Method and device for forecasting air-conditioning load and air-conditioner
An air-conditioning load and prediction method technology, applied in the field of air-conditioning, can solve the problems of not considering the dynamic changes of air-conditioning load, the inability of air-conditioning system equipment to operate, and the inconsistency of cooling capacity and air-conditioning load changes, so as to avoid energy waste.
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[0031] Example one
[0032] figure 1 Shows a flow chart of the implementation of the air conditioning load forecasting method provided by Embodiment 1 of the present invention, which is detailed as follows:
[0033] In S101, initialize the weights and thresholds corresponding to the air conditioning load forecasting neural network model;
[0034] In this embodiment, the initialized weight and threshold can be arbitrary values.
[0035] In S102, obtain the parameter quantity that affects the air conditioning system load;
[0036] In this embodiment, the parameter quantity may include: indoor temperature, outdoor temperature, indoor humidity, outdoor humidity, air conditioning chilled water system supply and return water temperature and flow rate, etc. The parameters can be adjusted according to actual conditions.
[0037] In S103, input the parameters into a pre-trained air conditioning load prediction neural network model, where the air conditioning load prediction neural network model ...
Example Embodiment
[0062] Example two
[0063] Figure 4 Shows the structure diagram of the air conditioning load forecasting system provided in the second embodiment of the present invention. For ease of description, only the parts related to the embodiment of the present invention are shown. The system may be a software unit, a hardware unit, or a built-in air conditioner. Soft and hard unit.
[0064] The system includes: a parameter amount obtaining unit 41, a parameter amount input unit 42 and a predicted value obtaining unit 43.
[0065] The parameter quantity obtaining unit 41 is used to obtain the parameter quantity that affects the load of the air conditioning system;
[0066] The parameter input unit 42 is configured to input the parameter to a pre-trained air conditioning load prediction neural network model, where the air conditioning load prediction neural network model includes: an input layer, an intermediate layer, a feedback layer, and an output layer;
[0067] The predicted value obtai...
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