Energy efficiency control method and system based on energy-saving air conditioner

Abstract

The invention relates to the technical field of air conditioner energy efficiency control, and discloses an energy efficiency control method and system based on an energy-saving air conditioner, the energy efficiency control method based on the energy-saving air conditioner comprises the following steps: step S101, when an intelligent energy efficiency management mode is set, entering step S102, and when a forced energy efficiency management mode is set, entering step S105; step S102, preprocessing to generate an environment sequence; s103, obtaining a target temperature through the target temperature prediction model; s104, the energy efficiency ratio is obtained through calculation, and an intelligent energy efficiency management mode is achieved in combination with the target temperature; s105, the energy efficiency ratio is obtained through calculation, and forced energy efficiency management is achieved in combination with the indoor temperature and the target temperature, set by a user, of the air conditioning system; according to the intelligent energy efficiency management mode and the forced energy efficiency management mode, the compressor operation frequency and the fan operation rotating speed are automatically adjusted in real time according to the calculated energy efficiency ratio of the air conditioner system, so that the energy efficiency ratio of the air conditioner system is increased.

Description

Technical Field

[0001] The present invention relates to the technical field of air conditioner energy efficiency control, and more specifically, to an energy efficiency control method and system based on energy-saving air conditioner. Background Art

[0002] With the development of the global economy and the acceleration of urbanization, air-conditioning systems have become an indispensable part of the modern building environment. However, the high energy consumption of air-conditioning systems has become increasingly prominent. According to relevant statistics, the energy consumption of air-conditioning systems accounts for 30% to 60% of the total energy consumption of buildings. Especially in the hot summer and cold winter, the energy consumption of air-conditioning systems will fluctuate greatly due to large temperature changes and the increased frequency of air-conditioning use.

[0003] Existing air-conditioning control methods mainly rely on set operating modes, lack real-time monitoring and adaptive adjustment functions, and are difficult to achieve the best energy efficiency ratio (COP) under changing environmental conditions. In addition, existing air-conditioning energy-saving control methods still mainly rely on set energy-saving modes. In energy-saving modes, the speed of the compressor is limited. However, when the load is large or the load changes frequently, the lower speed of the compressor may not meet the user's comfort requirements, resulting in a decrease in user experience.

[0004] Therefore, there is an urgent need for an air conditioning energy-saving control method based on real-time monitoring to improve the operating efficiency of the air conditioning system. Summary of the invention

[0005] The present invention provides an energy efficiency control method and system based on energy-saving air conditioning, which solves the technical problems in the above-mentioned background technology.

[0006] The present invention provides an energy efficiency control method based on energy-saving air conditioning, comprising the following steps: Step S101: when the user turns on the energy efficiency management mode, and the energy efficiency management mode is set to the intelligent energy efficiency management mode, the process proceeds to step S102; when the energy efficiency management mode is set to the forced energy efficiency management mode, the process proceeds to step S105; Step S102, within a first time period T1, collecting environmental parameters of the location of the air conditioning system according to a first preset time interval t1, and performing preprocessing to generate an environmental sequence; Environmental parameters include: indoor temperature and humidity, indoor air quality, outdoor temperature and humidity, outdoor air quality, outdoor rainfall and outdoor light intensity; The environmental sequence includes N sequence units, and the nth sequence unit represents the environmental parameters at the nth time point after pretreatment, where 1≤n≤N, N=T1 / t; Step S103, inputting the environment sequence into the target temperature prediction model, and the output value represents the target temperature of the air conditioning system in the second time period T2; Step S104, collecting the operating parameters of the air-conditioning system, calculating the energy efficiency ratio of the air-conditioning system, and adjusting the compressor operating frequency and the fan operating speed of the air-conditioning system in combination with the target temperature of the air-conditioning system in the second time period T2; The operating parameters include: inlet water temperature, outlet water temperature, refrigerant flow rate and electrical power; The larger the value of the energy efficiency ratio, the higher the energy conversion efficiency and the less power consumption; Step S105, collecting the operating parameters of the air-conditioning system, calculating the energy efficiency ratio of the air-conditioning system, and adjusting the compressor operating frequency and fan operating speed of the air-conditioning system in combination with the indoor temperature and the target temperature of the air-conditioning system set by the user.

[0007] Furthermore, the first time period T1, the preset time interval t1 and the second time period T2 are all user-defined parameters.

