Utility Data Processing System for Appliance Usage Inference
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Solution Overview
Problem
Current utility consumption management systems rely on infrequent meter readings and user input, which are inefficient and lack detailed insights into appliance usage patterns, hindering effective reduction of electricity, gas, and water waste.
Innovation Solution
A utility data processing system that analyzes consumption data from smart meters to identify appliances, infer usage patterns, and provide users with detailed consumption information without requiring extensive user input, enabling optimized utility management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed information on utility consumption is provided through smart meters and continuous monitoring systems, then measurement precision and information completeness are improved, but device complexity and cost increase
Solution Approach 1:
The system segments utility consumption data by appliance type and usage pattern, breaking down total consumption into identifiable components through analysis of consumption signatures. This allows detailed measurement without requiring separate physical meters for each appliance.
Solution Approach 2:
The system uses an intermediary processing layer that analyzes consumption data patterns to infer appliance-level information from aggregate meter readings. This intermediary analysis provides detailed insights without the complexity of installing multiple smart meters throughout the household.
2Loss of information
If user questionnaires are used to collect detailed household information, then information completeness is improved, but ease of operation and user convenience deteriorate due to time-consuming input requirements
Solution Approach 1:
The system performs self-service by automatically collecting household information through analysis of utility consumption patterns. The inference engine derives appliance inventory, usage behaviors, and household characteristics directly from meter data without requiring user questionnaires or manual input.
Solution Approach 2:
The system replaces the mechanical process of user questionnaire completion with an automated computational process that infers household information from consumption data patterns. This substitution eliminates the need for direct user input while maintaining information completeness.
3Ease of operation
If traditional meter reading systems are used with infrequent readings, then ease of operation and system simplicity are maintained, but loss of information and ability to identify efficiency opportunities increase
Solution Approach 1:
The system maintains the simplicity of traditional meter reading infrastructure while adding multi-functionality through data analysis. The same meter infrastructure provides both traditional billing data and detailed consumption pattern information through computational analysis of the reading data.
Data Source
AI summary
A utility data processing system for processing data relating to consumption of a utility comprises: a fact memory for storage of facts relating to utility consumption received from fact sources, at least one fact source module for deriving facts from utility consumption data and adding the derived facts to the tact memory, an inference module for inferring new facts relating to utility consumption from one or more facts stored in the fact memory, and an interlace module.