CN113627489A - Demand-based power consumption prediction method, device, equipment and storage medium - Google Patents
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Abstract
The embodiment of the invention provides a demand-based power consumption prediction method, device, equipment and storage medium. The method comprises the steps of obtaining a plurality of data related to electric energy consumption and electric energy data consumed at a collection point of preset time, obtaining the correlation degree of each data related to electric energy consumption and the electric energy data consumed, screening out the corresponding data related to electric energy consumption, of which the correlation degree does not meet a preset first threshold value, establishing an electric energy consumption prediction model according to the screened data related to electric energy consumption and the electric energy data consumed, and predicting the electric energy consumption according to the established electric energy consumption prediction model. The power utilization requirement of the user can be met.
Description
[ technical field ] A method for producing a semiconductor device
The present invention relates to the field of power supply, and in particular, to a method, an apparatus, a device, and a storage medium for demand-based power consumption prediction.
[ background of the invention ]
With the development of society, people's demand for electric power is also becoming larger and larger. Currently, the method for satisfying the demand of people for electric power is to continuously increase the amount of power supply. But has a certain randomness due to the demand of people for electricity. It may happen that the demand for electricity in a region suddenly increases during a certain period of time. In this case, the demand for electricity by the person may not be satisfied in stages.
[ summary of the invention ]
In view of this, embodiments of the present invention provide a demand-based power consumption prediction method, apparatus, device and storage medium, so as to meet the power demand of a user.
In one aspect, an embodiment of the present invention provides a demand-based power consumption prediction method, including:
acquiring a plurality of data related to electric energy consumption and consumed electric energy data of acquisition points at predetermined time
Acquiring the correlation degree of each data related to the electric energy consumption and the consumed electric energy data;
screening out the corresponding data related to the electric energy consumption, the correlation degree of which does not meet a preset first threshold value;
establishing a power consumption prediction model according to the screened data related to the power consumption and the consumed power data;
and predicting the power consumption according to the established power consumption prediction model.
Optionally, the plurality of data related to power consumption includes time, temperature, region, population density of region, commercialization degree, and industrialization degree.
Optionally, the obtaining of the correlation between each data related to power consumption and the power consumption data specifically includes:
and calculating the correlation degree of each data related to the electric energy consumption and the consumed electric energy data by adopting a preset cosine correlation degree calculation formula.
Optionally, establishing a power consumption prediction model according to the filtered data related to the power consumption and the power consumption data, specifically including:
training a preset convolutional neural network model according to the screened data related to the power consumption and the power consumption data;
stopping training when the difference value between the training value and the predicted value obtained by the power consumption prediction model meets a preset second preset threshold value;
and taking the convolutional neural network model after the training is stopped as a power consumption prediction model.
Optionally, the predetermined time comprises a plurality of past time periods having a periodicity.
In another aspect, an embodiment of the present invention provides a demand-based power consumption prediction apparatus, where the apparatus includes:
the acquisition module is used for acquiring a plurality of data related to electric energy consumption and consumed electric energy data of acquisition points at preset time;
the acquisition module is also used for acquiring the correlation degree of each data related to the electric energy consumption and the consumed electric energy data;
the screening module is used for screening out the corresponding data related to the electric energy consumption, the correlation degree of which does not meet a preset first threshold value;
the determining module is used for establishing a power consumption prediction model according to the screened data related to the electric energy consumption and the consumed electric energy data;
and the prediction module is used for predicting the power consumption according to the established power consumption prediction model.
Optionally, the obtaining module is specifically configured to:
and calculating the correlation degree of each data related to the electric energy consumption and the consumed electric energy data by adopting a preset cosine correlation degree calculation formula.
Optionally, the screening module is specifically configured to:
training a preset convolutional neural network model according to the screened data related to the power consumption and the power consumption data;
stopping training when the difference value between the training value and the predicted value obtained by the power consumption prediction model meets a preset second preset threshold value;
and taking the convolutional neural network model after the training is stopped as a power consumption prediction model.
In another aspect, an embodiment of the present invention provides a demand-based power consumption prediction apparatus, where the apparatus includes: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements a demand-based power consumption prediction method as in the first aspect or any one of the optional embodiments of the first aspect.
In another aspect, an embodiment of the present invention provides a storage medium, on which computer program instructions are stored, and the computer program instructions, when executed by a processor, implement the demand-based power consumption prediction method as in the first aspect or any optional embodiment of the first aspect.
