CN117708606A - Method for predicting air conditioner energy consumption, electronic equipment and storage medium - Google Patents
Method for predicting air conditioner energy consumption, electronic equipment and storage medium Download PDFInfo
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- 238000005265 energy consumption Methods 0.000 title claims abstract description 92
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- 238000013528 artificial neural network Methods 0.000 claims abstract description 19
- 230000000306 recurrent effect Effects 0.000 claims abstract description 19
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- 238000004378 air conditioning Methods 0.000 claims description 4
- 238000013468 resource allocation Methods 0.000 abstract description 14
- 238000010606 normalization Methods 0.000 description 7
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Abstract
The application provides a method for predicting air conditioner energy consumption, which relates to the technical field of intelligent household appliances and comprises the steps of determining real-time environment data of an air conditioner; inputting the real-time environment data into a pre-trained neural network model to conduct energy consumption prediction of a preset time period so as to obtain an energy consumption prediction result of the air conditioner in the preset time period; the neural network model is built based on a boltzmann machine. The energy consumption prediction result of the air conditioner in a preset time period is determined according to the real-time environment data by utilizing a neural network model built based on the Boltzmann machine and the recurrent neural network, so that the air conditioner energy consumption can be predicted, the air conditioner energy consumption resource allocation can be performed according to the prediction result, and the situation that the air conditioner energy consumption resource allocation is inconsistent with the actual energy consumption requirement of the air conditioner is reduced. A model training method, electronic equipment and storage medium are also provided.
Description
Technical Field
The application relates to the technical field of intelligent household appliances, in particular to a method for predicting air conditioner energy consumption, a model training method, electronic equipment and a storage medium.
Background
An Air Conditioner (Air Conditioner) is an apparatus for manually adjusting and controlling parameters such as temperature, humidity, and flow rate of Air in the environment of a building or structure.
Air conditioners generally comprise a cold source/heat source device, a cold and hot medium delivery system, a terminal device and other auxiliary devices. Mainly comprises the following steps: refrigeration host, water pump, fan and pipe system. The tail end device is responsible for specifically processing the air state by utilizing the transmitted cold and hot quantity, so that the air parameters of the target environment reach certain requirements.
In the operation process of the air conditioner, a large amount of energy consumption can be involved, the prior art distributes the energy consumption resources of the air conditioner, the energy consumption can not be matched with the energy consumption actually required by the air conditioner, if the energy consumption resources of the air conditioner are not distributed enough, the operation of the air conditioner can be influenced, and if the energy consumption resources of the air conditioner are distributed too much, the resource waste can be caused.
Disclosure of Invention
The application provides a method for predicting air conditioner energy consumption, a model training method, electronic equipment and a storage medium, which are used for at least solving the problem that the distribution of air conditioner energy consumption resources in the prior art cannot be matched with the energy consumption of the actual requirement of an air conditioner.
According to a first aspect of embodiments of the present application, there is provided a method for predicting energy consumption of an air conditioner, the method comprising:
determining real-time environment data of an air conditioner;
inputting the real-time environment data into a pre-trained neural network model to conduct energy consumption prediction of a preset time period so as to obtain an energy consumption prediction result of the air conditioner in the preset time period;
the neural network model is built based on a boltzmann machine.
According to the embodiment of the application, the energy consumption prediction result of the air conditioner in the preset time period is determined according to the real-time environment data by utilizing the neural network model built based on the Boltzmann machine and the recurrent neural network, so that the air conditioner energy consumption can be predicted, the air conditioner energy consumption resource allocation can be performed according to the prediction result, and the situation that the air conditioner energy consumption resource allocation is inconsistent with the actual energy consumption requirement of the air conditioner is reduced.
In some embodiments, the determining real-time environment data of the air conditioner includes:
acquiring real-time environment parameters of an air conditioner;
normalizing the real-time environment parameters, and determining that the normalized data is the real-time environment data of the air conditioner.
