CN117748498A - Method, device, equipment and storage medium for predicting electric load - Google Patents
Method, device, equipment and storage medium for predicting electric load Download PDFInfo
- Publication number
- CN117748498A CN117748498A CN202410018517.7A CN202410018517A CN117748498A CN 117748498 A CN117748498 A CN 117748498A CN 202410018517 A CN202410018517 A CN 202410018517A CN 117748498 A CN117748498 A CN 117748498A
- Authority
- CN
- China
- Prior art keywords
- load
- time period
- data
- neural network
- convolutional neural
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 62
- 238000003860 storage Methods 0.000 title claims abstract description 17
- 230000005611 electricity Effects 0.000 claims abstract description 100
- 238000013527 convolutional neural network Methods 0.000 claims abstract description 84
- 238000012545 processing Methods 0.000 claims abstract description 15
- 238000012549 training Methods 0.000 claims description 23
- 238000004590 computer program Methods 0.000 claims description 21
- 238000004364 calculation method Methods 0.000 claims description 13
- 238000011176 pooling Methods 0.000 claims description 9
- 238000007781 pre-processing Methods 0.000 claims description 9
- 238000010606 normalization Methods 0.000 claims description 7
- 230000002159 abnormal effect Effects 0.000 claims description 5
- 238000004140 cleaning Methods 0.000 claims description 5
- 238000001914 filtration Methods 0.000 claims description 4
- 238000005516 engineering process Methods 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 10
- 238000004891 communication Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 7
- 230000006870 function Effects 0.000 description 6
- 238000013528 artificial neural network Methods 0.000 description 4
- 230000005236 sound signal Effects 0.000 description 4
- 230000006399 behavior Effects 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 230000009471 action Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- KLDZYURQCUYZBL-UHFFFAOYSA-N 2-[3-[(2-hydroxyphenyl)methylideneamino]propyliminomethyl]phenol Chemical compound OC1=CC=CC=C1C=NCCCN=CC1=CC=CC=C1O KLDZYURQCUYZBL-UHFFFAOYSA-N 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000002790 cross-validation Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 201000001098 delayed sleep phase syndrome Diseases 0.000 description 1
- 208000033921 delayed sleep phase type circadian rhythm sleep disease Diseases 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 230000001932 seasonal effect Effects 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application provides a power consumption load prediction method, a device, equipment and a storage medium, which relate to a data processing technology used for improving the accuracy of power consumption load prediction. The method mainly comprises the following steps: acquiring historical electricity load data and historical weather data of a target user in a preset time period; inputting the historical electricity load data of the preset time period into a first convolutional neural network to predict and obtain a first electricity load of the target user in a future time period; inputting the historical electricity load data of the preset time period into a second convolutional neural network to predict to obtain a second electricity load of the target user in a future time period; and calculating the final electric load of the target user in a future time period according to the first electric load and the second electric load.
Description
Technical Field
The present disclosure relates to the field of data analysis technologies, and in particular, to a method, an apparatus, a device, and a storage medium for predicting an electrical load.
Background
The user is a basic component in the power grid and is also a source for causing load fluctuation of the power grid. At present, through data processing on the electricity consumption behavior of the user, the electricity consumption behavior rule of the user can be modeled to mine the attribute closely related to the electricity consumption behavior of the user, so that the electricity consumption load of the user is predicted.
At present, in the process of predicting the electricity load of a user, the correlation between the features needs to be analyzed and processed, if strong correlation or collinearity exists between the features, the model can be excessively dependent on certain features, and other important features are ignored, so that the accuracy of electricity load prediction is affected.
Disclosure of Invention
The application provides a power consumption load prediction method, a device, equipment and a storage medium, which are used for improving the accuracy of power consumption load prediction.
In a first aspect, there is provided a method of electrical load prediction, the method comprising:
acquiring historical electricity load data and historical weather data of a target user in a preset time period;
inputting the historical electricity load data of the preset time period into a first convolutional neural network to predict and obtain a first electricity load of the target user in a future time period;
inputting the historical electricity load data of the preset time period into a second convolutional neural network to predict to obtain a second electricity load of the target user in a future time period;
and calculating the final electric load of the target user in a future time period according to the first electric load and the second electric load.