[0008] Further, the environmental parameters are preprocessed to generate an environmental sequence, including the following steps: Step S201, for missing values ​​in environmental parameters, interpolation filling is performed by taking the average value of the environmental parameters at two adjacent time points of the missing value; Step S202, respectively calculating the change rate of the environmental parameter at each time point, and concatenating it with the environmental parameter at each time point to obtain a combined vector; The change rate of the environmental parameter at the first time point is assigned a value of 1; Step S203, normalizing the combined vector at each time point by a standard score method to obtain an environment sequence.

[0009] Further, the target temperature prediction model includes N hidden layers, each hidden layer including: a first unit and a second unit; The first unit of the nth hidden layer inputs the nth sequence unit of the environment sequence and outputs the update matrix; The second unit of the nth hidden layer inputs the update matrix output by the first unit of the nth hidden layer and outputs an update vector; The update vector output by the second unit of the Nth hidden layer is input to the first classifier, and the classification space of the first classifier represents the target temperature of the air-conditioning system in the second time period T2.

[0010] Furthermore, the calculation formula of the target temperature prediction model includes: The calculation formula for the first unit of the nth hidden layer is as follows: ; in represents the update matrix of the output of the first unit of the nth hidden layer, represents the nth sequence unit of the environment sequence input to the first unit of the nth hidden layer, and They represent the weight parameter and bias parameter of the first unit of the nth hidden layer, respectively. represents a convolution operation with a kernel size of 3×3 and a stride of 1. represents the maximum pooling operation with a pooling window size of 3×3 and a step size of 1, and sigmoid represents the sigmoid activation function; The calculation formula for the second unit of the nth hidden layer includes: ; ; ; in represents the update vector for the output of the second unit of the nth hidden layer, represents the intermediate vector of the second unit of the nth hidden layer, and denote the update matrices for the second unit inputs of the nth and n-1th hidden layers, respectively, It is represented by a matrix with the same size as the update matrix and all element values ​​are 0. express and The correlation coefficient between them, K represents the number of elements in the update matrix, spread represents expanding the matrix into a vector representation and sorting the elements in ascending order, and Rank represents the rank value calculation. and denote the first weight parameter and the second weight parameter of the second unit of the nth hidden layer, respectively. and denote the first bias parameter and the second bias parameter of the second unit of the nth hidden layer respectively, and Swish denotes the Swish activation function.

[0011] Furthermore, multiple independent experimental rooms are built to simulate the same environmental parameters, and sample data of training samples for training the target temperature prediction model are obtained according to steps S101 to S102. Then, the target temperature is manually set for each independent experimental room. The difference between the target temperatures of each independent experimental room cannot exceed 5°C. After the second time period T2, the target temperature with the largest average value of the comfort of multiple people in the experimental room is taken as the sample label of the training sample for training the target temperature prediction model.

[0012] Furthermore, the calculation formula of the energy efficiency ratio COP of the air conditioning system is as follows: ; Where Cp represents the specific heat capacity at constant pressure, which is assigned a value of 4.1868KJ / kg·℃, and R represents the specific gravity of the refrigerant, which is assigned a value of 1000Kg / m³. It indicates the refrigerant flow rate in m³ / h, Ts indicates the outlet water temperature in °C, Tw indicates the inlet water temperature in °C, and P indicates the electrical power in kW.

[0013] Furthermore, the intelligent energy efficiency management mode adjusts the compressor operating frequency and fan operating speed of the air conditioning system, including the following steps: Step S301, calculating the absolute value of the difference between the water temperature and the target temperature of the air conditioning system in the second time period T2 as the temperature difference value, and judging that the temperature difference value is less than or equal to 1°C, then proceeding to step S302; Step S302, in the third time period T3, the energy efficiency ratio is calculated according to the second preset time interval t2, and the average value of the energy efficiency ratios at all time points is calculated as a reference value of the energy efficiency ratio; The third time period T3 and the second preset time interval t2 are both custom parameters; Step S303, judging that the energy efficiency ratio at the current time point is greater than or equal to the energy efficiency ratio reference value, and the temperature difference value is less than or equal to 1°C, then reducing the compressor operating frequency by 1 Hz and running for 30 seconds, and entering step S304; Step S304, if it is determined that the energy efficiency ratio at the current time point is greater than or equal to the energy efficiency ratio reference value, and the temperature difference is less than or equal to 1°C, the fan speed is reduced by 1% and the fan is operated for 30 seconds, and the process proceeds to step S305; Step S305: If the energy efficiency ratio at the current time point is less than the energy efficiency ratio reference value or the temperature difference value is greater than 1°C, then stop adjusting and return to step S301.

