Load-predicting and control system and method for subway heating, ventilation and air conditioning system
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Solution Overview
Problem
Conventional load prediction and control systems for subway heating, ventilation, and air conditioning systems suffer from poor accuracy and inadequate control due to the complex and time-varying nature of load factors such as external temperature, humidity, and human activity, leading to inefficient energy usage.
Innovation Solution
A load-predicting and control system that integrates historical and real-time data using an exponential smoothing method, including sensors for person count, departure information, and environmental conditions, to calculate predicted load values and issue control commands for the HVAC system, thereby improving prediction accuracy and control efficacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional PID negative feedback control is used for subway HVAC system, then the control system is simple to implement, but the control result oscillates and energy-saving effects are unsatisfactory
Solution Approach 1:
The patent implements feedforward control by predicting the cooling load in advance using exponential smoothing algorithm. The predicted load values are calculated before the actual load occurs, allowing the HVAC system to adjust its operation proactively rather than reactively, thereby avoiding oscillations and reducing energy consumption.
Solution Approach 2:
The patent combines feedforward control with feedback mechanisms by continuously comparing predicted load values with actual load measurements. The prediction accuracy is continuously improved by adjusting the smoothing parameters based on the difference between predicted and actual values, creating a closed-loop control system that maintains both simplicity and energy efficiency.
2Device complexity
If linear regression algorithm is used for load prediction, then the control system is simple, but the prediction accuracy is poor
Solution Approach 1:
The patent transforms the load prediction problem by changing the parameters being smoothed - instead of directly smoothing the load values, it smooths the differences between consecutive load values. This parameter transformation allows the exponential smoothing algorithm to capture the time-varying characteristics of subway HVAC load more accurately while maintaining computational simplicity.
3Measurement precision
If neural network algorithm is used for load prediction, then the prediction accuracy may be improved, but the algorithm has many limitations in engineering applications
Solution Approach 1:
The patent replaces complex neural network models with a simpler exponential smoothing algorithm that requires minimal computational resources and is easy to implement in engineering practice. The simplified model achieves satisfactory prediction accuracy for subway HVAC systems without the training, validation, and maintenance complexities of neural networks, making it suitable for real-time control applications.
4Measurement precision
If comprehensive historical data is stored for load prediction, then the prediction accuracy may be improved, but the data storage requirement increases
Solution Approach 1:
The patent extracts only the essential features from historical data - specifically the recent load values and their differences - rather than storing and processing all available historical data. The exponential smoothing algorithm effectively uses this extracted information to generate accurate predictions while minimizing data storage requirements.
Data Source
AI summary
Disclosed is a load-predicting and control system for a subway heating, ventilation and air conditioning system. In one aspect, a load-predicting and control system for a subway heating, ventilation and air conditioning system is provided. The system includes a basic database, a sensing system, a load predicting unit, and a controller; the basic database stores historical data; the sensing system provides measured data; the load predicting unit calculates a predicted load value of the subway heating, ventilation and air conditioning based on the historical data and the measured data, and transmits the predicted load value to the controller; the controller issues a control command based on the predicted load value. Also provided is a load-predicting and control method for the subway heating, ventilation and air conditioning system. The present disclosure solves problems such as poor accuracy of conventional load prediction and inadequate control of the air conditioning system.