In an alternative embodiment, the method further comprises:
acquiring electricity load sample data and weather sample data of a target time period from the historical data;
preprocessing the electricity load sample data and the weather sample data, wherein the preprocessing at least comprises data cleaning, missing value filling, abnormal value filtering and normalization processing;
and performing model training according to the preprocessed electricity load sample data, weather sample data and the electricity load of a future time period corresponding to the corresponding target time period to obtain the first convolutional neural network and the second convolutional neural network.
In an alternative embodiment, the training of the model according to the preprocessed electricity load sample data, the weather sample data and the electricity load of the future time period corresponding to the corresponding target time period to obtain the first convolutional neural network and the second convolutional neural network includes:
taking the preprocessed electricity load sample data as sample data, and taking the electricity load of a future time period corresponding to the target time period as a sample label for model training to obtain the first convolutional neural network;
and taking the preprocessed electricity load sample data and weather sample data as sample data, and taking the electricity load of the future time period corresponding to the target time period as a sample label to perform model training to obtain the second convolutional neural network.
In an alternative embodiment, the calculating the final electric load of the target user in a future time period according to the first electric load and the second electric load includes:
and carrying out weighted calculation on the first electric load and the second electric load to obtain the final electric load of the target user in a future time period.
In an alternative embodiment, the calculating the final electric load of the target user in a future time period according to the first electric load and the second electric load includes:
and carrying out average calculation on the first electric load and the second electric load to obtain the final electric load of the target user in a future time period.
In an alternative embodiment, the first convolutional neural network and the second convolutional neural network comprise at least a convolutional layer, a pooling layer, a fully-connected layer.
In an alternative embodiment, the historical weather data includes at least: temperature, wind speed, humidity.
In a second aspect, there is provided an electrical load prediction apparatus, the apparatus comprising:
the acquisition module is used for acquiring historical electricity load data and historical weather data of a preset time period of a target user;
the first prediction module is used for inputting the historical electricity load data of the preset time period into a first convolutional neural network to predict and obtain the first electricity load of the target user in a future time period;
the second prediction module is used for inputting the historical power utilization load data and the historical weather data of the preset time period into a second convolutional neural network to predict and obtain a second power utilization load of the target user in a future time period;
and the calculation module is used for calculating the final electric load of the target user in a future time period according to the first electric load and the second electric load.
In a third aspect, there is provided an electronic device comprising: a processor and a memory for storing a computer program, the processor being for invoking and running the computer program stored in the memory for performing the method as in the first aspect or in various implementations thereof.
In a fourth aspect, a computer-readable storage medium is provided for storing a computer program for causing a computer to perform the method as in the first aspect or in various implementations thereof.
In a fifth aspect, a computer program product is provided comprising computer program instructions for causing a computer to perform the method as in the first aspect or in various implementations thereof.
In a sixth aspect, a computer program is provided, the computer program causing a computer to perform the method as in the first aspect or in various implementations thereof.
According to the technical scheme, firstly, historical electricity load data and historical weather data of a preset time period of a target user are obtained, then the historical electricity load data of the preset time period is input into a first convolution neural network to predict and obtain first electricity load of the target user in a future time period, the historical electricity load data of the preset time period and the historical weather data are input into a second convolution neural network to predict and obtain second electricity load of the target user in the future time period, and finally final electricity load of the target user in the future time period is obtained through calculation according to the first electricity load and the second electricity load. Compared with the prior art that the model excessively depends on certain characteristics, the method and the device have the advantages that based on the combined action of the first convolutional neural network and the second convolutional neural network, the final power load of the user in a future time period is obtained, and the first convolutional neural network and the second convolutional neural network in the method and the device do not depend on a specific characteristic, so that the power load prediction accuracy of the user can be improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:
FIG. 1 is a flowchart of a method for predicting an electrical load according to an embodiment of the present invention;
fig. 2 is a training flowchart of a convolutional neural network according to an embodiment of the present application;
FIG. 3 is a schematic diagram of an electrical load prediction apparatus according to an embodiment of the present disclosure;
fig. 4 is a schematic block diagram of an electronic device provided in an embodiment of the present application.
Detailed Description
The invention will be described in detail below with reference to the drawings in connection with embodiments. It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
The following detailed description is exemplary and is intended to provide further details of the invention. Unless defined otherwise, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the invention.
Fig. 1 is a flowchart of a method for predicting an electrical load according to an embodiment of the present application, where the method may include the following steps:
s110: and acquiring historical electricity load data and historical weather data of the target user in a preset time period.
