Appliance Load Disaggregation via State Machine Matching
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Solution Overview
Problem
Current energy management systems fail to accurately allocate energy consumption within households, making it difficult to identify and reduce energy usage by specific appliances, which is crucial for elderly and disabled populations who require continuous care and energy-efficient living solutions.
Innovation Solution
A system that includes an electric meter, a detector, a clusterizer, and an analyzer to disaggregate building composite load profiles into individual appliance energy loads, using regression analysis and appliance state machines to identify patterns and provide users with cost-saving recommendations for energy-efficient appliances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If energy consumption is reported as a lump sum for the entire building, then the reporting system is simple and easy to implement, but it is impossible to identify energy usage by specific appliances or end uses
Solution Approach 1:
The patent segments the building's total energy consumption into individual appliance-level measurements using smart meters and load disaggregation technology. Each appliance or end use is monitored separately, enabling precise allocation of energy consumption without requiring physical modification of the appliances themselves.
Solution Approach 2:
The patent introduces an intermediary energy management system that sits between the utility meter and the end-user. This system uses regression analysis and machine learning to disaggregate total energy consumption into appliance-specific usage, providing detailed information without requiring direct modification of each appliance.
2Measurement precision
If advanced metering systems record energy use by day, hour, or minute, then temporal resolution of energy data is improved, but the data remains aggregated at the household level and cannot be allocated to specific devices
Solution Approach 1:
The patent segments aggregated energy data into appliance-specific measurements using load disaggregation algorithms. By analyzing patterns in the aggregated data over time, the system reconstructs individual appliance usage profiles, recovering the appliance-level information that was lost in aggregation.
Solution Approach 2:
The patent performs preliminary data collection and processing by recording detailed temporal energy consumption patterns before the disaggregation analysis is applied. This preliminary action ensures that sufficient data is captured to enable accurate appliance-level allocation through subsequent regression analysis and machine learning.
3Measurement precision
If regression analysis is used to determine climate control and non-climate control energy use, then energy allocation accuracy is improved, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent segments the energy consumption into distinct categories (climate control vs. non-climate control) using regression analysis. By separating these categories based on their different relationships with outdoor temperature, the system achieves accurate allocation without requiring complex multi-variable models for the entire building.
Solution Approach 2:
The patent changes the parameter being analyzed from total energy consumption to temperature-adjusted energy consumption. By regressing energy use against outdoor temperature differences, the system isolates climate control loads and separates them from non-climate control loads, simplifying the allocation process while maintaining accuracy.
Data Source
AI summary
A system for detecting individual appliance energy loads from a building composite load profile includes an electric meter to capture building composite load profile; a detector coupled to the meter to detect transitions in the load profile to determine an appliance state machine for each appliance; a clusterizer to detect clusters of patterns in the load profile; and an analyzer coupled to the detector to receive the transitions and appliance state machines from the detector, the analyzer matching each transition to a predetermined appliance state machine to disaggregate the building composite load profile into individual appliance energy loads.