A Central Air Conditioning Energy Consumption Control Method Based on Improved Particle Swarm Algorithm

Abstract

The invention relates to a central air-conditioning energy consumption control method based on an improved particle swarm algorithm, comprising the following steps: obtaining the COP curves of different units of the central air-conditioning, and fitting the energy consumption functions of each unit; Punishment method to construct the energy consumption optimization model of the central air conditioner; use the improved particle swarm algorithm to solve the energy consumption optimization model of the central air conditioner to obtain the optimal load distribution rate of each unit; control the load of each unit according to the optimal load distribution rate of each unit Switching and switching to complete the optimal control of central air-conditioning energy consumption. Compared with the prior art, the present invention improves the particle swarm algorithm, uses the sinusoidal chaotic sequence to increase the ergodicity of the particle swarm algorithm in the early stage, and adds the sinusoidal chaotic disturbance so that the particle group has a mechanism of escaping from the local optimum, and self-adapts during the optimization process. Adjusting the particle inertia weight can effectively improve the optimization speed and optimization accuracy, so as to adapt to the complex operating conditions of the central air conditioner and ensure the minimum energy consumption of the air conditioner.

Description

technical field [0001] The invention relates to the technical field of central air-conditioning energy saving, in particular to a central air-conditioning energy consumption control method based on an improved particle swarm algorithm. Background technique [0002] Central air-conditioning can meet people's demand for indoor air quality in the new era, but when using central air-conditioning, it will also generate a lot of energy consumption, which does not meet the requirements of energy conservation and environmental protection. Air-conditioning equipment is manufactured and designed to cope with various extreme conditions, and it is designed according to the full load state. However, in a normal environment, the air conditioner operates at partial load for 90% of the time, and its load is only 50% of the time. half of the design load. Therefore, adjusting the settings of the air conditioner according to the needs can greatly reduce energy consumption. [0003] With the ...

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Problems solved by technology

[0004] The PSO algorithm has the advantages of simple algorithm structure, less dependence on key parameters, strong robustness, and easy engineering implementation, so it is widely used in the field of industrial optimization production, and is also used in the optimal control of air conditioning systems, but the PSO algorithm is prone to population diversity Due to the low stability, the algorithm falls into the local optimal solution, and its optimization speed and accuracy cannot adapt to complex working conditions. Since most central air-conditioning systems contain multiple units, the load distribution of the units is not uniform, and the operating conditions are more complicated. PSO Algorithms cannot quickly and accurately optimize unit load distribution, and cannot guarantee effective energy saving

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