Control method and system for air source heat pump variable return difference water temperature based on supply and demand matching
A water temperature control method and air source heat pump technology, which are applied in heating systems, heating methods, household heating, etc., can solve the problem of frequent start and stop, the improvement effect is limited, the setting value is reduced, etc., and the problem of frequent start and stop is improved. , reduce the impact of noise and improve system performance
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Embodiment 1
[0038] The present embodiment discloses a method and system for controlling the return difference water temperature of an air source heat pump based on matching supply and demand, including the following steps:
[0039] S1 According to the thermal design and air tightness of the building, the building model is established based on the finite difference method;
[0040] S2 establishes the unit model based on the BP neural network algorithm with state identification parameters according to the measured data of the air source heat pump operation minute by minute;
[0041] S3 establishes the terminal model considering the dynamic thermal process and the transmission and distribution model considering the inertia of the waterway;
[0042] S4 forms the dynamic model of the air source heat pump system based on the dynamic coupling relationship between the unit heating capacity, the terminal heat supply and the building heat dissipation;
[0043] Based on the established dynamic mode...
Embodiment 2
[0047] This embodiment discloses a unit model, including unit power solving networks BP-P1 and BP-P2 in the startup section and stable section, and BP-Q1 and BP-Q2 in the startup section and stable section for solving the unit heating capacity.
[0048] In the unit model of this embodiment, the state identification parameter i is introduced into the source data obtained by the test P , i Q , corresponding to the gradual change of unit power and heating capacity during the start-up phase, where: i P = 1, 2, 3, corresponding to the 1st minute, 2nd minute, and the following stable operation segments just after starting up, that is, the power reaches stability after 2 minutes of starting up; i Q = 1, 2, 3, 4, corresponding to the 1st, 2nd, 3rd minutes of just starting the machine, and then the stable operation segment, that is, the heating value reaches stability after 3 minutes of starting the machine, and then 4 BP neural networks are trained. .
[0049] The BP neural network...
Embodiment 3
[0052] In this embodiment, based on the established dynamic model, the outdoor meteorological parameters are determined according to the project location, and the indoor temperature T is determined according to the indoor thermal comfort requirements. in Allowable fluctuation range, under different outdoor temperature levels, by trying to calculate the indoor temperature under different water temperatures, determine the water temperature range that can keep the indoor temperature within the allowable range, so as to establish the variable return difference water temperature control strategy of the target system.
[0053] Further, in the method, the water temperature range can be disassembled into the water temperature setting value T commonly used in the control in combination with the actual operation mode and the built-in setting requirements of the unit. w,set Combination of upper and lower hysteresis mode, the target water temperature range can be expressed Format.
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