CN116646923A - Method, device, terminal and storage medium for predicting medium and long term power load - Google Patents
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Abstract
The application provides a method, a device, a terminal and a storage medium for predicting medium and long term of power load. The method comprises the following steps: based on the user portraits of the power users in the target area, predicting the power load of the target area in a prediction period to obtain a first prediction value; predicting the power load of the target area in a prediction period based on the historical power load of the target area to obtain a second predicted value; and fusing the first predicted value and the second predicted value based on a track fusion algorithm to obtain a power load predicted result of the target area. The application can obtain more accurate mid-long term prediction results of the power load.
Description
Technical Field
The present application relates to the field of power load technologies, and in particular, to a method, an apparatus, a terminal, and a storage medium for medium-long term prediction of a power load.
Background
The power load prediction is classified into three major categories, short term, medium term and long term, short term for several minutes and long term for several months or even years. The long-term prediction in the power load is often used for new station operation, power grid capacity increasing and reconstruction, equipment overhaul plan, reservoir optimization scheduling plan, fuel supply plan and the like.
At present, long-term prediction in the power load is generally performed only on the whole level according to the historical power load of the power generation side or the user side, the prediction mode and the prediction data base are single, randomness of the power load of the user side is difficult to adapt, and the accuracy of the long-term prediction in the current power load is low.
Disclosure of Invention
The embodiment of the application provides a method, a device, a terminal and a storage medium for predicting a power load in a medium-long term so as to solve the problem of low accuracy of the power load in the medium-long term prediction.
In a first aspect, an embodiment of the present application provides a method for medium-long term prediction of a power load, including:
based on the user portraits of the power users in the target area, predicting the power load of the target area in a prediction period to obtain a first prediction value;
predicting the power load of the target area in a prediction period based on the historical power load of the target area to obtain a second predicted value;
and fusing the first predicted value and the second predicted value based on a track fusion algorithm to obtain a power load predicted result of the target area.
In one possible implementation, the user representation includes a user behavior pattern, a user age composition, a population number variation probability, and a behavior pattern variation probability.
In one possible implementation, the target area includes at least one prediction partition, each prediction partition corresponding to a user representation;
based on the user portraits of the power users in the target area, predicting the power load of the target area in a prediction period, and obtaining a first predicted value comprises:
for each prediction partition, predicting the power load of the prediction partition in a prediction period based on the user representation of the prediction partition to obtain a partition prediction value of the prediction partition
And adding all the partition predicted values to obtain a first predicted value.
In one possible implementation, predicting the power load of the target area over the prediction period based on the historical power load of the target area, the obtaining the second prediction value includes:
acquiring a trained power load prediction model; the trained power load prediction model is obtained by training the power load prediction model based on the historical power load and the historical power utilization characteristics of the target area;
inputting the electricity utilization characteristics of the target area in the prediction period into a trained power load prediction model to obtain a second predicted value; wherein the electricity usage characteristics include date, weather, and temperature.
In one possible implementation, the power usage characteristics further include distributed power supply parameters.
In one possible implementation, based on a track fusion algorithm, fusing the first predicted value and the second predicted value to obtain the power load predicted result of the target area includes:
predicting a first test set based on a user portrait of a power user in a target area to obtain a first error; wherein the first test set includes a historical power load of the target area;
predicting a second test set based on the historical power load of the target area to obtain a second error; wherein the second test set includes a historical power load of the target area;
determining a weight of the first predicted value and a weight of the second predicted value based on the first error and the second error;
and fusing the first predicted value and the second predicted value based on a weighted Kalman filtering track fusion algorithm, the weight of the first predicted value and the weight of the second predicted value to obtain a power load predicted result of the target area.
In one possible implementation, determining the weights of the first predictor and the weights of the second predictor based on the first error and the second error comprises:
calculating the ratio of the first error to the second error, and taking the ratio as the ratio of the weight of the second predicted value to the weight of the first predicted value;
the sum of the weight of the second predicted value and the weight of the first predicted value is 1, and the weight of the second predicted value and the weight of the first predicted value are solved based on the ratio of the weight of the second predicted value to the weight of the first predicted value and the sum of the weight of the second predicted value and the weight of the first predicted value.
In a second aspect, an embodiment of the present application provides a device for medium-long term prediction of power load, including:
the first prediction module is used for predicting the power load of the target area in a prediction period based on the user portrait of the power user in the target area to obtain a first prediction value;
the second prediction module is used for predicting the power load of the target area in a prediction period based on the historical power load of the target area to obtain a second predicted value;
and the fusion module is used for fusing the first predicted value and the second predicted value based on a track fusion algorithm to obtain a power load predicted result of the target area.
