Operation Schedule Optimizer with Dynamic Approval Thresholds
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Solution Overview
Problem
In smart communities, optimizing the operation schedule of large-scale buildings to manage energy usage effectively is challenging due to unpredictable power usage changes caused by events like breakdowns or fluctuating demand, making it difficult to set thresholds for schedule adjustments and reducing the burden on facility operators.
Innovation Solution
An operation schedule optimizing device that predicts energy consumption or supply, optimizes schedules based on evaluation barometers, determines the need for operator approval, and transmits determination results, allowing for real-time adjustments without requiring frequent operator intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the operation schedule is automatically adjusted based on predicted energy consumption, then energy management efficiency is improved, but the accuracy of power usage prediction deteriorates due to unexpected events
Solution Approach 1:
The system compares actual power usage values with predicted values, and when the absolute difference exceeds a threshold, it recalculates the operation schedule. This feedback mechanism allows the system to adapt to unexpected events while maintaining automatic optimization, resolving the contradiction between automation efficiency and prediction accuracy.
2Adaptability or versatility
If the threshold for schedule adjustment is set low, then response to changing conditions is improved, but the frequency of operator approval requests increases
Solution Approach 1:
The system dynamically adjusts the threshold value based on the difference between actual and predicted power usage. When the difference is small, a lower threshold enables responsive adjustments; when the difference is large, the threshold prevents excessive approval requests. This parameter adaptation resolves the contradiction between adaptability and operational ease.
3Manufacturing precision
If the operation schedule is frequently reviewed and adjusted, then optimization accuracy is improved, but the operational burden on facility operators increases
Solution Approach 1:
The system performs self-adjustment by automatically recalculating operation schedules when unexpected events are detected through the feedback mechanism. This reduces the need for operator intervention while maintaining optimization accuracy, resolving the contradiction between precision and operator time loss.
4Extent of automation
If automated control is implemented without approval requests, then operational efficiency is improved, but the ability to handle unexpected events deteriorates
Solution Approach 1:
The system maintains high automation by automatically detecting unexpected events through feedback comparison and triggering recalculation only when necessary. This conditional automation approach ensures reliable handling of unexpected events while preserving operational efficiency through automated control.
Data Source
AI summary
An optimized operation schedule of a control-target apparatus is ensured while maximally reducing a burden share to a facility operator. An energy predictor sets, for a control-target apparatus, a predicted value of energy consumption or energy supply within a predetermined future time period based on process data. A schedule optimizer optimizes an operation schedule of the control-target apparatus within the predetermined time period with a predetermined evaluation barometer based on the predicted value, the characteristic of the control-target apparatus, and the process data. An approval request determiner determines a necessity of an approval for a latest operation schedule based on a preset determining condition. A determination result transmitter transmits a determination result by the approval request determiner.