user perspective.
Solution Approach 2:
The system changes control parameters dynamically based on the identified user group and corresponding load profile. By adjusting operational parameters according to pre-determined profile characteristics, the system achieves high adaptability to different user patterns while keeping the control logic itself relatively simple through parameter-based adaptation rather than complex decision-making.
Data Source
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AI summary
The invention relates to a method, an air conditioning device and a system, wherein in a first method step for a first time interval at least one value of a performance parameter of an air conditioning device of the system is determined, wherein in a second method step a time-dependent first load characteristic is determined for the first time interval, wherein in a third method step, a first degree of similarity is determined at least in sections of the first load characteristic with a predefined load profile,-wherein in a fourth method step the determined first degree of similarity is compared with a predefined threshold value, wherein in a fifth method step a first area in which the first degree of similarity is lower than the predefined threshold value is determined,-a first load profile being determined in a sixth method step on the basis of the first area.