According to the technical scheme of the demand-based power consumption prediction method, the multiple data related to power consumption and the power consumption data of the collection point at the preset time are obtained, the correlation degree between each data related to power consumption and the power consumption data is obtained, the corresponding data related to power consumption, the correlation degree of which does not meet a preset first threshold value, is screened out, a power consumption prediction model is built according to the screened data related to power consumption and the power consumption data, and power consumption is predicted according to the built power consumption prediction model. The power utilization requirement of the user can be met.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
FIG. 1 is a flow chart illustrating a method for demand-based power consumption prediction according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating a hardware structure of a demand-based power consumption prediction apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a demand-based power consumption prediction apparatus according to an embodiment of the present invention.
[ detailed description ] embodiments
For better understanding of the technical solutions of the present invention, the following detailed descriptions of the embodiments of the present invention are provided with reference to the accompanying drawings.
It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be understood that the term "and/or" as used herein is merely one type of associative relationship that describes an associated object, meaning that three types of relationships may exist, e.g., A and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
With the development of society, people's demand for electric power is also becoming larger and larger. Currently, the method for satisfying the demand of people for electric power is to continuously increase the amount of power supply. But has a certain randomness due to the demand of people for electricity. It may happen that the demand for electricity in a region suddenly increases during a certain period of time. In this case, the demand for electricity by the person may not be satisfied in stages.
In order to solve the existing technical problem, embodiments of the present invention provide a method, an apparatus, a device, and a storage medium for demand-based power consumption prediction.
First, a method for predicting power consumption based on demand according to an embodiment of the present invention will be described.
A flow chart of a demand-based power consumption prediction method according to an embodiment of the present invention is illustrated, and as shown in fig. 1, the method may include the following steps:
step S101, acquiring a plurality of data related to electric energy consumption and consumed electric energy data of acquisition points at preset time.
In some embodiments, the plurality of data related to power consumption includes time, temperature, region population density, commercialization level, and industrialization level.
Specifically, the factors of the power consumption of the image are various, and may include seasonal factors, population density factors, and population preference factors of the location. On the basis, the data influencing the electric energy consumption comprise time, temperature, regions, region population density, commercialization degree and industrialization degree.
In some embodiments, the predetermined time comprises a plurality of time periods with a periodicity in the past.
Specifically, the data collection may be performed in a plurality of past time periods having periodicity, wherein the plurality of past time periods having periodicity includes days, weeks, months, and the like.
And S102, acquiring the correlation degree of each data related to the electric energy consumption and the consumed electric energy data.
In some embodiments, obtaining the correlation between each data related to power consumption and the power consumption data specifically includes:
and calculating the correlation degree of each data related to the electric energy consumption and the consumed electric energy data by adopting a preset cosine correlation degree calculation formula.
The cosine correlation calculation formula may include:
where cos θ is the correlation obtained by calculation, a is data related to power consumption, and B is power consumption data.
In some embodiments, the correlation of the consumed power data with the respective related data affecting the power consumption may be obtained by a cosine calculation formula.
And step S103, screening out the corresponding data related to the electric energy consumption, of which the correlation degree does not meet a preset first threshold value.
In some embodiments, the first threshold is a dynamic threshold, wherein the dynamic threshold comprises an average of all correlations to obtain correlated data affecting electrical energy consumption.
Because the acquired first threshold is a dynamic threshold, the value related to the consumed electric energy data can be acquired more accurately, and the influence of the change of the related data affecting the electric energy consumption on the first threshold can be avoided.
And step S104, establishing a power consumption prediction model according to the screened data related to the power consumption and the consumed power data.
In some embodiments, establishing a power consumption prediction model according to the filtered data related to the power consumption and the power consumption data, specifically includes:
and training a preset convolutional neural network model according to the screened data related to the power consumption and the power consumption data, stopping training under the condition that the difference value between the training value and the predicted value obtained by the power consumption prediction model meets a preset second preset threshold value, and taking the convolutional neural network model after the training is stopped as the power consumption prediction model.
In some embodiments, the evaluation data and the target first-level index, the target second-level index and the target third-level index are used for training the convolutional neural network model, when the difference value between the training value and the evaluation data meets a preset threshold value, the training is stopped, and the model after the training is stopped is used as an enterprise evaluation model.
As a specific example, in the convolutional neural network, the network input layer is determined according to the filtered data related to the influence on the power consumption. The output layer is determined by preset consumed power data. The convolutional neural network is composed of 2 convolutional layers, one pooling layer and one full-connection layer, wherein the convolutional layer convolutional core is of a 10 x 10 structure, and the pooling layer is of a 2 x 2 structure.
