Automated Household Energy Management via Appliance Classification
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Solution Overview
Problem
Current household energy management systems lack the ability to effectively divide energy consumption data to the appliance level, limiting consumers' ability to make informed decisions for energy savings and environmental sustainability, as they rely on general consumption monitoring rather than detailed appliance usage.
Innovation Solution
An automated household energy management system that uses appliance classification, location information, and a household energy profile to monitor and adjust energy consumption based on user preferences and environmental factors, providing suggestions for reducing energy usage through automated control and user guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If general consumption monitoring is used, then system complexity is reduced, but measurement precision of energy consumption at appliance level deteriorates
Solution Approach 1:
The patent segments the household energy consumption monitoring into two levels: aggregate monitoring for overall system overview and appliance-specific monitoring for detailed analysis. The system divides total energy consumption into individual appliance contributions using classification data, location information, and energy profiles, allowing precise measurement without requiring complex dedicated metering for each appliance.
Solution Approach 2:
The system uses a single monitoring infrastructure that serves multiple functions: it tracks overall household consumption, identifies appliance-level consumption patterns, and provides data for both immediate control decisions and long-term profile development. This multi-functional approach eliminates the need for separate dedicated metering devices for each appliance.
2Loss of energy
If automated control is implemented, then energy consumption is reduced, but device complexity increases
Solution Approach 1:
The system implements self-service through automated control algorithms that independently analyze energy consumption data, identify optimization opportunities, and execute control actions without requiring continuous user intervention. The household energy profile automatically learns from past consumption patterns and autonomously generates control strategies, reducing the need for complex user interfaces and manual configuration.
Solution Approach 2:
The system employs feedback mechanisms where energy consumption data is continuously monitored, compared against the household energy profile, and used to adjust control strategies. The deviation analysis provides feedback on the effectiveness of implemented measures, allowing the system to automatically refine its approach and maintain optimal energy consumption without increasing operational complexity.
3Measurement precision
If detailed appliance-level monitoring is implemented, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the monitoring function by combining aggregate monitoring with appliance-level analysis through data processing rather than physical segmentation. A single monitoring system captures overall consumption and uses classification information, location data, and energy profiles to mathematically decompose consumption into appliance-specific components, achieving detailed measurement precision without the complexity of multiple dedicated sensors.
Solution Approach 2:
The system introduces an intermediary layer of data processing that bridges between simple aggregate monitoring and complex appliance-level measurement. By using household energy profiles, classification data, and location information as intermediaries, the system translates overall consumption patterns into appliance-specific insights without requiring direct complex measurement infrastructure at each appliance.
4Loss of energy
If user behavior adaptation is required, then energy savings are achieved, but loss of time for user education and behavior change increases
Solution Approach 1:
The system performs preliminary action by proactively analyzing energy consumption data and automatically implementing control measures before users need to manually intervene. The household energy profile is built in advance from historical data, and the system preemptively identifies and executes energy-saving opportunities, reducing the time users would otherwise need to spend learning about and implementing energy-saving behaviors.
Solution Approach 2:
The system enables self-service by automatically guiding users through energy-saving opportunities and implementing controls without requiring extensive user education. The system independently processes complex energy optimization tasks and presents simplified recommendations or automated actions, significantly reducing the time investment needed from users compared to traditional energy conservation approaches.
Data Source
AI summary
A household energy management system and method, having one or more controlled appliances and an energy profile representing energy consumption, with one or more associated parameters and limits, wherein each of the controlled appliances are assigned a control class, a set of variable values collected, a first group of appliances determined based on the control class, parameters and collected values, a change requested in their power consumption, and the energy consumption of the household measured to calculate a difference between the consumption and energy profile, and a proposed change in the power consumption of appliances of a second group different from the first group is communicated if the difference surpasses one or more of the associated limits, the second group of appliances being determined on basis of the class, parameters and collected values, and a change in power consumption of one or more appliances of the second group requested.