In some embodiments, the environmental parameters include one or a combination of the following:
outdoor temperature, indoor temperature, humidity, load, current set temperature of the air conditioner.
In some embodiments, the pre-trained neural network model is built in conjunction with a constrained boltzmann machine and a recurrent neural network, the pre-trained neural network model comprising:
the input layer, upper and lower layers, hidden layer and output layer, wherein upper and lower layers are parallel with hidden layer, are located between input layer and the output layer.
In some embodiments, the method for predicting air conditioner energy consumption further comprises:
acquiring real-time environment parameters of a set time interval of an air conditioner;
determining training data according to the real-time environment parameters of the set time interval;
inputting the training data into a neural network model for model training to obtain a trained neural network model;
the neural network model is built based on a Boltzmann machine and a recurrent neural network, the input of the neural network model is real-time environment data, and the output is an energy consumption prediction result of the air conditioner in a preset time period.
In some embodiments, the inputting the training data into a neural network model for model training comprises:
determining initial weights among layers of the neural network model through a random function;
and adjusting weights among layers of the neural network model based on the initial weights in a model training process.
In some embodiments, the adjusting the weights between the layers of the neural network model based on the initial weights in the model training process is achieved by:
for the weight between the input layer and the hidden layer, or the weight between the output layer and the hidden layer, the adjusted weight is determined by the following formula:
wherein E (v, h) is a network function, W when determining hidden layer adjusted weights ij For the weight between the input layer node i and the hidden layer node j, v i Represents the state of the ith input cell, h j Represents the state of the jth hidden unit, a i B for input layer bias j Bias for hidden layer;
when the weights of the output layer and the hidden layer after adjustment are determined, W ij For the weight between the output layer node i and the hidden layer node j, v i Represents the state of the ith output unit, h j Represents the state of the jth hidden unit, a i B for output layer bias j Biased for the hidden layer.
In some embodiments, the method for predicting air conditioner energy consumption further comprises:
determining test data according to the real-time environment parameters of the set time interval;
and in the model training process, inputting the test data into the neural network model to perform model adjustment.
The embodiment of the application also provides a model training method, which comprises the following steps:
acquiring real-time environment parameters of a set time interval of an air conditioner;
determining training data according to the real-time environment parameters of the set time interval;
inputting the training data into a neural network model for model training to obtain a trained neural network model;
the neural network model is built based on a Boltzmann machine and a recurrent neural network, the input of the neural network model is real-time environment data, and the output is an energy consumption prediction result of the air conditioner in a preset time period.
According to the embodiment of the application, the energy consumption prediction result of the air conditioner in the preset time period is determined according to the real-time environment data by utilizing the neural network model built based on the Boltzmann machine and the recurrent neural network, so that the air conditioner energy consumption can be predicted, the air conditioner energy consumption resource allocation can be performed according to the prediction result, and the situation that the air conditioner energy consumption resource allocation is inconsistent with the actual energy consumption requirement of the air conditioner is reduced.
The embodiment of the application also provides electronic equipment, which comprises a processor and a memory storing program instructions, wherein the processor is configured to execute the method for predicting the energy consumption of the air conditioner or the model training method provided by the embodiment of the application when executing the program instructions.
The embodiment of the application also provides a storage medium, and the storage medium stores a computer program, and the computer program realizes the method for predicting the air conditioner energy consumption provided by the embodiment of the application or the model training method provided by the embodiment of the application when being executed by a processor.
In the embodiment of the application, the neural network model built based on the Boltzmann machine and the recurrent neural network is utilized, the energy consumption prediction result of the air conditioner in the preset time period is determined according to the real-time environment data, the air conditioner energy consumption can be predicted, the air conditioner energy consumption resource allocation can be performed according to the prediction result, and the situation that the air conditioner energy consumption resource allocation is inconsistent with the actual energy consumption requirement of the air conditioner is reduced.
