Appliance Consumption Monitoring via Binary State Sensing
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Solution Overview
Problem
Existing methods for monitoring power and energy consumption in electrical networks are inefficient, particularly in large buildings, due to the need for extensive deployment of power meters and the complexity of non-stationary power profiles, which increases costs and reduces accuracy.
Innovation Solution
The use of binary power state sensors and a methodology that estimates energy consumption breakdown by collecting data on appliance ON/OFF states and total power consumption, allowing for optimal placement of additional power meters to enhance estimation certainty, thereby reducing the number of required meters and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If power meters are deployed at all outlets to accurately measure individual appliance consumption, then measurement precision is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent introduces binary power state sensors as intermediary devices that detect whether appliances are ON or OFF. These sensors act as mediators between the appliances and the central processing system, providing state information that helps infer individual appliance consumption without requiring direct power measurement at each outlet. This reduces the number of power meters needed while maintaining reasonable measurement accuracy.
Solution Approach 2:
The patent replaces the traditional mechanical/electrical power measurement system with a hybrid system that uses binary state sensing combined with computational algorithms. Instead of using power meters to directly measure electrical consumption at each outlet, the system substitutes this with binary sensors that detect power states and uses processing unit algorithms to calculate individual appliance consumption from aggregate power data and state information.
2Measurement precision
If power meters are deployed at all outlets to monitor each appliance, then measurement precision is improved, but installation cost increases
Solution Approach 1:
The patent employs binary power state sensors that are significantly cheaper than full power meters. These low-cost binary sensors provide sufficient information (ON/OFF state) for the estimation algorithm to work effectively. The system trades off some measurement detail for substantial cost reduction, using inexpensive binary sensing combined with computational processing to achieve accurate energy breakdown monitoring.
3Device complexity
If binary power state sensors are used for all appliances, then device complexity is reduced, but measurement precision deteriorates due to lack of detailed power data
Solution Approach 1:
The patent implements a feedback mechanism where the processing unit continuously receives binary state data from sensors, compares it with aggregate power consumption data, and uses algorithms to infer individual appliance consumption. The system processes the binary state information through computational feedback loops that refine consumption estimates by analyzing patterns in appliance state changes and their relationship to total power usage variations.
Solution Approach 2:
The patent changes the measurement parameter from continuous power values to binary state values (ON/OFF). This parameter transformation simplifies the sensing requirement while the processing unit compensates by using algorithms that analyze temporal patterns and correlations in the binary state data combined with aggregate power measurements to reconstruct individual appliance consumption profiles.
4Device complexity
If a single power meter is used for the entire network, then device complexity is minimized, but measurement precision is insufficient for individual appliance monitoring
Solution Approach 1:
The patent segments the monitoring function into two parts: a single power meter that measures aggregate consumption and multiple binary state sensors that track individual appliance states. This segmentation allows the system to divide the measurement task between devices with different capabilities, using the single power meter for total consumption and binary sensors for state information, which together enable individual appliance breakdown through computational processing.
Data Source
AI summary
Systems and methods are provided for estimating power breakdowns for a set of one or more appliances inside a building by exploiting a small number of power meters and data indicative of binary power states of individual appliances of such set. In one aspect, a breakdown estimation problem is solved within a tree configuration, and utilizing a single power meter and data indicative of binary power states of a plurality of appliances. Based at least in part on such solution, an estimation quality metric is derived. In another aspect, such metric can be exploited in a methodology for optimally placing additional power meters to increase the estimation certainty for individual appliances to a desired or intended level. Estimated power breakdown and energy breakdown—individually or collectively referred to as consumption breakdown—rely on measurements and numerical simulations, and can be evaluated in exemplary electrical network utilizing binary sensors.