Mobile Device Load Disaggregation for Granular Energy Monitoring
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Solution Overview
Problem
Current energy management systems lack the ability to provide granular, cost-effective monitoring of energy consumption at the consumer level, relying on manual meter readings or expensive interval-metering systems, and struggle with load disaggregation without requiring multiple appliance sensors.
Innovation Solution
A system and method for monitoring energy consumption using sensors to measure aggregate energy consumption, processing data on mobile devices to create profiles of energy usage, and providing notifications of consumption pattern changes without the need for additional resources or Master Stations, enabling granular data acquisition and load disaggregation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If manual meter readings are used for energy data collection, then cost is reduced, but measurement precision and data granularity deteriorate
Solution Approach 1:
The system enables consumers to self-monitor their energy consumption through mobile devices that automatically collect and process meter data. The consumer's own mobile device serves as the monitoring platform, eliminating the need for expensive external interval-metering systems while providing continuous granular data without manual intervention.
2Measurement precision
If interval-metering systems are installed to automatically measure energy consumption, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The mobile device serves multiple functions: it acts as the data collection platform, processing unit, storage system, and user interface for energy monitoring. By leveraging the universal capabilities of existing mobile devices, the system avoids the complexity of dedicated interval-metering hardware while achieving continuous granular measurement.
Solution Approach 2:
The mobile device serves as an intermediary between the energy meter and the consumer. It automatically collects data from the meter, processes the information, and presents actionable insights to the user, eliminating the need for complex dedicated monitoring hardware while maintaining measurement precision.
3Measurement precision
If power sensors are installed on every appliance for itemized billing, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The system extracts appliance-level energy consumption information from aggregate meter data through automated processing algorithms. Instead of physically installing sensors on each appliance, the system extracts and attributes energy usage to specific devices and time periods through data analysis, maintaining precision without additional hardware complexity.
4Measurement precision
If manual meter reading frequency is increased to improve data granularity, then measurement precision is improved, but loss of time and labor increase
Solution Approach 1:
The system replaces the mechanical process of manual meter reading with automated electronic data collection. The mobile device automatically retrieves energy consumption data from the meter at scheduled intervals, eliminating the need for human intervention while providing continuous granular measurements, thus improving precision without increasing time loss.
Data Source
AI summary
A method and system for use in creating a profile of, managing and understanding power consumption in a premise of a user, wherein said premise comprises two or more power consuming devices comprises measuring, via at least one sensor, aggregate energy consumption at the premise, receiving at a mobile computing device comprising a data processor, said aggregated signal from the sensor, collecting and recording the aggregate signal over a plurality of time resolutions and frequencies, therein to create a predicted aggregate signal for each time x and frequency y, detecting changes in the predicted aggregate signal at time x an frequency y (detected consumption pattern changes) and conveying to at least one of the user, a utility company, and other third party a notification of detected consumption pattern changes.