Power Consumption Analysis Using Environmental Similarity Modeling
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Solution Overview
Problem
Existing power analysis methods struggle to accurately estimate user power consumption habits due to high construction costs and limited understanding of power consumption patterns, leading to inaccurate analysis results.
Innovation Solution
A power analysis system and method that utilizes user electricity meters and an analysis server to integrate power consumption and environmental data, employing machine learning to establish an analysis model that calculates power consumption composition by analyzing similarities between reference and target samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional power analysis methods are used, then the system construction cost is reduced, but the measurement precision of power consumption estimation deteriorates
Solution Approach 1:
The patent introduces environmental data (temperature, humidity, air quality) as an intermediary element to bridge the gap between limited detection devices and accurate power consumption estimation. The analysis server uses these environmental parameters as additional features in the machine learning model to improve estimation accuracy without requiring complex detection devices at every location
Solution Approach 2:
The patent replaces the traditional mechanical/detector-based power consumption measurement system with a data-driven machine learning approach. Instead of using complex detection devices to directly measure each electrical device's power consumption, the system uses environmental sensors and ML algorithms to infer and estimate power consumption patterns, significantly reducing hardware complexity
2Measurement precision
If detection devices are installed for each electrical device, then the measurement precision is improved, but the device complexity increases
Solution Approach 1:
The patent segments the power consumption analysis into two parts: environmental data collection (using少量 environmental sensors) and power consumption estimation (performed by the analysis server using ML models). This segmentation allows the system to achieve accurate estimation without installing detection devices on every electrical device, as the heavy computational task is separated from the physical measurement layer
Solution Approach 2:
The patent creates a virtual model of power consumption patterns through machine learning that copies and replicates the behavior of actual electrical devices. The analysis server builds predictive models that simulate how different electrical devices consume power under various environmental conditions, eliminating the need for physical detection devices on each device while maintaining estimation accuracy
Data Source
AI summary
A power analysis method, comprising: receiving multiple reference power consumption data; obtaining multiple reference environment data corresponding to the multiple reference power consumption data, wherein the multiple reference power consumption data and the multiple reference environment data are used as multiple reference samples to establish an analysis model; receiving a target power consumption data, and obtaining a target environment data corresponding to the target power consumption data; using the target power consumption data and the target environment data as a target sample to analyze a similarity between the target sample and the multiple reference samples by the analysis model; and calculating an power consumption composition data according to the similarity by the analysis model, wherein the power consumption composition data corresponds to the multiple electrical devices of the plurality of types.