Method for optimizing operation parameters of central air conditioners based on neutral network and genetic algorithm

Abstract

The invention relates to energy saving and efficiency improving of operation of central air conditioners and aims to provide a method for optimizing operation parameters of the central air conditioners based on a neutral network and a genetic algorithm. The method for optimizing the operation parameters of the central air conditioners based on the neutral network and the genetic algorithm comprises the steps that data are obtained; the BP neutral network is modeled; and the neutral network is called by the genetic algorithm for optimizing the operation parameters of the central air conditioners, and finally, control variables are output and set as the operation parameters of the central air conditioners. According to the method for optimizing the operation parameters of the central air conditioners based on the neutral network and the genetic algorithm, under the premise of meeting the operation technological requirements of a central air conditioner system, the five operation workingparameters of the air conditioner system under the situation of the maximum comprehensive energy efficiency can be obtained through the load rate of the system and the dry-bulb temperature and the relative humidity of the environment during operation of the given air conditioner system, and the actual working condition is met, wherein the five operation working parameters include the water outlettemperature teo of chilled water, the water return temperature tei of the chilled water, the water supply and return pressure difference delta P of the chilled water, the water outlet temperature tciof cooling water and the water return temperature tco of the cooling water.

Description

Technical field [0001] The invention relates to the field of energy-saving and efficiency-enhancing operation of central air-conditioning, and particularly relates to a method for optimizing central air-conditioning operating parameters based on neural networks and genetic algorithms. Background technique [0002] The energy consumption of the central air-conditioning system accounts for about 40% to 60% of the overall energy consumption of public buildings. Therefore, energy saving of the air-conditioning system is the key to building energy saving. Through the control of the energy consumption of the central air-conditioning system, the goal of building energy saving can be clearly achieved. [0003] However, due to the main controlled parameters (temperature, flow, pressure difference) of the central air-conditioning operating system, affected by various factors such as seasonal changes, use time, environmental changes, and changes in the flow of people, there are serious factors...

AI Technical Summary

Problems solved by technology

[0003] However, since the main controlled parameters (temperature, flow, and pressure difference) of the central air-conditioning operation system are affected by various factors such as seasonal changes, use time, environmental changes, and human flow changes, there are serious discrepancies among the process elements. Non-linear, large lag and strong coupling relationship, so this is a stochastic, time-varying, nonlinear, multivariable, complex system

Generally difficult to describe with precise mathematical models or methods

For such a system, traditional control based on precise mathematical models, whether it is classic PID control or various algorithms of modern control theory, is difficult to achieve better energy-saving control effects

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