Dynamic predictive machine learning type air conditioner energy-saving control method

Abstract

The invention discloses a dynamic predictive machine learning type air conditioner energy-saving control method. By means of the dynamic predictive machine learning type air conditioner energy-savingcontrol method, the prediction accuracy and parameter optimization performance are improved by obtaining multiple characteristic variables such as environmental parameters, user habits and holiday laws and optimizing a support vector machine central air conditioner load prediction model through a dual-population particle swarm training set; and a global parameter optimization model is constructedby taking the minimization of operation energy consumption of an air conditioning system as a target. The dynamic predictive machine learning type air conditioner energy-saving control method has thebeneficial effects that the optimization of the comprehensive energy consumption of a central air conditioner system is met, and dynamic predictive intelligent management and stable operation can be realized.

Description

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AI Technical Summary

Benefits of technology

This technology helps optimize the supportingvector machines' models by utilizing large amounts of historical data from various sources such as users behavior or environmental factors like weather conditions for better predictions. By doing this it reduces power usage while ensuring that the systems operate efficiently over different periods of time. Additionally, an algorithm called Random Forced Swarm (RFS) learns how best to adjust certain aspects of these models during specific hours depending upon their preferences. Overall, this innovation enhances the efficiency and effectiveness of air conditioners throughout any given period of time without requiring constant maintenance.

Problems solved by technology

The patent text discusses the issue of energy consumption in buildings, particularly related to central air conditioning systems. The text highlights that the current energy-saving strategies for these systems are limited and mainly focus on adjusting the temperature difference and flow of chilled water pumps. However, these strategies do not consider the overall energy consumption optimization of the system, including other equipment like cooling water pumps, refrigeration units, and cooling towers. Additionally, the existing automation control methods, such as PID control, are not suitable for the complex and dynamic nature of central air conditioning systems. The text emphasizes the need for a new energy-saving control method that can optimize energy consumption, provide dynamic predictive management, and ensure stable operation of central air conditioning systems.

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