The preset time period may be divided in units of hours, weeks, months, for example, 3 hours, 1 week, 1 month, etc., which is not particularly limited in this embodiment. In order to improve the accuracy of the power load prediction, the preset time period in this embodiment is a historical time period starting from the current time.
For example, when the current time point is 2023, 4, 15 days, and the duration corresponding to the preset time period is 5 days, the historical electricity load data and the historical weather data of 2023, 4, 10 days, 2023, 4, 14 days are obtained.
In this embodiment, the historical electricity load data is electricity load data actually generated by the target user in the history, and the historical weather data is also actual weather data. Wherein, the historical weather data includes at least: all of the temperature, wind speed, humidity and the like can influence the weather data of the electricity consumption of the user.
S120: and inputting the historical electricity load data of the preset time period into a first convolutional neural network to predict and obtain the first electricity load of the target user in the future time period.
S130: and inputting the historical electricity load data of the preset time period into a second convolutional neural network to predict and obtain the second electricity load of the target user in the future time period.
It should be noted that, the first convolutional neural network and the second convolutional neural network in this embodiment may be time prediction models, that is, input data of a historical period of time into the models may predict the power load of the next time node.
S140: and calculating the final electric load of the target user in a future time period according to the first electric load and the second electric load.
In an alternative embodiment provided in the present embodiment, calculating the final electric load of the target user in the future time period according to the first electric load and the second electric load includes: and carrying out weighted calculation on the first electric load and the second electric load to obtain the final electric load of the target user in a future time period.
In another alternative embodiment provided in the present embodiment, calculating the final electric load of the target user in the future time period according to the first electric load and the second electric load includes: and carrying out average calculation on the first electric load and the second electric load to obtain the final electric load of the target user in a future time period.
In this embodiment, the first convolutional neural network may process single-channel sequence data, while the second convolutional neural network may process multi-channel data. In electrical load prediction, multiple features often exist for multiple channel input. By using the first convolutional neural network and the second convolutional neural network in a mixing way, the characteristic input of multiple channels can be processed at the same time, and the information interaction among multiple characteristics is fully utilized, so that the accuracy of electricity load prediction is improved.
In the embodiment, the first convolutional neural network and the second convolutional neural network are mixed for multi-feature power consumption load prediction, so that the expression capability of different types of features can be fully utilized, and the relevance among different features is captured, so that the accuracy and the stability of power consumption load prediction are improved. Meanwhile, the flexibility of the network structures of the first convolutional neural network and the second convolutional neural network and the processing capacity of the multichannel input also provide convenience for optimizing and applying the model.
According to the electricity load prediction method, firstly, historical electricity load data and historical weather data of a preset time period of a target user are obtained, then the historical electricity load data of the preset time period are input into a first convolution neural network to be predicted to obtain first electricity load of the target user in a future time period, the historical electricity load data of the preset time period and the historical weather data are input into a second convolution neural network to be predicted to obtain second electricity load of the target user in the future time period, and finally final electricity load of the target user in the future time period is obtained through calculation according to the first electricity load and the second electricity load. Compared with the prior art that the model excessively depends on certain characteristics, the method and the device have the advantages that based on the combined action of the first convolutional neural network and the second convolutional neural network, the final power load of the user in a future time period is obtained, and the first convolutional neural network and the second convolutional neural network in the method and the device do not depend on a specific characteristic, so that the power load prediction accuracy of the user can be improved.
Fig. 2 is a training flowchart of a convolutional neural network provided in an embodiment of the present application, where the method may include the following steps:
s210: and acquiring electricity load sample data and weather sample data of the target time period from the historical data.
It should be noted that, in this embodiment, the duration of the target time period is the same as the duration of the preset time period in fig. 1, if the duration of the target time period is 3 days, the duration of the preset time period is also 3 days, that is, the sample data of the specific duration used in the model training process is also used in the model using process, and the data of the specific duration is also used in the model using process to input into the model, so that the corresponding power load can be predicted by the model.
S220: and preprocessing the electricity load sample data and the weather sample data, wherein the preprocessing at least comprises data cleaning, missing value filling, abnormal value filtering and normalization processing.
In the present embodiment, after the electricity load sample data and the weather sample data of the target period are acquired from the history data, it is necessary to secure the quality and integrity of the electricity load sample data and the weather sample data. Then, preprocessing is performed on the electricity load sample data and the weather sample data, such as data cleaning, missing value filling, abnormal value processing, normalization processing, and the like, which is not particularly limited in this embodiment.
