Power Consumption Behavior Analysis Using Household Feature Correlation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing power consumption analysis methods rely solely on power consumption curves and struggle to accurately interpret and provide effective recommendations for adjusting consumption patterns, making it difficult to offer clear energy-saving solutions for households.
Innovation Solution
A power consumption behavior analyzing device and method that generates power consumption curves, extracts feature points, performs correlation analysis with household data to identify key features, clusters data to define power consumption patterns, and calculates similarities to classify household power consumption patterns, enabling customized recommendations for energy-saving adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If power consumption analysis methods rely solely on power consumption curves, then the analysis process is simple, but the interpretation accuracy and effectiveness of recommendations deteriorate
Solution Approach 1:
The patent combines power consumption curve analysis with household feature data (number of members, age structure, appliance ownership) to create a comprehensive analysis system. This merging of multiple data sources improves interpretation accuracy while maintaining reasonable process complexity through automated correlation analysis and clustering algorithms.
Solution Approach 2:
The patent introduces household feature data as an intermediary element that bridges power consumption curves and actionable recommendations. This intermediary data layer enables more accurate pattern recognition and generates tailored recommendations by considering both consumption behavior and household characteristics.
2Device complexity
If power consumption analysis methods only use power consumption curves, then the system complexity is low, but the ability to provide effective energy-saving recommendations deteriorates
Solution Approach 1:
The patent segments the analysis process into distinct modules: power consumption curve generation, feature point extraction, correlation analysis with household features, clustering into patterns, and recommendation generation. This segmentation improves recommendation effectiveness while keeping each module relatively simple and manageable.
Solution Approach 2:
The patent changes the parameters used for analysis by incorporating multiple dimensions: time-based consumption patterns, household demographic parameters, appliance-type breakdowns, and correlation thresholds. This multi-parameter approach enhances recommendation effectiveness without creating an overly complex system.
3Device complexity
If power consumption analysis does not consider household features, then the analysis is straightforward, but the accuracy of power consumption behavior classification deteriorates
Solution Approach 1:
The patent creates a universal analysis framework that handles multiple analysis tasks simultaneously: curve generation, feature extraction, correlation analysis, clustering, and recommendation generation. This multi-functional approach improves classification accuracy while maintaining analysis straightforwardness through a unified processed.
Data Source
AI summary
A power consumption behavior analyzing device and a power consumption behavior analyzing method are provided. The power consumption behavior analyzing method includes: generating, according to power consumption data, power consumption curves of household ends, and extracting feature points; acquiring household data records corresponding to the households, respectively; performing a correlation analysis according to the household data records and the power consumption data of the feature points to find household features corresponding to correlations of the feature points as key features based on a correlation threshold value; clustering, according to the key features, total power consumption data to obtain a plurality of household power consumption characteristic curves and a plurality of power consumption patterns; and calculating similarities respectively between a power consumption curve of a to-be-analyzed household end and the household power consumption characteristic curves, and marking the to-be-analyzed household end as a corresponding power consumption pattern.