Drawings
Fig. 1 is a schematic flow chart of a method for predicting energy consumption of an air conditioner according to an embodiment of the present application;
FIG. 2 is a flow chart of a method for determining real-time environmental data according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of a model training method according to an embodiment of the present application;
FIG. 4 is a flow chart of another model training method provided in an embodiment of the present application;
FIG. 5 is a flow chart of another model training method provided in an embodiment of the present application;
FIG. 6 is a flow chart of another method for predicting air conditioner energy consumption provided in an embodiment of the present application;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the present application solution better understood by those skilled in the art, the following description will be made in detail and with reference to the accompanying drawings in the embodiments of the present application, it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The method accurately predicts the refrigerating and heating requirements (energy consumption) in a period of time in the future, is beneficial to an air conditioner control system to automatically control and regulate or operation and maintenance personnel to manually regulate the operation of an air conditioner, ensures the service quality of the air conditioner (ensures the comfort of users for comfort air conditioners, has large matching test quantity required by the design of the air conditioner system for production process, seriously wastes the hydropower and human resources of companies, ensures the requirements of reasonably distributing energy consumption resources, saving energy and reducing emission for the companies, and reduces the energy consumption level of the air conditioner. The application provides an air conditioner energy consumption prediction system and a control method aiming at the problem that air conditioner energy consumption is difficult to predict due to the influence of multiple factors of indoor and outdoor environments.
According to embodiments of the present application, there is provided a method embodiment of a method for predicting air conditioner energy consumption, it should be noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that although a logical sequence is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in a different order than herein.
The application also provides a model training method for predicting the energy consumption of the air conditioner, electronic equipment and a storage medium, so as to at least solve the problem that the distribution of the energy consumption resources of the air conditioner in the prior art cannot be matched with the actual energy consumption of the air conditioner.
An embodiment of the present application provides one of the embodiments of an electronic device, including a processor and a memory storing program instructions, where the processor is configured to execute the method for predicting energy consumption of an air conditioner provided in the embodiment of the present application when executing the program instructions.
The second embodiment of the application provides an electronic device, which comprises a processor and a memory storing program instructions, wherein the processor is configured to execute the model training method provided by the embodiment of the application when executing the program instructions.
Specifically, as shown in fig. 1, an embodiment of the present application provides a method for predicting energy consumption of an air conditioner, where the method includes:
s101, a processor determines real-time environment data of an air conditioner;
s102, inputting the real-time environment data into a pre-trained neural network model by a processor to conduct energy consumption prediction of a preset time period so as to obtain an energy consumption prediction result of the air conditioner in the preset time period;
the neural network model is built based on a Boltzmann machine and a recurrent neural network.
According to the embodiment of the application, the energy consumption prediction result of the air conditioner in the preset time period is determined according to the real-time environment data by utilizing the neural network model built based on the Boltzmann machine and the recurrent neural network, so that the air conditioner energy consumption can be predicted, the air conditioner energy consumption resource allocation can be performed according to the prediction result, and the situation that the air conditioner energy consumption resource allocation is inconsistent with the actual energy consumption requirement of the air conditioner is reduced.
In some embodiments, as shown in fig. 2, the determining the real-time environment data of the air conditioner includes:
s201, a processor acquires real-time environment parameters of an air conditioner;
and S203, normalizing the real-time environment parameters by the processor, and determining that the normalized data are real-time environment data of the air conditioner.
In practical application, the normalization of the real-time environment parameters can be achieved according to the following manner.
Where x is the original data, maxIs the maximum value of x, min is the minimum value of x, x (0) (i) Is a normalized value.
The real-time environment parameters are processed into real-time environment data in (0, 1) through normalization standards, and the real-time environment parameters are shown in the following formula:
x (0) ＝{x (0) (i)|i＝1,2,3,…,N}
in some embodiments, the environmental parameters include one or a combination of the following:
indoor environment data, outdoor environment data, user setting data and performance data of the refrigerating system of the air conditioner;
in some embodiments, the environmental parameters include one or a combination of the following:
outdoor temperature, indoor humidity, air conditioning load, current set temperature of the air conditioner.