Further, the present embodiment also requires converting the electrical load sample data into a hysteresis feature, i.e., using load sample data at several past moments (i.e., target time periods) as features to capture the timing relationship between the load sample data. Time slices, hours, days of week, etc. are extracted as features to capture the periodic and seasonal variations of the load sample data.
S230: and performing model training according to the preprocessed electricity load sample data, weather sample data and the electricity load of a future time period corresponding to the corresponding target time period to obtain a first convolutional neural network and a second convolutional neural network.
In this embodiment, the first and second convolutional neural networks use convolutional neural networks (Convolutional Neural Network, CNN) to capture local features and spatial correlations in the electrical load sample data using a CNN model. One-dimensional or two-dimensional convolution operation can be adopted, and proper network structure and convolution kernel size can be selected according to data characteristics. A fully connected layer is added after the CNN model to handle the combination and abstract representation of features, increasing the nonlinear fitting capability of the model.
In the embodiment, a sample data set (the sample data set comprises electricity load sample data and weather sample data) is divided into a training set, a verification set and a test set which are respectively used for training, verifying and evaluating a model, specifically, the test set is used for evaluating the model, and an accuracy index of load prediction, such as Root Mean Square Error (RMSE) and average absolute error (MAE), is calculated; the model is evaluated using a test set and an accuracy index of the load prediction, such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), is calculated. The embodiment uses a proper loss function (such as a mean square error) to measure the prediction errors of the first convolutional neural network and the second convolutional neural network, and updates the network parameters of the first convolutional neural network and the second convolutional neural network through a back propagation algorithm. Aiming at the over-fitting problem, the parameter optimization can be carried out by adopting a regularization method (such as L1 or L2 regularization) and batch normalization and other technologies.
Specifically, constructing the first convolutional neural network requires: the structure of the one-dimensional convolutional neural network is defined and comprises an input layer, a convolutional layer, a pooling layer, a full-connection layer and the like. Wherein the input layer accepts one-dimensional sequential type feature data such as hysteresis features and temporal features. The timing information in the sequence data is extracted using an appropriate convolution kernel size, stride, and padding. The addition of the pooling layer can reduce the feature dimension by using a maximum pooling or average pooling operation. The combination and abstract representation of features is performed through the fully connected layers.
Specifically, constructing the second convolutional neural network requires: the structure of the two-dimensional convolutional neural network is defined and comprises an input layer, a convolutional layer, a pooling layer, a full-connection layer and the like. The input layer accepts two-dimensional image type feature data, such as weather data. Spatial correlation in the image data is extracted using an appropriate convolution kernel size, stride, and fill-in. And adding a pooling layer to reduce the feature dimension. The combination and abstract representation of features is performed through the fully connected layers. The second convolutional neural network in the embodiment fuses a plurality of characteristics such as electricity load sample data, weather sample data, time characteristics and the like, and constructs multi-channel input, so that the second convolutional neural network can learn the relevance between the characteristics at the same time.
In the specific implementation, the method can be adjusted and optimized according to the characteristics of data and task requirements. For example, different convolution kernel sizes, network layers and connection modes can be selected for different features, and operations such as proper regularization and batch normalization can be added. In addition, hyper-parametric selection and cross-validation of models are also important steps for model optimization.
In an alternative embodiment provided in the application, model training is performed according to the preprocessed electricity load sample data, weather sample data and the electricity load of a future time period corresponding to the corresponding target time period, so as to obtain a first convolutional neural network and a second convolutional neural network, which includes: taking the preprocessed electricity load sample data as sample data, and taking the electricity load of a future time period corresponding to a target time period as a sample label to perform model training to obtain a first convolutional neural network; and taking the preprocessed electricity load sample data and weather sample data as sample data, and taking the electricity load of a future time period corresponding to the target time period as a sample label to perform model training to obtain the second convolutional neural network.
In this embodiment, model training is performed according to the preprocessed electricity load sample data, weather sample data, and electricity loads of future time periods corresponding to the corresponding target time periods, so as to obtain a first convolutional neural network and a second convolutional neural network. The first convolutional neural network and the second convolutional neural network can fully utilize the expression capability of different types of features and capture the relevance among different features, so that the accuracy of power load prediction can be improved through the first convolutional neural network and the second convolutional neural network.
