Household Energy Prediction Using Similar Home Device Profiles
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Solution Overview
Problem
New households applying for energy usage prediction services face challenges as they lack past energy usage information, preventing accurate prediction and energy reduction commands.
Innovation Solution
An electronic apparatus uses stored home device information and electrical power usage data from multiple households to predict the new household's power usage by updating and correcting data, employing neural networks for accurate forecasting and controlling device operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If energy usage prediction service is provided to new households, then user convenience is improved, but prediction accuracy deteriorates due to lack of past energy usage information
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing predicted energy usage values for new households based on their device configurations before actual usage data is available. This allows the service to be provided immediately to new households with predicted values, which are later refined as actual usage data becomes available, thus maintaining user convenience while improving prediction accuracy over time.
Solution Approach 2:
The system creates copies of energy usage patterns from similar households (those with comparable device configurations) to generate predicted values for new households. By copying and adapting data from households with similar characteristics, the system can provide prediction services to new households immediately, improving user convenience while the copied data serves as a preliminary approximation that can be refined.
2Loss of energy
If energy reduction commands are provided based on predicted values, then energy consumption is reduced, but the system cannot operate without past energy usage information
Solution Approach 1:
The system performs preliminary energy reduction planning by calculating predicted energy usage and identifying potential reduction opportunities before actual energy consumption occurs. This allows the system to proactively provide energy reduction commands to new households based on their device configurations and predicted usage patterns, enabling energy conservation without requiring historical data.
Solution Approach 2:
The system introduces device configuration information as an intermediary element that bridges the gap between lack of historical energy data and need for energy reduction commands. By using device specifications, power ratings, and operational patterns as intermediate data, the system can generate meaningful energy reduction recommendations for new households without directly relying on past energy usage information.
3Measurement precision
If home device information is updated to reflect actual devices, then prediction accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system segments the data processing task by handling device information updates independently from energy calculation processes. Device configuration data is processed and stored separately, then referenced during energy prediction without requiring complex real-time processing. This segmentation allows accurate predictions based on updated device information while maintaining manageable system complexity through modular data handling.
Data Source
AI summary
An electronic apparatus is disclosed. The electronic apparatus includes a memory stored with home device information corresponding respectively to a plurality of households and electrical power usage information corresponding respectively to the plurality of households, and a processor configured to control the electronic apparatus by being connected to the memory, and the processor is configured to obtain, based on a service request being received from a new household, home device information corresponding to the new household, update home device information corresponding to the new household based on at least one device included in the obtained home device information, identify at least one household from among the plurality of households based on the updated home device information, and obtain predicted electrical power usage information of the new household based on electrical power usage information corresponding to the identified at least one household.