In some embodiments, the pre-trained neural network model is built in conjunction with a constrained boltzmann machine and a recurrent neural network, the pre-trained neural network model comprising:
the input layer, upper and lower layers, hidden layer and output layer, wherein upper and lower layers are parallel with hidden layer, are located between input layer and the output layer.
In some embodiments, as shown in fig. 3, the method for predicting energy consumption of an air conditioner further includes:
s301, a processor acquires real-time environment parameters of a set time interval of an air conditioner;
s302, the processor determines training data according to the real-time environment parameters of the set time interval;
s303, the processor inputs the training data into a neural network model for model training to obtain a trained neural network model;
the neural network model is built based on a Boltzmann machine and a recurrent neural network, the input of the neural network model is real-time environment data, and the output is an energy consumption prediction result of the air conditioner in a preset time period.
The neural network model is built by combining a limited Boltzmann machine and a recurrent neural network and comprises an input layer, an upper layer, a lower layer, a hidden layer and an output layer, wherein the upper layer and the lower layer are parallel to the hidden layer and are positioned between the input layer and the output layer.
In some embodiments, as shown in fig. 4, the inputting the training data into the neural network model for model training includes:
s401, determining initial weights among layers of the neural network model through a random function by a processor;
and S402, the processor adjusts weights among layers of the neural network model based on the initial weights in the model training process.
In some embodiments, the adjusting the weights between the layers of the neural network model based on the initial weights in the model training process is achieved by:
for the weight between the input layer and the hidden layer, or the weight between the output layer and the hidden layer, the adjusted weight is determined by the following formula:
wherein E (v, h) is a network function, W when determining hidden layer adjusted weights ij For the weight between the input layer node i and the hidden layer node j, v i Represents the state of the ith input cell, h j Represents the state of the jth hidden unit, a i B for input layer bias j Bias for hidden layer;
when the weights of the output layer and the hidden layer after adjustment are determined, W ij For the weight between the output layer node i and the hidden layer node j, v i Represents the state of the ith output unit, h j Represents the state of the jth hidden unit, a i B for output layer bias j Biased for the hidden layer.
In some embodiments, as shown in fig. 5, the method for predicting energy consumption of an air conditioner further includes:
s501, the processor determines test data according to the real-time environment parameters of the set time interval;
s502, the processor inputs the test data into the neural network model to carry out model adjustment in the model training process.
In practical application, the acquired real-time environment parameter of the set time interval may be divided into two parts, one part is used for determining training data, and the other part is used for determining test data.
In the process of determining training data and determining test data, normalization operation is needed for real-time environment parameters.
In practical application, relatively better training times and learning rate can be found through experiments, and the neural network model is trained by using the relatively better training times and learning rate, so that the prediction effect of the neural network model is better.
As shown in fig. 6, an embodiment of the present application further provides a method for predicting energy consumption of an air conditioner, including:
s601, a processor acquires real-time environment parameters of a set time interval of an air conditioner;
s602, the processor performs normalization processing on the real-time environment parameters of the set time interval.
S603, the processor determines training data according to the real-time environment parameters of the set time interval after normalization processing;
s604, inputting the training data into a neural network model by a processor to perform model training to obtain a trained neural network model;
s605, the processor determines test data according to the real-time environment parameters of the set time interval after normalization processing;
s606, the processor inputs the test data into the neural network model to carry out model adjustment in the model training process.
S607, the processor determines the initial weight among the layers of the neural network model through a random function;
and S608, the processor adjusts the weights among layers of the neural network model based on the initial weights in the model training process.