[0014] Furthermore, the mandatory energy efficiency management mode adjusts the compressor operating frequency and fan operating speed of the air conditioning system, including the following steps: Step S401, determining that the indoor temperature is within a preset indoor temperature range and the outlet water temperature is within a preset outlet water temperature range, and determining an energy efficiency ratio reference value according to a mapping relationship between the indoor temperature and the outlet water temperature and a preset energy efficiency ratio; The preset indoor temperature range, preset water outlet temperature range, and the mapping relationship between the indoor temperature and water outlet temperature and the preset energy efficiency ratio are all custom settings; Step S402, calculating the absolute value of the difference between the water temperature and the target temperature of the air conditioning system set by the user as the temperature difference value, and judging that the temperature difference value is less than or equal to 2°C, then proceeding to step S403; Step S403, judging that the energy efficiency ratio at the current time point is greater than or equal to the energy efficiency ratio reference value, and the temperature difference value is less than or equal to 2°C, then reducing the compressor operating frequency by 1 Hz and running for 30 seconds, and entering step S404; Step S404, if it is determined that the energy efficiency ratio at the current time point is greater than or equal to the energy efficiency ratio reference value, and the temperature difference is less than or equal to 2°C, the fan speed is reduced by 1% and the fan is operated for 30 seconds, and the process proceeds to step S405; Step S405: If the energy efficiency ratio at the current time point is less than the energy efficiency ratio reference value or the temperature difference value is greater than 2°C, then stop adjusting and return to step S401.

[0015] The present invention provides an energy efficiency control system based on energy-saving air conditioning, comprising: An environment sequence generation module, which is used to collect the environment parameters of the location of the air-conditioning system within a first time period T1 according to a first preset time interval t1, and perform preprocessing to generate an environment sequence; a target temperature prediction module, which is used to input the environment sequence into the target temperature prediction model, and the output value represents the target temperature of the air conditioning system in the second time period T2; An intelligent energy efficiency management module, which is used to collect operating parameters of the air-conditioning system, calculate the energy efficiency ratio of the air-conditioning system, and adjust the compressor operating frequency and fan operating speed of the air-conditioning system in combination with the target temperature of the air-conditioning system in the second time period T2; The mandatory energy efficiency management module is used to collect the operating parameters of the air-conditioning system to calculate the energy efficiency ratio of the air-conditioning system, and adjust the compressor operating frequency and fan operating speed of the air-conditioning system in combination with the indoor temperature and the target temperature of the air-conditioning system set by the user.

[0016] The beneficial effects of the present invention are as follows: the present invention provides an intelligent energy efficiency management mode and a forced energy efficiency management mode to automatically adjust the compressor operating frequency and the fan operating speed in real time according to the calculated energy efficiency ratio of the air-conditioning system to improve the energy efficiency ratio of the air-conditioning system, and the intelligent energy efficiency management mode predicts the target temperature of the air-conditioning system through a target temperature prediction model to ensure the user's comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of an energy efficiency control method based on energy-saving air conditioning of the present invention; Figure 2 is a flow chart of the preprocessing generation environment sequence of the present invention; Figure 3 is a flow chart of the intelligent energy efficiency management mode of the present invention; Figure 4 is a flow chart of the mandatory energy efficiency management mode of the present invention; Figure 5 is a schematic diagram of an energy efficiency control system based on energy-saving air conditioning of the present invention; Figure 6 It is a schematic diagram of the mapping relationship between the indoor temperature and the water outlet temperature and the preset energy efficiency ratio of the present invention.

[0018] In the figure: an environmental sequence generation module 501, a target temperature prediction module 502, an intelligent energy efficiency management module 503, and a mandatory energy efficiency management module 504. DETAILED DESCRIPTION

[0019] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that the discussion of these embodiments is only to enable those skilled in the art to better understand and implement the subject matter described herein, and the functions and arrangements of the elements discussed may be changed without departing from the scope of protection of the contents of this specification. Each example may omit, replace or add various processes or components as needed. In addition, the features described relative to some examples may also be combined in other examples.