In a third aspect, an embodiment of the present application provides a terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method according to the first aspect or any one of the possible implementations of the first aspect, when the computer program is executed by the processor.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method as described above in the first aspect or any one of the possible implementations of the first aspect.
The power load medium-long term prediction method, the device, the terminal and the storage medium provided by the embodiment of the application have the beneficial effects that:
according to the application, on one hand, through user portraits, the power load of a target area is predicted at a user side level, and the power consumption behavior mode of a power user is considered; on the other hand, the power load is predicted on the whole level of the target area through the historical power load, and the historical power consumption condition of the user side is considered; and finally, fusing the two predicted values through a track fusion algorithm, wherein the obtained power load predicted result not only relates to the power consumption behavior mode of the user, but also does not deviate from the historical power load of the target area, and compared with the power load in the existing mode, the power load predicted result in a long term is more accurate.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of an implementation of a method for mid-long term prediction of power load provided by an embodiment of the present application;
FIG. 2 is a schematic diagram of a power load medium-long term prediction device according to an embodiment of the present application;
fig. 3 is a schematic diagram of a terminal according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the following description will be made by way of specific embodiments with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of an implementation of a method for predicting a mid-long term of a power load according to an embodiment of the present application is shown, and is described in detail as follows:
and step 101, predicting the power load of the target area in a prediction period based on the user portrait of the power user in the target area to obtain a first prediction value.
In this embodiment, the target area is an area in which power load prediction is required, and there is a power consumer in the target area, and the power consumer may be a resident, an enterprise, a public utility, or the like. The consumer representation may be constructed based on the power usage habits of the power consumers in the target area, thereby predicting the power load of the target area in the predicted period based on the power usage habits of the power consumers.
And step 102, predicting the power load of the target area in a prediction period based on the historical power load of the target area to obtain a second predicted value.
In this embodiment, the historical power load of the target area includes a certain rule, and prediction model training such as neural network, random forest and the like can be performed based on the historical power load of the target area, so as to find the power load change rule of the target area, and the power load of the target area in the prediction period is predicted based on the change rule.
And step 103, fusing the first predicted value and the second predicted value based on a track fusion algorithm to obtain a power load predicted result of the target area.
In the present embodiment, the power load prediction is performed only by the power consumption habit of the power consumer or only by the historical power load, and the obtained prediction result may not be accurate. In order to obtain more accurate prediction results as far as possible, in this embodiment, the two prediction values are fused, so that the final prediction result is affected by the two prediction results at the same time, that is, the power load prediction is performed based on the power consumption habit of the user and the overall power load change rule in the target area at the same time.
Because the power load in the prediction period is predicted, the obtained predicted value is a power load sequence of a plurality of time points in the prediction period, the power load of each time point has certain association with the power load of the previous time point, and the data can be fused through a track fusion algorithm, so that the fused power load sequence still maintains the characteristics, and the final power load predicted result is more similar to real data.
In one possible implementation, the user representation includes a user behavior pattern, a user age composition, a population number variation probability, and a behavior pattern variation probability.
In this embodiment, the corresponding power consumption mode may be searched in the preset knowledge graph according to the user behavior mode and the user age composition in the user portrait, so as to determine the power load corresponding to the user portrait. According to the current user age composition and population quantity change probability, the user age composition of the prediction period can be estimated, according to the current user behavior mode and behavior mode change probability, the user behavior mode of the prediction period can be estimated, and finally according to the user behavior mode of the prediction period and the user age composition, the electricity consumption mode of the user can be found, so that the electric load prediction is carried out according to the electricity consumption mode of each user or each group of users.
In one possible implementation, the target area includes at least one prediction partition, each prediction partition corresponding to a user representation;
based on the user portraits of the power users in the target area, predicting the power load of the target area in a prediction period, and obtaining a first predicted value comprises:
for each prediction partition, predicting the power load of the prediction partition in a prediction period based on the user representation of the prediction partition to obtain a partition prediction value of the prediction partition
And adding all the partition predicted values to obtain a first predicted value.
In this embodiment, the target area may be divided into a plurality of prediction partitions according to the specific situation of the target area. For example, the target area is a city area in a city, and the target area may be divided into a plurality of prediction partitions according to a division standard of a residential area, a business area, an office area, and an industrial area, where each prediction partition corresponds to a respective user portrait. Based on the user representation of each prediction partition, the power load of the prediction partition in the prediction period can be predicted, and finally the power load prediction results of the target area can be obtained by adding the prediction values of the partitions. The prediction partitions are divided according to the region categories, and prediction can be performed by using more proper user portraits aiming at the prediction partitions of different categories, so that the first predicted value is more accurate.