S609, the processor determines real-time environment data of the air conditioner;
and S610, the processor inputs the real-time environment data into a pre-trained neural network model to conduct energy consumption prediction of a preset time period so as to obtain an energy consumption prediction result of the air conditioner in the preset time period.
In practical application, after the energy consumption prediction result is determined, the energy consumption prediction result can be displayed or sent to a client system, and the system can be watched on a background page of a monitoring system of a computer end or a mobile end, so that a customer or operation and maintenance personnel can conveniently and manually adjust the operation of an air conditioner, and the service quality of the air conditioner is ensured.
The embodiment of the application also provides a model training method, which comprises the following steps:
acquiring real-time environment parameters of a set time interval of an air conditioner;
determining training data according to the real-time environment parameters of the set time interval;
inputting the training data into a neural network model for model training to obtain a trained neural network model;
the neural network model is built based on a Boltzmann machine and a recurrent neural network, the input of the neural network model is real-time environment data, and the output is an energy consumption prediction result of the air conditioner in a preset time period.
According to the embodiment of the application, the energy consumption prediction result of the air conditioner in the preset time period is determined according to the real-time environment data by utilizing the neural network model built based on the Boltzmann machine and the recurrent neural network, so that the air conditioner energy consumption can be predicted, the air conditioner energy consumption resource allocation can be performed according to the prediction result, and the situation that the air conditioner energy consumption resource allocation is inconsistent with the actual energy consumption requirement of the air conditioner is reduced.
The neural network model is built by combining a limited Boltzmann machine and a recurrent neural network and comprises an input layer, an upper layer, a lower layer, a hidden layer and an output layer, wherein the upper layer and the lower layer are parallel to the hidden layer and are positioned between the input layer and the output layer.
In some embodiments, the inputting the training data into a neural network model for model training comprises:
the processor determines initial weights among layers of the neural network model through random functions;
the processor adjusts weights between layers of the neural network model based on the initial weights during model training.
In some embodiments, the adjusting the weights between the layers of the neural network model based on the initial weights in the model training process is achieved by:
for the weight between the input layer and the hidden layer, or the weight between the output layer and the hidden layer, the adjusted weight is determined by the following formula:
wherein E (v, h) is a network function, W when determining hidden layer adjusted weights ij For the weight between the input layer node i and the hidden layer node j, v i Represents the state of the ith input cell, h j Represents the state of the jth hidden unit, a i B for input layer bias j Bias for hidden layer;
when the weights of the output layer and the hidden layer after adjustment are determined, W ij For the weight between the output layer node i and the hidden layer node j, v i Represents the state of the ith output unit, h j Represents the state of the jth hidden unit, a i B for output layer bias j Biased for the hidden layer.
In some embodiments, the method for predicting air conditioner energy consumption further comprises:
determining test data according to the real-time environment parameters of the set time interval;
and in the model training process, inputting the test data into the neural network model to perform model adjustment.
In practical application, the acquired real-time environment parameter of the set time interval may be divided into two parts, one part is used for determining training data, and the other part is used for determining test data.
In the process of determining training data and determining test data, normalization operation is needed for real-time environment parameters.
The embodiment of the application also provides a storage medium, wherein the storage medium stores a computer program, and the computer program realizes the method for predicting the energy consumption of the air conditioner provided by the embodiment of the application when being executed by a processor.
The embodiment of the application also provides a storage medium, and the storage medium stores a computer program, and the computer program realizes the model training method provided by the embodiment of the application when being executed by a processor.
In the embodiment of the application, the neural network model built based on the Boltzmann machine and the recurrent neural network is utilized, the energy consumption prediction result of the air conditioner in the preset time period is determined according to the real-time environment data, the air conditioner energy consumption can be predicted, the air conditioner energy consumption resource allocation can be performed according to the prediction result, and the situation that the air conditioner energy consumption resource allocation is inconsistent with the actual energy consumption requirement of the air conditioner is reduced.
