An air source heat pump multi-objective optimization design method integrating a BP neural network and a multi-parent genetic algorithm
A BP neural network, multi-objective optimization technology, applied in biological neural network models, computer-aided design, neural architecture and other directions, can solve the problems of long time, low precision, single optimization target, etc., to reduce the time and cost of transformation, The effect of low heat pump cost and high system efficiency
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[0034] The present invention will be further described below in conjunction with the accompanying drawings.
[0035] refer to figure 1 and figure 2 , a multi-objective optimization design method for air source heat pumps that combines BP neural network and multi-parent genetic algorithm, including the following steps:
[0036] Step 1. Parameter selection and data processing according to design requirements
[0037] Select the heat transfer temperature difference T of the air cooler gc , Evaporator heat transfer temperature difference T ev , air cooler pressure p gc As input variables, the system COP and cost are used as output variables, and the input and output variables are normalized so that they are between [0,1]. The training sample data can come from literature or measured in experiments. Normalization The unification formula is as follows:
[0038]
[0039] Where k is the normalized value, x is the normalized data, and x min 、x max are the minimum and maximu...
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