Fig. 3 is a schematic diagram of an electrical load prediction device according to an embodiment of the present application. As shown in fig. 3, the apparatus includes:
an acquisition module 301, configured to acquire historical electricity load data and historical weather data of a preset period of time of a target user;
a first prediction module 302, configured to first input the historical electrical load data of the preset time period into a first convolutional neural network to predict to obtain a first electrical load of the target user in a future time period;
a second prediction module 303, configured to input the historical electricity load data and the historical weather data of the preset time period into a second convolutional neural network to predict to obtain a second electricity load of the target user in a future time period;
a calculation module 304, configured to calculate a final electric load of the target user in a future time period according to the first electric load and the second electric load.
In some implementations, the apparatus further includes a training module 305 to:
acquiring electricity load sample data and weather sample data of a target time period from the historical data;
preprocessing the electricity load sample data and the weather sample data, wherein the preprocessing at least comprises data cleaning, missing value filling, abnormal value filtering and normalization processing;
and performing model training according to the preprocessed electricity load sample data, weather sample data and the electricity load of a future time period corresponding to the corresponding target time period to obtain the first convolutional neural network and the second convolutional neural network.
In some implementations, the training module 305 is specifically configured to:
taking the preprocessed electricity load sample data as sample data, and taking the electricity load of a future time period corresponding to the target time period as a sample label for model training to obtain the first convolutional neural network;
and taking the preprocessed electricity load sample data and weather sample data as sample data, and taking the electricity load of the future time period corresponding to the target time period as a sample label to perform model training to obtain the second convolutional neural network.
In some implementations, the computing module 304 is specifically configured to:
and carrying out weighted calculation on the first electric load and the second electric load to obtain the final electric load of the target user in a future time period.
In some implementations, the computing module 304 is specifically configured to:
and carrying out average calculation on the first electric load and the second electric load to obtain the final electric load of the target user in a future time period.
In some implementations, the first convolutional neural network and the second convolutional neural network include at least a convolutional layer, a pooling layer, a fully-connected layer.
In some implementations, the historical weather data includes at least: temperature, wind speed, humidity.
It should be understood that the apparatus embodiment and the electrical load prediction method embodiment may correspond to each other, and similar descriptions may refer to the electrical load prediction method embodiment. To avoid repetition, no further description is provided here. Specifically, the apparatus shown in fig. 3 may perform the above embodiment of the electrical load prediction method, and the foregoing and other operations and/or functions of each module in the apparatus are respectively for implementing the corresponding flow in the electrical load prediction method, which are not described herein for brevity.
The apparatus of the embodiments of the present application are described above in terms of functional modules in conjunction with the accompanying drawings. It should be understood that the functional module may be implemented in hardware, or may be implemented by instructions in software, or may be implemented by a combination of hardware and software modules. Specifically, each step of the embodiment of the method for predicting the electrical load in the embodiment of the application may be completed by an integrated logic circuit of hardware in a processor and/or an instruction in a software form, and the steps of the method for predicting the electrical load disclosed in the embodiment of the application may be directly implemented as a hardware decoding processor or be completed by a combination of hardware and software modules in the decoding processor. Alternatively, the software modules may be located in a well-established storage medium in the art such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, and the like. The storage medium is located in the memory, and the processor reads the information in the memory, and the steps in the embodiment of the power load prediction method are completed by combining the hardware of the processor.
The apparatus of the embodiments of the present application are described above in terms of functional modules in conjunction with the accompanying drawings. It should be understood that the functional module may be implemented in hardware, or may be implemented by instructions in software, or may be implemented by a combination of hardware and software modules. Specifically, each step of the embodiment of the method for predicting the electrical load in the embodiment of the application may be completed by an integrated logic circuit of hardware in a processor and/or an instruction in a software form, and the steps of the method for predicting the electrical load disclosed in the embodiment of the application may be directly implemented as a hardware decoding processor or be completed by a combination of hardware and software modules in the decoding processor. Alternatively, the software modules may be located in a well-established storage medium in the art such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, and the like. The storage medium is located in the memory, and the processor reads the information in the memory, and the steps in the embodiment of the power load prediction method are completed by combining the hardware of the processor.
Fig. 4 is a schematic block diagram of an electronic device 400 provided by an embodiment of the present application. As shown in fig. 4, the electronic device 400 may include: a processor 401, a memory 402. The electronic device 400 may also include one or more of a multimedia component 403, an input/output (I/O) interface 404, and a communication component 405.
