Utility Meter Usage Detection Without Appliance Sensors
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Solution Overview
Problem
Existing methods for monitoring energy consumption in properties require sensors connected to appliances, which can be difficult to obtain and may not provide accurate or comprehensive data for assessing device usage.
Innovation Solution
A method that receives consumption data from utility meters, identifies variations indicative of device switching, groups events into blocks, and classifies them into clusters to determine device usage, enabling detection of normal or abnormal usage patterns without the need for appliance-specific sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are connected to appliances for monitoring energy consumption, then measurement precision is improved, but device complexity and ease of operation worsen due to difficult sensor installation and data acquisition
Solution Approach 1:
The patent uses utility meter data as an intermediary to indirectly measure appliance energy consumption. Instead of connecting sensors directly to appliances, the system monitors utility meter consumption data and uses this as a proxy measurement, eliminating the need for complex sensor installations while still achieving energy consumption monitoring goals
Solution Approach 2:
The system creates a virtual model of appliance usage by analyzing utility meter data patterns. It copies the essential information needed for monitoring (energy consumption patterns) from the utility meter data without requiring physical sensors on each appliance, thus simplifying the system while maintaining measurement capability
2Reliability
If sensors are connected to appliances for monitoring, then reliability of usage data is improved, but ease of operation worsens due to difficulty in obtaining sensor data
Solution Approach 1:
The utility meter serves as an intermediary data source that provides reliable energy consumption information without requiring direct appliance sensor connections. The system processes utility meter readings to infer appliance usage patterns, maintaining data reliability while dramatically improving ease of data acquisition
Solution Approach 2:
The system uses already-existing utility meter infrastructure to provide the data needed for monitoring. Instead of requiring separate sensor installations on each appliance, the solution leverages the self-service capability of utility meters that already record consumption data, making the system easier to implement while maintaining reliability
3Measurement precision
If sensor data from individual appliances is collected, then measurement precision is improved, but device complexity worsens due to need for multiple sensors and data management systems
Solution Approach 1:
The patent merges the monitoring function for multiple appliances into a single utility meter data stream. Instead of requiring separate sensors for each appliance, the system combines all energy consumption measurements from the utility meter and uses pattern recognition to identify individual appliance usage, significantly reducing system complexity while maintaining detection precision
Solution Approach 2:
The utility meter data serves multiple functions simultaneously: it provides overall energy consumption monitoring, enables individual appliance usage detection through pattern analysis, and supports various monitoring objectives without requiring different sensor systems. This multi-functionality eliminates the need for complex sensor networks
Data Source
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Figure 3A~3B
AI summary
In one aspect, there is provided a method comprising: receiving consumption data comprising readings from one or more utility meters associated with a property comprising one or more devices, the one or more devices comprising one or more devices of interest; determining, in the received consumption data,one or more positive consumption variations indicative of switching on of one or more of the devices and/or one or more negative consumption variations indicative of switching off of one or more of the devices; identifying one or more events associated with the one or more devices, based on the determined variations, by matching one or more positive variations with one or more negative variations; grouping the identified one or more events into one or more blocks, each block corresponding to an occurrence of usage of a device of the property; classifying the one or more blocks into one or more predetermined clusters, the one or more predetermined clusters comprising a respective predetermined cluster associated with each device of interest of the property; and determining an occurrence and/or an absence of usage of the one or more devices of interest of the property, based on the classification into the one or more predetermined clusters.