Electricity Consumption Recommendation System Using Eigenvalue Analysis
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Solution Overview
Problem
Conventional energy management technologies at the power-consuming side fail to provide users with explicit ways to reduce electricity consumption, leading to inefficient energy saving efforts that may compromise comfort and convenience.
Innovation Solution
An apparatus and method that calculates a power saving matrix and a changing willingness matrix using appliance efficiency values and electricity-consuming parameter values, then recommends specific electricity consumption behaviors based on eigenvalues and eigenvectors of a transform probability matrix.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional energy management technologies rank users' electricity consumptions or hold power saving competitions, then users can be motivated to reduce electricity consumption, but users are not provided with explicit ways to achieve power saving and may save power blindly compromising comfort and convenience
Solution Approach 1:
The system provides feedback to users about their electricity consumption patterns by analyzing appliance efficiency values and electricity-consuming parameter values. The feedback includes specific recommendations on which appliances to adjust and how much to save, enabling informed power saving decisions rather than blind reduction.
Solution Approach 2:
The server acts as an intermediary between the power supply network and users, processing appliance efficiency data and consumption parameters to generate actionable recommendations. This intermediary provides the missing information bridge that connects raw consumption data to practical power saving guidance.
2Loss of energy
If users reduce electricity consumption without specific guidance, then energy saving can be achieved, but users have no idea whether high electricity consuming amount results from low appliance efficiency or improper operations
Solution Approach 1:
The system segments the electricity consumption problem into specific appliances and their individual efficiency values. By analyzing each appliance separately with its own efficiency metric, the system identifies whether high consumption is due to low appliance efficiency or improper operation, providing targeted guidance for each segment.
Solution Approach 2:
The system changes the parameter representation from aggregate electricity consumption to disaggregated appliance-level efficiency values and consumption parameters. This parameter transformation enables the system to distinguish between efficiency-related consumption and operation-related consumption, providing actionable insights.
3Loss of energy
If blind power saving is encouraged, then electricity consumption can be reduced, but loss of due comfort and convenience will decrease the willingness of users for saving energy
Solution Approach 1:
The system recommends partial adjustments to appliance parameters rather than complete shutdowns or extreme changes. By suggesting moderate, targeted adjustments to specific appliances, the system achieves power saving while maintaining adequate comfort and convenience levels, preventing excessive action that would user dissatisfaction.
Data Source
AI summary
An apparatus, a method, and a non-transitory computer readable storage medium thereof for recommending an electricity consumption behavior are provided. The apparatus is stored with an appliance efficiency value and an electricity-consuming parameter value for each of a plurality of users. The apparatus generates a plurality of first temporary values by multiplying each of the appliance efficiency values with each of the electricity-consuming parameters, generates a power saving matrix by subtracting each of the electricity-consuming parameters from each of the electricity-consuming parameters individually, generates a changing willingness matrix by the second temporary values, calculates a transform probability matrix by the power saving matrix and the changing willingness matrix, calculates an eigenvalue and an eigenvector of the transform probability matrix, and recommends an electricity consumption behavior according to the eigenvalue and the eigenvector.