DC varaiable speed compressor control method and control system
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Solution Overview
Problem
Current DC variable speed air conditioning systems do not effectively estimate and manage energy consumption based on temperature curve profiles, leading to inefficient energy use, particularly during peak consumption periods.
Innovation Solution
A control system comprising a speed control calculation unit, data storage unit, information acquisition unit, network communication module, and speed control output unit, which calculates and adjusts compressor speed to match energy output modes with peak temperature timing, using sensor data and weather forecasts to optimize energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If DC variable speed AC adjusts compressor speed freely to lower on/off cycles, then comfort and energy saving are improved, but the system does not factor in energy consumption when choosing speed control strategies
Solution Approach 1:
The system performs preliminary actions by obtaining weather forecast data and calculating future peak temperature timing before executing the speed control strategy. The control system pre-determines the optimal speed profile that will align low-speed periods with predicted peak temperature periods, rather than simply reacting to current temperature conditions. This allows the system to proactively minimize energy consumption during high-temperature periods while maintaining comfort.
Solution Approach 2:
The system dynamically adjusts compressor speed based on predicted future temperature conditions rather than static on/off control or simple proportional control. The speed control strategy is continuously optimized by comparing actual temperature deviations with forecasted conditions, allowing the system to adapt its operation in real-time to minimize energy consumption while maintaining comfort throughout the cooling period.
2Duration of action of stationary object
If the system uses basic matching principle to match current output to current load, then the number of on/off cycles is reduced, but energy consumption during peak periods is not optimized
Solution Approach 1:
The system obtains weather forecast data in advance and calculates the timing of future peak temperature periods before the compressor starts operating. This preliminary information is used to pre-determine the optimal speed control strategy that will align low-speed operation with predicted peak demand periods, rather than simply matching current load conditions. The system proactively plans the speed profile to minimize energy consumption during high-temperature periods.
Solution Approach 2:
The system changes the control parameter from simple on/off cycling or proportional speed matching to a forecast-based speed profile that explicitly targets energy consumption minimization. By incorporating weather forecast temperature predictions into the control logic, the system transforms the speed control parameter to account for future thermal conditions, allowing optimization of energy usage during peak periods while maintaining adequate cooling capacity.
3Use of energy by moving object
If the system wants to estimate energy consumption based on temperature curve profiles, then optimal speed control strategy can be chosen, but the system requires self-learning characteristic and multiple factors like future temperature changes and building heat gain coefficient
Solution Approach 1:
The system performs preliminary calculations by obtaining weather forecast data and computing the timing of peak temperature periods before compressor operation begins. This advance preparation allows the control system to pre-determine the optimal speed profile without requiring complex real-time self-learning during operation. The forecast-based approach simplifies the control logic by using predicted temperature curves rather than requiring the system to learn and adapt to actual temperature variations during runtime.
Solution Approach 2:
The system introduces weather forecast data as an intermediary element that bridges the gap between simple control and complex self-learning requirements. Instead of requiring the system to directly learn and model building heat gain coefficients and temperature curves in real-time, the forecast data serves as a pre-processed intermediary that provides the necessary thermal condition information in advance, simplifying the control algorithm while still enabling energy consumption optimization.
Data Source
AI summary
The present disclosure relates to the field of air conditioning technology. In particular, it involves a control method and control device based on a DC variable speed AC compressor.