The processor 401 is configured to control the overall operation of the electronic device 400 to perform all or part of the steps in the power load prediction method described above. The memory 402 is used to store various types of data to support operation at the electronic device 400, which may include, for example, instructions for any application or method operating on the electronic device 400, as well as application-related data, such as contact data, transceived messages, pictures, audio, video, and the like. The Memory 402 may be implemented by any type or combination of volatile or non-volatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM for short), electrically erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM for short), programmable Read-Only Memory (Programmable Read-Only Memory, PROM for short), read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk, or optical disk. The multimedia component 403 may include a screen and an audio component. Wherein the screen may be, for example, a touch screen, the audio component being for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 402 or transmitted through the communication component 405. The audio assembly further comprises at least one speaker for outputting audio signals. The I/O interface 404 provides an interface between the processor 401 and other interface modules, which may be a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 405 is used for wired or wireless communication between the electronic device 400 and other devices. Wireless communication, such as Wi-Fi, bluetooth, near field communication (Near Field Communication, NFC for short), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or one or a combination of more of them, is not limited herein. The corresponding communication component 405 may thus comprise: wi-Fi module, bluetooth module, NFC module, etc.
In an exemplary embodiment, the electronic device 400 may be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASIC), digital signal processors (Digital Signal Processor, abbreviated as DSP), digital signal processing devices (Digital Signal Processing Device, abbreviated as DSPD), programmable logic devices (Programmable Logic Device, abbreviated as PLD), field programmable gate arrays (Field Programmable Gate Array, abbreviated as FPGA), controllers, microcontrollers, microprocessors, or other electronic components for performing the electrical load prediction methods described above.
In another exemplary embodiment, a computer readable storage medium is also provided, comprising program instructions which, when executed by a processor, implement the steps of the above-described electrical load prediction method. For example, the computer readable storage medium may be the memory 402 including program instructions described above, which are executable by the processor 401 of the electronic device 400 to perform the power load prediction method described above.
In another exemplary embodiment, a computer readable storage medium is also provided, comprising program instructions which, when executed by a processor, implement the steps of the above-described electrical load prediction method.
In another exemplary embodiment, a computer program product is also provided, comprising a computer program executable by a programmable apparatus, the computer program having code portions for performing the above-described electrical load prediction method when executed by the programmable apparatus.
In another exemplary embodiment, there is also provided a computer program that causes a computer to execute the electric load prediction method as described above.
It will be appreciated by those skilled in the art that the present invention can be carried out in other embodiments without departing from the spirit or essential characteristics thereof. Accordingly, the above disclosed embodiments are illustrative in all respects, and not exclusive. All changes that come within the scope of the invention or equivalents thereto are intended to be embraced therein.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (10)
1. A method of electrical load prediction, the method comprising:
acquiring historical electricity load data and historical weather data of a target user in a preset time period;
inputting the historical electricity load data of the preset time period into a first convolutional neural network to predict and obtain a first electricity load of the target user in a future time period;
inputting the historical electricity load data of the preset time period into a second convolutional neural network to predict to obtain a second electricity load of the target user in a future time period;
and calculating the final electric load of the target user in a future time period according to the first electric load and the second electric load.
2. The method according to claim 1, wherein the method further comprises:
acquiring electricity load sample data and weather sample data of a target time period from the historical data;
preprocessing the electricity load sample data and the weather sample data, wherein the preprocessing at least comprises data cleaning, missing value filling, abnormal value filtering and normalization processing;
and performing model training according to the preprocessed electricity load sample data, weather sample data and the electricity load of a future time period corresponding to the corresponding target time period to obtain the first convolutional neural network and the second convolutional neural network.
3. The method of claim 2, wherein the model training based on the preprocessed electrical load sample data, the weather sample data, and the electrical load for the future time period corresponding to the target time period, to obtain the first convolutional neural network and the second convolutional neural network, comprises:
taking the preprocessed electricity load sample data as sample data, and taking the electricity load of a future time period corresponding to the target time period as a sample label for model training to obtain the first convolutional neural network;
and taking the preprocessed electricity load sample data and weather sample data as sample data, and taking the electricity load of the future time period corresponding to the target time period as a sample label to perform model training to obtain the second convolutional neural network.