The activation function may include both Relu and Softmax. The loss function may comprise a binary cross sensitive poor entropy function, and the modeling optimizer selects rmsoprop.
The second preset threshold value can be set by self.
In some embodiments, the predicted value of the consumed power data obtained by the convolutional neural network can be obtained by inputting power consumption related data into the convolutional neural network model. And then comparing the predicted value and the actual value of the consumed electric energy data, and stopping training under the condition that the difference value between the predicted value and the actual value of the consumed electric energy data meets a second preset threshold value. And obtaining a power consumption prediction model.
In some embodiments, the obtained power consumption prediction model is a power consumption prediction model of an acquisition point, and one cell may be used as one acquisition point, and then a predicted value of the power consumption of an area is obtained by predicting a plurality of cells.
The above embodiment of the demand-based power consumption prediction method provided by the embodiment of the application obtains a plurality of pieces of data related to power consumption and consumed power data at a collection point of a predetermined time, obtains a correlation degree between each piece of data related to power consumption and the consumed power data, screens out corresponding data related to power consumption, the correlation degree of which does not satisfy a preset first threshold value, establishes a power consumption prediction model according to the screened data related to power consumption and the consumed power data, and predicts power consumption according to the established power consumption prediction model. The power utilization requirement of the user can be met.
Based on the same inventive concept, the application also provides a power consumption prediction device based on the demand.
Fig. 2 is a schematic diagram illustrating a hardware structure of a demand-based power consumption prediction apparatus according to an embodiment of the present invention, where the apparatus shown in fig. 2 includes:
the obtaining module 201 is configured to obtain a plurality of data related to power consumption and power consumption data at a collection point at a predetermined time;
the obtaining module 201 is further configured to obtain a correlation between each data related to power consumption and the consumed power data;
the screening module 202 is configured to screen out data related to power consumption corresponding to the correlation degree that does not satisfy the preset first threshold;
the determining module 203 is configured to establish a power consumption prediction model according to the screened data related to the power consumption and the consumed power data;
the prediction module 204 is configured to predict the power consumption according to the established power consumption prediction model.
In some embodiments, the obtaining module 201 is specifically configured to:
and calculating the correlation degree of each data related to the electric energy consumption and the consumed electric energy data by adopting a preset cosine correlation degree calculation formula.
In some embodiments, the sifting module 202 is specifically configured to:
and training a preset convolutional neural network model according to the screened data related to the power consumption and the power consumption data, stopping training under the condition that the difference value between the training value and the predicted value obtained by the power consumption prediction model meets a preset second preset threshold value, and taking the convolutional neural network model after the training is stopped as the power consumption prediction model.
In some embodiments, the plurality of data related to power consumption includes time, temperature, region population density, commercialization level, and industrialization level.
In some embodiments, the predetermined time comprises a plurality of time periods with a periodicity in the past.
The above embodiment of the demand-based power consumption prediction apparatus provided in this embodiment of the present application obtains a plurality of data related to power consumption and power consumption data at a collection point of a predetermined time, obtains a correlation between each data related to power consumption and power consumption data, screens out data related to power consumption corresponding to the correlation that does not satisfy a preset first threshold, establishes a power consumption prediction model according to the screened data related to power consumption and the power consumption data, and predicts power consumption according to the established power consumption prediction model. The power utilization requirement of the user can be met.
Based on the same inventive concept, the application also provides a power consumption prediction device based on the demand.
Fig. 3 is a schematic diagram illustrating a hardware structure of a demand-based power consumption prediction apparatus according to an embodiment of the present invention.
The demand based power consumption prediction device may comprise a processor 301 and a memory 302 in which computer program instructions are stored.
Specifically, the processor 301 may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement the embodiments of the present invention.
In one example, the Memory 302 may be a Read Only Memory (ROM). In one example, the ROM may be mask programmed ROM, programmable ROM (prom), erasable prom (eprom), electrically erasable prom (eeprom), electrically rewritable ROM (earom), or flash memory, or a combination of two or more of these.
The memory 302 may include Read Only Memory (ROM), Random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors), it is operable to perform operations described with reference to the methods according to an aspect of the present disclosure.
The processor 301 reads and executes the computer program instructions stored in the memory 302 to implement the methods/steps S101 to S103 in the embodiment shown in fig. 1, and achieve the corresponding technical effects achieved by the embodiment shown in fig. 1 executing the methods/steps thereof, which are not described herein again for brevity.