As shown in fig. 7, an embodiment of the present disclosure provides an electronic device. Including a memory 701, a processor 702. The memory 701 and the processor 702 may communicate via a bus. The memory 701 is used for storing a computer program. The processor 702 is configured to execute a computer program to implement the method for predicting air conditioner energy consumption provided in the embodiment of the present application or the model training method provided in the embodiment of the present application.
Alternatively, the processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of an embodiment of a method disclosed in connection with the present application may be embodied directly in a hardware processor or in a combination of hardware and software modules in a processor.
The serial numbers in the embodiments of the present application are merely for description and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present application, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application and are intended to be comprehended within the scope of the present application.
Claims (10)
1. A method for predicting energy consumption of an air conditioner, the method comprising:
acquiring real-time environment data of an air conditioner;
inputting the real-time environment data into a pre-trained neural network model to conduct energy consumption prediction of a preset time period so as to obtain an energy consumption prediction result of the air conditioner in the preset time period;
the neural network model is built based on boltzmann machines and recurrent neural networks.
2. The method of claim 1, wherein the determining real-time environment data of the air conditioner comprises:
acquiring real-time environment parameters of an air conditioner;
normalizing the real-time environment parameters, and determining that the normalized data is the real-time environment data of the air conditioner.
3. The method of claim 2, wherein the environmental parameters comprise one or a combination of:
outdoor temperature, indoor humidity, air conditioning load, current set temperature of the air conditioner.
4. The method of claim 1, wherein the pre-trained neural network model is built in conjunction with a constrained boltzmann machine and a recurrent neural network, the pre-trained neural network model comprising:
the input layer, upper and lower layers, hidden layer and output layer, wherein upper and lower layers are parallel with hidden layer, are located between input layer and the output layer.
5. A method of model training, comprising:
acquiring real-time environment parameters of a set time interval of an air conditioner;
determining training data according to the real-time environment parameters of the set time interval;
inputting the training data into a neural network model for model training to obtain a trained neural network model;
the neural network model is built based on a Boltzmann machine and a recurrent neural network, the input of the neural network model is real-time environment data, and the output is an energy consumption prediction result of the air conditioner in a preset time period.
6. The method of claim 5, wherein inputting the training data into a neural network model for model training comprises:
determining initial weights among layers of the neural network model through a random function;
and adjusting weights among layers of the neural network model based on the initial weights in a model training process.
7. The method of claim 6, wherein the adjusting weights between layers of the neural network model based on the initial weights during model training is accomplished by:
for the weight between the input layer and the hidden layer, or the weight between the output layer and the hidden layer, the adjusted weight is determined by the following formula:
wherein E (v, h) is a network function, W when determining hidden layer adjusted weights ij For the weight between the input layer node i and the hidden layer node j, v i Represents the state of the ith input cell, h j Represents the state of the jth hidden unit, a i B for input layer bias j Bias for hidden layer;
when the weights of the output layer and the hidden layer after adjustment are determined, W ij For the weight between the output layer node i and the hidden layer node j, v i Represents the state of the ith output unit, h j Represents the state of the jth hidden unit, a i B for output layer bias j Biased for the hidden layer.
8. The method as recited in claim 5, further comprising:
determining test data according to the real-time environment parameters of the set time interval;
and in the model training process, inputting the test data into the neural network model to perform model adjustment.
9. An electronic device comprising a processor and a memory storing program instructions, wherein the processor is configured, when executing the program instructions, to perform the method for predicting air conditioning energy consumption of any one of claims 1 to 4 or the model training method of any one of claims 5 to 8.
10. A storage medium storing a computer program which, when executed by a processor, implements the method for predicting air conditioning energy consumption according to any one of claims 1 to 4, or the model training method according to any one of claims 5 to 8.
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| CN202311600188.9A CN117708606A (en) | 2023-11-27 | 2023-11-27 | Method for predicting air conditioner energy consumption, electronic equipment and storage medium |
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