[0020] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in one or more embodiments of the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in one or more embodiments of the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0021] like Figure 1 to Figure 6 As shown, an energy efficiency control method based on energy-saving air conditioning includes the following steps: Step S101: when the user turns on the energy efficiency management mode, and the energy efficiency management mode is set to the intelligent energy efficiency management mode, the process proceeds to step S102; when the energy efficiency management mode is set to the forced energy efficiency management mode, the process proceeds to step S105; Step S102, within a first time period T1, collecting environmental parameters of the location of the air conditioning system according to a first preset time interval t1, and performing preprocessing to generate an environmental sequence; Environmental parameters include: indoor temperature and humidity, indoor air quality, outdoor temperature and humidity, outdoor air quality, outdoor rainfall and outdoor light intensity; The environmental sequence includes N sequence units, and the nth sequence unit represents the environmental parameters at the nth time point after pretreatment, where 1≤n≤N, N=T1 / t; Step S103, inputting the environment sequence into the target temperature prediction model, and the output value represents the target temperature of the air conditioning system in the second time period T2; Step S104, collecting the operating parameters of the air-conditioning system, calculating the energy efficiency ratio of the air-conditioning system, and adjusting the compressor operating frequency and the fan operating speed of the air-conditioning system in combination with the target temperature of the air-conditioning system in the second time period T2; The operating parameters include: inlet water temperature, outlet water temperature, refrigerant flow rate and electrical power; The larger the value of the energy efficiency ratio, the higher the energy conversion efficiency and the less power consumption; Step S105, collecting the operating parameters of the air-conditioning system, calculating the energy efficiency ratio of the air-conditioning system, and adjusting the compressor operating frequency and fan operating speed of the air-conditioning system in combination with the indoor temperature and the target temperature of the air-conditioning system set by the user.

[0022] It should be noted that the opening and closing of the energy efficiency management mode is controlled by the user, and the opening and closing of the intelligent energy efficiency management mode and the forced energy efficiency management mode are also controlled by the user. The intelligent energy efficiency management mode first predicts the target temperature that the air-conditioning system needs to adjust in the future time period based on the target temperature prediction model according to the environmental parameters, and then automatically adjusts the compressor operating frequency and the fan operating speed according to the energy efficiency ratio of the air-conditioning system obtained by real-time calculation, mainly for the purpose of meeting the user's comfort; while the forced energy efficiency management mode adjusts the compressor operating frequency and the fan operating speed of the air-conditioning system according to the indoor temperature and the target temperature of the air-conditioning system set by the user, mainly for the purpose of saving energy and optimizing the energy efficiency ratio.

[0023] In one embodiment of the present invention, air quality is represented by inhalable particulate matter (PM2.5), which is collected by a PM2.5 detector. Air quality can also be represented by carbon dioxide concentration, which is collected by a carbon dioxide concentration detector. Temperature and humidity, rainfall and light intensity are collected by temperature and humidity sensors, rain gauges and light intensity sensors respectively. Refrigerant flow is collected by a liquid flow sensor, and electric power is collected by an electric energy meter.

[0024] In one embodiment of the present invention, the first time period T1, the preset time interval t1 and the second time period T2 are all custom parameters. Preferably, the first time period T1 is set to 5 minutes, the preset time interval t1 is set to 15 seconds, then N=T1 / t1=20, and the second time period T2 is set to 30 minutes.

[0025] In one embodiment of the present invention, Figure 2 As shown, preprocessing the environmental parameters to generate an environmental sequence includes the following steps: Step S201, for missing values ​​in environmental parameters, interpolation filling is performed by taking the average value of the environmental parameters at two adjacent time points of the missing value; Step S202, respectively calculating the change rate of the environmental parameter at each time point, and concatenating it with the environmental parameter at each time point to obtain a combined vector; The change rate of the environmental parameter at the first time point is assigned a value of 1; Step S203, normalizing the combined vector at each time point by a standard score (Z-score) method to obtain an environment sequence.

[0026] It should be noted that the normalized combination vector corresponds to a sequence unit of the environmental sequence, and the size of the normalized combination vector is 1×16, that is, the number of dimensions is 16, which are 8 values ​​of the environmental parameters and 8 change rate values ​​of the environmental parameters. In addition, the average value, maximum value, minimum value, skewness and kurtosis of the environmental parameters can also be extracted, which will not be elaborated here.

[0027] In one embodiment of the present invention, the target temperature prediction model includes N hidden layers, each hidden layer includes: a first unit and a second unit; The first unit of the nth hidden layer inputs the nth sequence unit of the environment sequence and outputs the update matrix; The second unit of the nth hidden layer inputs the update matrix output by the first unit of the nth hidden layer and outputs an update vector; The update vector output by the second unit of the Nth hidden layer is input to the first classifier, and the classification space of the first classifier represents the target temperature of the air-conditioning system in the second time period T2.

