Target electric power calculation device, target electric power calculation method, and target electric power calculation program
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Solution Overview
Problem
Conventional demand control systems are inadequate in deriving proper target power, leading to inefficient power management and control.
Innovation Solution
A demand control system comprising a target power calculation apparatus that uses machine learning to derive and adjust target power based on historical data and comfort indexes, ensuring accurate power allocation and management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional check technology is used to verify target power adequacy, then power management control can be performed, but the system cannot derive proper target power when inadequacy is found
Solution Approach 1:
The system implements a feedback mechanism where the adequacy determination unit evaluates whether the target power is adequate based on learned patterns, and this evaluation result feeds back to the target power derivation unit to adjust and improve future target power derivations. This closed-loop feedback enables continuous improvement of target power accuracy while maintaining verification capability.
Solution Approach 2:
The system employs machine learning to enable self-improvement of target power derivation. The adequacy determination unit learns from historical data and verification results, automatically refining its ability to derive proper target power without external intervention. This self-learning mechanism resolves the contradiction by making the system progressively more capable in both verification and derivation functions.
2Measurement precision
If machine learning is implemented to derive target power, then power allocation accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex machine learning functionality into distinct modular units: a target power derivation unit that generates initial target power values, and a separate adequacy determination unit that verifies and learns from these values. This segmentation allows the complex ML operations to be distributed across specialized modules, improving accuracy while managing system complexity through functional decomposition.
Data Source
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AI summary
A target power calculation apparatus, a target power calculation method, and a target power calculation program for deriving target power that is to be set when performing a demand control process are provided. A target power calculation apparatus includes a learning unit configured to learn whether a target power set in a first period is adequate, according to control information obtained upon performing a demand control process in the first period based on the set target power, an inference unit configured to infer whether a target power set in a second period is adequate, according to control information obtained upon performing a demand control process in the second period based on a result of learning by the learning unit, and a correction unit configured to correct the target power set in the second period based on a predetermined correction amount upon the inference unit inferring that there is inadequacy.