In one example, the demand-based power consumption prediction device may also include a communication interface 303 and a bus 310. As shown in fig. 3, the processor 301, the memory 302, and the communication interface 303 are connected via a bus 310 to complete communication therebetween.
The communication interface 303 is mainly used for implementing communication between modules, apparatuses, units and/or devices in the embodiment of the present invention.
The demand-based power consumption prediction apparatus may be based on a demand-based power consumption prediction method, thereby implementing the demand-based power consumption prediction method and apparatus described in conjunction with fig. 1 and 2.
In addition, in combination with the demand-based power consumption prediction method in the above embodiments, the embodiments of the present invention may be implemented by providing a storage medium. The storage medium has stored thereon computer program instructions. The computer program instructions, when executed by a processor, implement any of the above-described embodiments of the demand-based power consumption prediction method.
It is to be understood that the invention is not limited to the specific arrangements and instrumentality described above and shown in the drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present invention are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications and additions or change the order between the steps after comprehending the spirit of the present invention.
The functional blocks shown in the above structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic Circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the invention are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, Erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, Radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.
It should also be noted that the exemplary embodiments mentioned in this patent describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.
Aspects of the present disclosure are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of 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, 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, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware for performing the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The present invention is not limited to the above preferred embodiments, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A demand-based power consumption prediction method, the method comprising:
acquiring a plurality of data related to electric energy consumption and consumed electric energy data of a collection point at a preset time;
acquiring the correlation degree of each data related to the electric energy consumption and the consumed electric energy data;
screening out the corresponding data related to the electric energy consumption, of which the correlation does not meet a preset first threshold value;
establishing a power consumption prediction model according to the screened data related to the power consumption and the consumed power data;
and predicting the power consumption according to the established power consumption prediction model.
2. The method of claim 1, wherein the plurality of data related to power consumption comprises time, temperature, region population density, degree of commercialization, and degree of industrialization.
3. The method according to claim 1, wherein the obtaining of the correlation between each of the data related to power consumption and the power data consumed specifically comprises:
and calculating the correlation degree of each data related to the electric energy consumption and the consumed electric energy data by adopting a preset cosine correlation degree calculation formula.
4. The method according to claim 1, wherein the building of the power consumption prediction model according to the screened data related to the power consumption and the power consumption data includes:
training a preset convolutional neural network model according to the screened data related to the electric energy consumption and the consumed electric energy data;
stopping training when the difference value between the training value and the predicted value obtained by the power consumption prediction model meets a preset second preset threshold value;
and taking the convolutional neural network model after the training is stopped as a power consumption prediction model.
5. The method of claim 1, wherein the predetermined time comprises a plurality of past time periods having a periodicity.
6. A demand-based power consumption prediction apparatus, the apparatus comprising:
the acquisition module is used for acquiring a plurality of data related to electric energy consumption and consumed electric energy data of acquisition points at preset time;
the acquisition module is further used for acquiring the correlation degree of each piece of data related to the electric energy consumption and the consumed electric energy data;
the screening module is used for screening out the corresponding data related to the electric energy consumption, of which the correlation degree does not meet a preset first threshold value;
the determining module is used for establishing a power consumption prediction model according to the screened data related to the electric energy consumption and the consumed electric energy data;
and the prediction module is used for predicting the power consumption according to the established power consumption prediction model.
7. The apparatus of claim 6, wherein the obtaining module is specifically configured to:
and calculating the correlation degree of each data related to the electric energy consumption and the consumed electric energy data by adopting a preset cosine correlation degree calculation formula.
8. The device according to claim 6, characterized in that the screening module is particularly adapted to:
training a preset convolutional neural network model according to the screened data related to the electric energy consumption and the consumed electric energy data;
stopping training when the difference value between the training value and the predicted value obtained by the power consumption prediction model meets a preset second preset threshold value;
and taking the convolutional neural network model after the training is stopped as a power consumption prediction model.
9. A demand-based power consumption prediction apparatus, characterized in that the apparatus comprises: a processor, and a memory storing computer program instructions; the processor reads and executes the computer program instructions to implement the demand-based power consumption prediction method of any one of claims 1 to 5.
10. A storage medium having stored thereon computer program instructions which, when executed by a processor, implement a demand-based power consumption prediction method according to any one of claims 1 to 5.
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