[0028] In one embodiment of the present invention, the calculation formula of the target temperature prediction model includes: The calculation formula for the first unit of the nth hidden layer is as follows: ; in represents the update matrix of the output of the first unit of the nth hidden layer, represents the nth sequence unit of the environment sequence input to the first unit of the nth hidden layer, and They represent the weight parameter and bias parameter of the first unit of the nth hidden layer, respectively. represents a convolution operation with a kernel size of 3×3 and a stride of 1. represents the maximum pooling operation with a pooling window size of 3×3 and a step size of 1, and sigmoid represents the sigmoid activation function; The calculation formula for the second unit of the nth hidden layer includes: ; ; ; in represents the update vector for the output of the second unit of the nth hidden layer, represents the intermediate vector of the second unit of the nth hidden layer, and denote the update matrices for the second unit inputs of the nth and n-1th hidden layers, respectively, It is represented by a matrix with the same size as the update matrix and all element values ​​are 0. express and The correlation coefficient between them, K represents the number of elements in the update matrix, spread represents expanding the matrix into a vector representation and sorting the elements in ascending order, and Rank represents the rank value calculation. and denote the first weight parameter and the second weight parameter of the second unit of the nth hidden layer, respectively. and denote the first bias parameter and the second bias parameter of the second unit of the nth hidden layer respectively, and Swish denotes the Swish activation function.

[0029] It should be noted that the calculation of the update vector does not need to rely on the output of the previous hidden layer when calculating the output of the current hidden layer like RNN (recurrent neural network model) or LSTM (long short-term memory neural network model). The second unit provided by the present invention calculates the correlation between the output of the current hidden layer and the output of the previous hidden layer through the Spearman rank correlation coefficient, realizes the "gating" function through the Spearman rank correlation coefficient, and can perform parallel calculation and parallel training, thereby greatly improving the calculation speed of the target temperature prediction model.

[0030] It should be noted that the weight parameters and bias parameters in the target temperature prediction model are all learnable hyperparameters, such as The size is 1×16, It can be designed as a 16×1 vector. The two are multiplied to get a 16×16 matrix. After the convolution operation with a convolution kernel of 3×3 and a step size of 1, a 14×14 matrix is ​​obtained. After the maximum pooling operation with a pooling window of 3×3 and a step size of 1, a 12×12 matrix is ​​obtained. The size of the updated matrix is ​​12×12, so K is equal to 12×12, which is equal to 144. and They can all be designed as vectors of size 1×12, then the size of the intermediate vector is 1×12. It can be designed as a matrix of size 12×8, and the size of the update vector obtained by multiplying the two is 1×8.

[0031] In one embodiment of the present invention, multiple independent experimental rooms are built to simulate the same environmental parameters, and sample data of training samples for training the target temperature prediction model are obtained according to step S101 to step S102, and then the target temperature is manually set for each independent experimental room. The difference between the target temperatures of each independent experimental room cannot exceed 5°C. After the second time period T2, the target temperature with the maximum average value of the comfort of multiple people in the experimental room is taken as the sample label of the training sample for training the target temperature prediction model.

[0032] In one embodiment of the present invention, a training sample includes a sample data and a sample label, and the physical signs of the people in each experimental room are as close as possible to ensure the accuracy of the target temperature prediction model. In addition, in order to speed up the training speed of the target temperature prediction model, pre-training can be added, and the update vector output by the second unit of the Nth hidden layer is input into the second classifier, and the classification space of the second classifier represents the indoor temperature in the second time period T2.

[0033] It should be noted that the mean square error between the predicted value of the target temperature prediction model and the sample label of the training sample is specified as the loss function, and the weight parameters and bias parameters of the target temperature prediction model are updated by the chain rule and gradient descent algorithm to minimize the loss value calculated by the loss function.

[0034] In one embodiment of the present invention, the calculation formula of the energy efficiency ratio COP of the air conditioning system is as follows: ; Where Cp represents the specific heat capacity at constant pressure, which is assigned a value of 4.1868KJ / kg·℃, and R represents the specific gravity of the refrigerant, which is assigned a value of 1000Kg / m³. It indicates the refrigerant flow rate in m³ / h, Ts indicates the outlet water temperature in °C, Tw indicates the inlet water temperature in °C, and P indicates the electrical power in kW.