4. A method according to any one of claims 1-3, wherein said calculating a final electrical load of the target user over a future time period from the first electrical load and the second electrical load comprises:
and carrying out weighted calculation on the first electric load and the second electric load to obtain the final electric load of the target user in a future time period.
5. A method according to any one of claims 1-3, wherein said calculating a final electrical load of the target user over a future time period from the first electrical load and the second electrical load comprises:
and carrying out average calculation on the first electric load and the second electric load to obtain the final electric load of the target user in a future time period.
6. A method according to any of claims 2 or 3, wherein the first convolutional neural network and the second convolutional neural network comprise at least a convolutional layer, a pooling layer, a fully-connected layer.
7. A method according to any one of claims 1-3, wherein the historical weather data comprises at least: temperature, wind speed, humidity.
8. An electrical load prediction apparatus, the apparatus comprising:
the acquisition module is used for acquiring historical electricity load data and historical weather data of a preset time period of a target user;
the first prediction module is used for inputting the historical electricity load data of the preset time period into a first convolutional neural network to predict and obtain the first electricity load of the target user in a future time period;
the second prediction module is used for inputting the historical power utilization load data and the historical weather data of the preset time period into a second convolutional neural network to predict and obtain a second power utilization load of the target user in a future time period;
and the calculation module is used for calculating the final electric load of the target user in a future time period according to the first electric load and the second electric load.
9. An electronic device, comprising:
a processor and a memory for storing a computer program, the processor for invoking and running the computer program stored in the memory to perform the electrical load prediction method of any of claims 1-7.
10. A computer-readable storage medium storing a computer program for causing a computer to execute the electrical load prediction method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410018517.7A CN117748498A (en) | 2024-01-04 | 2024-01-04 | Method, device, equipment and storage medium for predicting electric load |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410018517.7A CN117748498A (en) | 2024-01-04 | 2024-01-04 | Method, device, equipment and storage medium for predicting electric load |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117748498A true CN117748498A (en) | 2024-03-22 |
Family
ID=90279659
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410018517.7A Pending CN117748498A (en) | 2024-01-04 | 2024-01-04 | Method, device, equipment and storage medium for predicting electric load |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117748498A (en) |
-
2024
- 2024-01-04 CN CN202410018517.7A patent/CN117748498A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107844837B (en) | Method and system for adjusting and optimizing algorithm parameters aiming at machine learning algorithm | |
| CN110766142A (en) | Model generation method and device | |
| EP3734519A1 (en) | Method for generating universal learned model | |
| CN110413867B (en) | Method and system for content recommendation | |
| CN110166276A (en) | A kind of localization method, device, terminal device and the medium of frame synchronization exception | |
| CN113379564A (en) | Power grid load prediction method and device and terminal equipment | |
| CN115034519A (en) | Method and device for predicting power load, electronic equipment and storage medium | |
| CN111881023A (en) | Software aging prediction method and device based on multi-model comparison | |
| CN116684330A (en) | Traffic prediction method, device, equipment and storage medium based on artificial intelligence | |
| CN114338416B (en) | Space-time multi-index prediction method and device and storage medium | |
| CN117236805B (en) | Power equipment control method, device, electronic equipment and computer readable medium | |
| CN114220479A (en) | Protein structure prediction method, protein structure prediction device and medium | |
| CN113205230A (en) | Data prediction method, device and equipment based on model set and storage medium | |
| CN111415200A (en) | Data processing method and device | |
| CN111507541A (en) | Goods quantity prediction model construction method, goods quantity measurement device and electronic equipment | |
| CN117748498A (en) | Method, device, equipment and storage medium for predicting electric load | |
| CN114676601A (en) | Emission cost calculation method and device, computer equipment and storage medium | |
| CN114242181A (en) | Desert sand concrete strength prediction model training method, device, equipment and medium | |
| CN109766255B (en) | Equipment state analysis method and device, storage medium and electronic equipment | |
| CN116011593B (en) | Method and device for determining energy consumption of network model | |
| CN113093702B (en) | Fault data prediction method and device, electronic equipment and storage medium | |
| WO2024093249A1 (en) | Weather forecast method and apparatus, and related device | |
| CN116954642A (en) | Updating method, device, equipment, medium and program product of recommendation system | |
| CN117743992A (en) | Carbon emission prediction method and device, storage medium and electronic equipment | |
| CN116975590A (en) | Data processing method, apparatus, program product, computer device, and medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |