Demand control device and program
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Solution Overview
Problem
Existing air-conditioning control methods using intermittent shut-off operations struggle to balance power reduction with maintaining a comfortable indoor environment, especially when external and internal conditions change constantly, leading to potential comfort deterioration.
Innovation Solution
A demand control device that groups indoor units, estimates reducible power based on low power operations, predicts power consumption, and calculates targeted power reduction distribution across groups to maintain a comfortable environment while avoiding exceeding the target demand power, using power consumption measurement and correlation models to optimize shut-off times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If intermittent shut-off operation is performed to reduce power consumption, then power consumption amount is reduced, but indoor comfort may deteriorate
Solution Approach 1:
The patent divides indoor units into multiple groups and performs intermittent shut-off operations on a per-group basis rather than shutting off all units simultaneously. This segmentation allows power consumption to be reduced while ensuring that at least some groups remain operational to maintain indoor comfort standards.
Solution Approach 2:
The system dynamically adjusts the shut-off timing and duration for each group based on real-time power consumption measurements and predictions. The control strategy adapts to changing conditions by modifying which groups are shut off and for how long, optimizing the balance between power reduction and comfort maintenance.
2Device complexity
If constant values are used in power consumption calculation expressions, then calculation is simplified, but accuracy decreases when external conditions change
Solution Approach 1:
The patent incorporates feedback mechanisms where actual power consumption measurements from previous operations are used to update and refine the calculation expressions. The system learns from historical data and adjusts the correlation models to reflect current external conditions, improving prediction accuracy without requiring overly complex real-time calculations.
Solution Approach 2:
The system changes the parameters used in calculations based on external conditions. Instead of using fixed constant values, the calculation expressions incorporate variable parameters such as outdoor temperature, humidity, and historical power consumption data, allowing the model to adapt to changing environmental conditions while maintaining computational efficiency.
3Device complexity
If N hours before demand time limit data is used for prediction, then calculation is simplified, but prediction accuracy decreases when conditions change constantly
Solution Approach 1:
The patent performs periodic power consumption measurements during the demand time limit period and uses these periodic data points to update predictions in real-time. Rather than relying solely on historical data from N hours before, the system continuously monitors and adjusts predictions based on the most recent operational patterns, improving accuracy for constantly changing conditions.
Solution Approach 2:
The system performs preliminary power consumption measurements and establishes baseline correlation models before the demand time limit begins. These preliminary actions create a foundation that is then refined with real-time data during the actual demand period, combining advance preparation with adaptive real-time adjustment.
Data Source
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AI summary
There is provided air-conditioning control in a low power operation so as not to exceed a target demand power amount in a predetermined measurement period while preventing deterioration of comfortableness of a living space. There include a reducible-power-amount estimation unit (361) configured to calculate, for each group, a reducible power amount by making indoor units (7) perform, for each group, a shut-off operation for a minimum shut-off time in the first half of a demand time limit, and a reduced-power-amount determination unit (362) configured to distribute, when a power consumption amount predicted by a power consumption amount prediction unit (34) exceeds a target demand power amount after the first half of the demand time limit passes, the exceeding power amount to each zone, evenly distribute, in each zone, the distributed power amount to each group, and calculate, based on the reducible power amount of each group, a shut-off time of each group when each group performs the shut-off operation to reduce the distributed power amount.