[0035] In one embodiment of the present invention, Figure 3 As shown, the intelligent energy efficiency management mode adjusts the compressor operating frequency and fan operating speed of the air conditioning system, including the following steps: Step S301, calculating the absolute value of the difference between the water temperature and the target temperature of the air conditioning system in the second time period T2 as the temperature difference value, and judging that the temperature difference value is less than or equal to 1°C, then proceeding to step S302; Step S302, in the third time period T3, the energy efficiency ratio is calculated according to the second preset time interval t2, and the average value of the energy efficiency ratios at all time points is calculated as a reference value of the energy efficiency ratio; The third time period T3 and the second preset time interval t2 are both custom parameters. Preferably, the third time period T3 is set to 3 minutes, and the second preset time interval t2 is set to 10 seconds; Step S303, judging that the energy efficiency ratio at the current time point is greater than or equal to the energy efficiency ratio reference value, and the temperature difference value is less than or equal to 1°C, then reducing the compressor operating frequency by 1 Hz and running for 30 seconds, and entering step S304; Step S304, if it is determined that the energy efficiency ratio at the current time point is greater than or equal to the energy efficiency ratio reference value, and the temperature difference is less than or equal to 1°C, the fan speed is reduced by 1% and the fan is operated for 30 seconds, and the process proceeds to step S305; Step S305: If the energy efficiency ratio at the current time point is less than the energy efficiency ratio reference value or the temperature difference value is greater than 1°C, then stop adjusting and return to step S301.

[0036] In one embodiment of the present invention, Figure 4 As shown, the forced energy efficiency management mode adjusts the compressor operating frequency and fan operating speed of the air conditioning system, including the following steps: Step S401, determining that the indoor temperature is within a preset indoor temperature range and the outlet water temperature is within a preset outlet water temperature range, and determining an energy efficiency ratio reference value according to a mapping relationship between the indoor temperature and the outlet water temperature and a preset energy efficiency ratio; The preset indoor temperature range, preset water outlet temperature range, and the mapping relationship between the indoor temperature and water outlet temperature and the preset energy efficiency ratio are all custom settings; Step S402, calculating the absolute value of the difference between the water temperature and the target temperature of the air conditioning system set by the user as the temperature difference value, and judging that the temperature difference value is less than or equal to 2°C, then proceeding to step S403; Step S403, judging that the energy efficiency ratio at the current time point is greater than or equal to the energy efficiency ratio reference value, and the temperature difference value is less than or equal to 2°C, then reducing the compressor operating frequency by 1 Hz and running for 30 seconds, and entering step S404; Step S404, if it is determined that the energy efficiency ratio at the current time point is greater than or equal to the energy efficiency ratio reference value, and the temperature difference is less than or equal to 2°C, the fan speed is reduced by 1% and the fan is operated for 30 seconds, and the process proceeds to step S405; Step S405: If the energy efficiency ratio at the current time point is less than the energy efficiency ratio reference value or the temperature difference value is greater than 2°C, then stop adjusting and return to step S401.

[0037] In one embodiment of the present invention, Figure 5 As shown, an energy efficiency control system based on energy-saving air conditioning includes: An environment sequence generation module 501 is used to collect environment parameters of the location of the air conditioning system within a first time period T1 according to a first preset time interval t1, and perform preprocessing to generate an environment sequence; A target temperature prediction module 502, which is used to input the environment sequence into the target temperature prediction model, and the output value represents the target temperature of the air conditioning system in the second time period T2; The intelligent energy efficiency management module 503 is used to collect the operating parameters of the air-conditioning system, calculate the energy efficiency ratio of the air-conditioning system, and adjust the compressor operating frequency and fan operating speed of the air-conditioning system in combination with the target temperature of the air-conditioning system in the second time period T2; The mandatory energy efficiency management module 504 is used to collect the operating parameters of the air-conditioning system to calculate the energy efficiency ratio of the air-conditioning system, and adjust the compressor operating frequency and fan operating speed of the air-conditioning system in combination with the indoor temperature and the target temperature of the air-conditioning system set by the user.

[0038] The above describes an embodiment of the present embodiment, but the present embodiment is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present embodiment, ordinary technicians in this field can also make many forms, all of which are within the protection of the present embodiment.

Claims

1. An energy efficiency control method based on energy-saving air conditioning, characterized in that: The following steps are involved: Step S101: when the user turns on the energy efficiency management mode, and the energy efficiency management mode is set to the intelligent energy efficiency management mode, the process proceeds to step S102; when the energy efficiency management mode is set to the forced energy efficiency management mode, the process proceeds to step S105; Step S102, within a first time period T1, collecting environmental parameters of the location of the air conditioning system according to a first preset time interval t1, and performing preprocessing to generate an environmental sequence; Environmental parameters include: indoor temperature and humidity, indoor air quality, outdoor temperature and humidity, outdoor air quality, outdoor rainfall and outdoor light intensity; The environmental sequence includes N sequence units, and the nth sequence unit represents the environmental parameters at the nth time point after pretreatment, where 1≤n≤N, N=T1 / t; Step S103, inputting the environment sequence into the target temperature prediction model, and the output value represents the target temperature of the air conditioning system in the second time period T2; Step S104, collecting the operating parameters of the air-conditioning system, calculating the energy efficiency ratio of the air-conditioning system, and adjusting the compressor operating frequency and the fan operating speed of the air-conditioning system in combination with the target temperature of the air-conditioning system in the second time period T2; The operating parameters include: inlet water temperature, outlet water temperature, refrigerant flow rate and electrical power; The larger the value of the energy efficiency ratio, the higher the energy conversion efficiency and the less power consumption; Step S105, collecting the operating parameters of the air-conditioning system, calculating the energy efficiency ratio of the air-conditioning system, and adjusting the compressor operating frequency and fan operating speed of the air-conditioning system in combination with the indoor temperature and the target temperature of the air-conditioning system set by the user.

2. The energy efficiency control method based on energy-saving air conditioning according to claim 1, characterized in that: The first time period T1, the preset time interval t1 and the second time period T2 are all custom parameters.

3. The energy efficiency control method based on energy-saving air conditioning according to claim 1, characterized in that: Preprocessing the environment parameters to generate an environment sequence includes the following steps: Step S201, for missing values ​​in environmental parameters, interpolation filling is performed by taking the average value of the environmental parameters at two adjacent time points of the missing value; Step S202, respectively calculating the change rate of the environmental parameter at each time point, and concatenating it with the environmental parameter at each time point to obtain a combined vector; The change rate of the environmental parameter at the first time point is assigned a value of 1; Step S203, normalizing the combined vector at each time point by a standard score method to obtain an environment sequence.

4. The energy efficiency control method based on energy-saving air conditioning according to claim 1, characterized in that: The target temperature prediction model includes N hidden layers, each hidden layer includes: a first unit and a second unit; The first unit of the nth hidden layer inputs the nth sequence unit of the environment sequence and outputs the update matrix; The second unit of the nth hidden layer inputs the update matrix output by the first unit of the nth hidden layer and outputs an update vector; The update vector output by the second unit of the Nth hidden layer is input to the first classifier, and the classification space of the first classifier represents the target temperature of the air-conditioning system in the second time period T2.

5. The energy efficiency control method based on energy-saving air conditioning according to claim 4, characterized in that: The calculation formula of the target temperature prediction model includes: The calculation formula for the first unit of the nth hidden layer is as follows: ； in represents the update matrix of the output of the first unit of the nth hidden layer, represents the nth sequence unit of the environment sequence input to the first unit of the nth hidden layer, and They represent the weight parameter and bias parameter of the first unit of the nth hidden layer, respectively. represents a convolution operation with a kernel size of 3×3 and a stride of 1. represents the maximum pooling operation with a pooling window size of 3×3 and a step size of 1, and sigmoid represents the sigmoid activation function; The calculation formula for the second unit of the nth hidden layer includes: ； ； ； in represents the update vector for the output of the second unit of the nth hidden layer, represents the intermediate vector of the second unit of the nth hidden layer, and denote the update matrices for the second unit inputs of the nth and n-1th hidden layers, respectively, It is represented by a matrix with the same size as the update matrix and all element values ​​are 0. express and The correlation coefficient between them, K represents the number of elements in the update matrix, spread represents expanding the matrix into a vector representation and sorting the elements in ascending order, and Rank represents the rank value calculation. and denote the first weight parameter and the second weight parameter of the second unit of the nth hidden layer, respectively. and denote the first bias parameter and the second bias parameter of the second unit of the nth hidden layer respectively, and Swish denotes the Swish activation function.

6. The energy efficiency control method based on energy-saving air conditioning according to claim 1, characterized in that: Build multiple independent experimental rooms to simulate the same environmental parameters, and obtain sample data of training samples for training the target temperature prediction model according to steps S101 to S102, and then manually set the target temperature for each independent experimental room. The difference between the target temperatures of each independent experimental room cannot exceed 5°C. After the second time period T2, take the target temperature with the maximum average value of the comfort of multiple people in the experimental room as the sample label of the training sample for training the target temperature prediction model.

7. The energy efficiency control method based on energy-saving air conditioning according to claim 1, characterized in that: The calculation formula of the energy efficiency ratio COP of the air conditioning system is as follows: ； Where Cp represents the specific heat capacity at constant pressure, which is assigned a value of 4.1868KJ / kg·℃, and R represents the specific gravity of the refrigerant, which is assigned a value of 1000Kg / m³. It indicates the refrigerant flow rate in m³ / h, Ts indicates the outlet water temperature in °C, Tw indicates the inlet water temperature in °C, and P indicates the electrical power in kW.

8. The energy efficiency control method based on energy-saving air conditioning according to claim 1, characterized in that: The intelligent energy efficiency management mode adjusts the compressor operating frequency and fan operating speed of the air conditioning system, including the following steps: Step S301, calculating the absolute value of the difference between the water temperature and the target temperature of the air conditioning system in the second time period T2 as the temperature difference value, and judging that the temperature difference value is less than or equal to 1°C, then proceeding to step S302; Step S302, in the third time period T3, the energy efficiency ratio is calculated according to the second preset time interval t2, and the average value of the energy efficiency ratios at all time points is calculated as a reference value of the energy efficiency ratio; The third time period T3 and the second preset time interval t2 are both custom parameters; Step S303, judging that the energy efficiency ratio at the current time point is greater than or equal to the energy efficiency ratio reference value, and the temperature difference value is less than or equal to 1°C, then reducing the compressor operating frequency by 1 Hz and running for 30 seconds, and entering step S304; Step S304, if it is determined that the energy efficiency ratio at the current time point is greater than or equal to the energy efficiency ratio reference value, and the temperature difference is less than or equal to 1°C, the fan speed is reduced by 1% and the fan is operated for 30 seconds, and the process proceeds to step S305; Step S305: If the energy efficiency ratio at the current time point is less than the energy efficiency ratio reference value or the temperature difference value is greater than 1°C, then stop adjusting and return to step S301.

9. The energy efficiency control method based on energy-saving air conditioning according to claim 1, characterized in that: The mandatory energy efficiency management mode adjusts the compressor operating frequency and fan operating speed of the air conditioning system, including the following steps: Step S401, determining that the indoor temperature is within a preset indoor temperature range and the outlet water temperature is within a preset outlet water temperature range, and determining an energy efficiency ratio reference value according to a mapping relationship between the indoor temperature and the outlet water temperature and a preset energy efficiency ratio; The preset indoor temperature range, preset water outlet temperature range, and the mapping relationship between the indoor temperature and water outlet temperature and the preset energy efficiency ratio are all custom settings; Step S402, calculating the absolute value of the difference between the water temperature and the target temperature of the air conditioning system set by the user as the temperature difference value, and judging that the temperature difference value is less than or equal to 2°C, then proceeding to step S403; Step S403, judging that the energy efficiency ratio at the current time point is greater than or equal to the energy efficiency ratio reference value, and the temperature difference value is less than or equal to 2°C, then reducing the compressor operating frequency by 1 Hz and running for 30 seconds, and entering step S404; Step S404, if it is determined that the energy efficiency ratio at the current time point is greater than or equal to the energy efficiency ratio reference value, and the temperature difference is less than or equal to 2°C, the fan speed is reduced by 1% and the fan is operated for 30 seconds, and the process proceeds to step S405; Step S405: If the energy efficiency ratio at the current time point is less than the energy efficiency ratio reference value or the temperature difference value is greater than 2°C, then stop adjusting and return to step S401.

10. An energy efficiency control system based on energy-saving air conditioning, characterized in that: Executing an energy efficiency control method based on energy-saving air conditioning as described in any one of claims 1 to 9, comprising: An environment sequence generation module, which is used to collect the environment parameters of the location of the air-conditioning system within a first time period T1 according to a first preset time interval t1, and perform preprocessing to generate an environment sequence; a target temperature prediction module, which is used to input the environment sequence into the target temperature prediction model, and the output value represents the target temperature of the air conditioning system in the second time period T2; An intelligent energy efficiency management module, which is used to collect operating parameters of the air-conditioning system, calculate the energy efficiency ratio of the air-conditioning system, and adjust the compressor operating frequency and fan operating speed of the air-conditioning system in combination with the target temperature of the air-conditioning system in the second time period T2; The mandatory energy efficiency management module is used to collect the operating parameters of the air-conditioning system to calculate the energy efficiency ratio of the air-conditioning system, and adjust the compressor operating frequency and fan operating speed of the air-conditioning system in combination with the indoor temperature and the target temperature of the air-conditioning system set by the user.

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