Gas AMI Data Analysis for Household Energy Consumption Comparison
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Solution Overview
Problem
Current methods for analyzing and reducing household gas energy consumption are inadequate, as existing feedback data is fragmented and does not effectively encourage energy savings, as households lack realistic and intuitive information for comparison.
Innovation Solution
A method utilizing machine learning-based analysis of Advanced Metering Infrastructure (AMI) data to collect and compare gas energy consumption patterns across households, providing feedback on similar energy usage and consumption patterns to induce efficient energy use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional gas bill feedback data is provided showing only current charges and usage amounts, then the billing system remains simple and easy to operate, but the feedback is fragmentary and does not effectively urge consumers to reduce energy consumption
Solution Approach 1:
The patent segments feedback information into multiple dimensions: temporal comparison (current vs. historical usage), spatial comparison (household-specific vs. peer group averages), and contextual comparison (usage patterns during specific time periods). This segmentation transforms fragmentary data into comprehensive insights while maintaining system simplicity through automated processing.
Solution Approach 2:
The patent adds new dimensions to traditional billing feedback by incorporating peer group comparisons (spatial dimension) and temporal patterns (time dimension) alongside the traditional temporal comparison. This multi-dimensional approach provides realistic and intuitive information for energy saving without complicating the underlying billing system.
2Loss of information
If detailed AMI data collection and machine learning analysis are implemented to provide comprehensive energy consumption insights, then realistic and intuitive feedback for energy saving is achieved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent introduces an intermediary analysis system that sits between the AMI data collection infrastructure and the consumer feedback interface. This intermediary performs machine learning-based pattern recognition, peer group matching, and insightful generation, transforming raw data into actionable feedback while shielding consumers from system complexity.
Solution Approach 2:
The patent implements multi-loop feedback mechanisms: immediate feedback on current usage vs. historical usage, comparative feedback against peer groups, and pattern-based feedback on consumption behaviors. These feedback loops provide realistic and intuitive information that effectively urges energy conservation without requiring consumers to understand the complex analysis processes.
3Loss of information
If peer group comparison data is provided to help households understand their consumption patterns, then energy saving motivation is enhanced, but data privacy and security requirements increase
Solution Approach 1:
The patent applies local quality by providing customized peer group comparisons tailored to each household's specific characteristics (house size, occupancy patterns, usage behaviors). Instead of generic comparisons, each household receives feedback against a carefully selected peer group that matches their local context, enhancing motivation while maintaining privacy through aggregated and anonymized data.
Solution Approach 2:
The patent uses copying by creating anonymized and aggregated representations of peer group consumption patterns rather than sharing individual household data. This allows realistic and intuitive comparison information to be provided while protecting privacy, as households see patterns and averages rather than sensitive personal data from other households.
Data Source
AI summary
There is provided a gas AMI data-based household gas energy consumption analysis and comparison method. An energy consumption analysis method according to an embodiment may include: collecting AMI data of a plurality of households; collecting household information of the plurality of households; analyzing energy consumption of each household, based on the collected AMI data; and providing household information of households that have similar energy consumption, based on a result of analyzing. Accordingly, information of other households that use the same/similar energy or have the same/similar energy consumption pattern may be fed back as comparison information, so that efficient use of energy and energy saving may be effectively induced.