Central air-conditioning water chilling unit load optimization method and system, medium and equipment

Abstract

The invention discloses a central air-conditioning water chilling unit load optimization method and system, a medium and equipment, and the method comprises: initializing a central air-conditioning water chilling unit load system, randomly generating an initial group, and setting the parameters of the central air-conditioning water chilling unit load system; calculating the fitness value F of each individual according to the set water chilling unit load optimization system parameters; selecting a sequence of genetic operations according to the fitness value F of each individual; according to the determined genetic operation sequence, judging whether the genetic operation result has searched an optimal value or has iterated to a set maximum iteration frequency; and if the convergence condition is met, outputting the minimum system energy consumption value, the average system energy consumption and the convergence speed so as to achieve optimization of the central air conditioner water chilling unit load system. According to the invention, the convergence speed is improved while the quality of the solution is ensured, and the system energy consumption is effectively reduced through reasonable load distribution.

Description

technical field [0001] The invention belongs to the technical field of central air-conditioning, and in particular relates to a load optimization method, system, medium and equipment of a central air-conditioning chiller. Background technique [0002] In the energy consumption of large public buildings in my country, the energy consumption of the air-conditioning system accounts for about 40% of the total energy consumption of the building. However, the energy consumption of the air-conditioning system mainly comes from the energy consumption of the chiller. Inferiority will directly determine the level of unit energy consumption. Therefore, how to reduce the energy consumption of the chiller system under different cooling load conditions, and then how to control and optimize it reasonably is an urgent problem to be solved. In addition, since chiller group control systems are usually composed of chillers with different performances and capacities, it is of great significance...

AI Technical Summary

Problems solved by technology

[0003] At present, a variety of chiller load optimization models and optimization algorithms have been proposed in this field to promote the research on the Optimal Chiller Loading (OCL) problem. Circumstances that reasonably result in a large waste of energy

[0004] Aiming at this problem, considering the effectiveness of the Differential Evolution (DE) algorithm in solving OCL problems, the algorithm can not only find the optimal solution of the problem, but also obtain a better average solution, and the algorithm can solve the optimal It is superior to the basic GA algorithm in solving problems, and solves the divergence problem caused by the Lagrangian method under low demand, but there are also problems such as slow convergence speed in the late iteration and easy to fall into local optimum.

In addition, considering that the Adaptive Genetic Algorithm (AGA) has improved the basic genetic algorithm, the crossover probability and mutation probability are empirical values ​​and are fixed, which is prone to poor performance of the algorithm, resulting in the selected The problem of inaccurate optimal parameters, it adjusts the probability of crossover and mutation adaptively according to the individual fitness value, so that the optimal individual of each generation will not be in a state of no change, but the biggest deficiency in practical applications It is prone to premature convergence, that is, immature convergence. This phenomenon is unique to genetic algorithms, and it has strong randomness. It is almost impossible to predict whether it will produce

Images