In one possible implementation, predicting the power load of the target area over the prediction period based on the historical power load of the target area, the obtaining the second prediction value includes:
acquiring a trained power load prediction model; the trained power load prediction model is obtained by training the power load prediction model based on the historical power load and the historical power utilization characteristics of the target area;
inputting the electricity utilization characteristics of the target area in the prediction period into a trained power load prediction model to obtain a second predicted value; wherein the electricity usage characteristics include date, weather, and temperature.
In this embodiment, the power load prediction model may be a BP neural network model, a linear regression model, a random forest model, a boltzmann state machine model, or the like, and may be selected according to actual situations. After the power load prediction model is trained by utilizing the historical power load and the historical power utilization characteristic of the target area, the power load prediction model learns the relation between the historical power load and the historical power utilization characteristic in the target area, and the power load can be predicted according to the power utilization characteristic of the target area in a prediction period.
In one possible implementation, the power usage characteristics further include distributed power supply parameters.
In this embodiment, as a large number of distributed power sources are introduced into the power grid, the power load characteristics of the power grid are greatly affected, so that the distributed power source parameter can be used as one of the influencing factors of power load prediction, so that the power load prediction result is more accurate. In particular, the distributed power parameters may include data on the number, type, model, etc. of distributed power sources.
In one possible implementation, based on a track fusion algorithm, fusing the first predicted value and the second predicted value to obtain the power load predicted result of the target area includes:
predicting a first test set based on a user portrait of a power user in a target area to obtain a first error; wherein the first test set includes a historical power load of the target area;
predicting a second test set based on the historical power load of the target area to obtain a second error; wherein the second test set includes a historical power load of the target area;
determining a weight of the first predicted value and a weight of the second predicted value based on the first error and the second error;
and fusing the first predicted value and the second predicted value based on a weighted Kalman filtering track fusion algorithm, the weight of the first predicted value and the weight of the second predicted value to obtain a power load predicted result of the target area.
In this embodiment, the first error may represent an accurate probability of the first predicted value, and the second error may represent an accurate probability of the second predicted value. Kalman filtering can extract useful information from noisy data. For the first predicted value and the second predicted value in this embodiment, both have errors, but it can be determined that the actual value of the power load is distributed near both with a high probability, and at this time, after the two predicted values are fused into one data, the obtained data is closer to the actual value. The process of fusing the first predicted value and the second predicted value based on the weighted Kalman filtering track fusion algorithm can be expressed by the following formula:
P vf (t)＝ 1 (t)S 1 (t)+ 2 () 2 ()
wherein P is vf (t) is the power load prediction result, P 1 (t) is the weight of the first predicted value, S 1 (t) is the first predicted value, P 2 () Is the weight of the second predicted value, S 2 () Is the second predicted value.
In one possible implementation, determining the weights of the first predictor and the weights of the second predictor based on the first error and the second error comprises:
calculating the ratio of the first error to the second error, and taking the ratio as the ratio of the weight of the second predicted value to the weight of the first predicted value;
the sum of the weight of the second predicted value and the weight of the first predicted value is 1, and the weight of the second predicted value and the weight of the first predicted value are solved based on the ratio of the weight of the second predicted value to the weight of the first predicted value and the sum of the weight of the second predicted value and the weight of the first predicted value.
In this embodiment, the smaller the error of a certain predicted value is, the higher the reliability of the predicted value is, and the higher the weight should be, so that the weight distribution can be reasonably performed by taking the ratio of the first error to the second error as the ratio of the weight of the second predicted value to the weight of the first predicted value.
On one hand, the embodiment of the application predicts the power load of the target area on the user side by the user portrait, and considers the power consumption behavior mode of the power user; on the other hand, the power load is predicted on the whole level of the target area through the historical power load, and the historical power consumption condition of the user side is considered; and finally, fusing the two predicted values through a track fusion algorithm, wherein the obtained power load predicted result not only relates to the power consumption behavior mode of the user, but also does not deviate from the historical power load of the target area, and compared with the power load in the existing mode, the power load predicted result in a long term is more accurate.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present application.
The following are device embodiments of the application, for details not described in detail therein, reference may be made to the corresponding method embodiments described above.
Fig. 2 is a schematic structural diagram of a power load medium-long term prediction device according to an embodiment of the present application, and for convenience of explanation, only a portion related to the embodiment of the present application is shown, which is described in detail below:
as shown in fig. 2, the power load medium-long term prediction apparatus 2 includes:
a first prediction module 21, configured to predict a power load of a target area in a prediction period based on a user representation of a power user in the target area, to obtain a first predicted value;
a second prediction module 22, configured to predict, based on the historical power load of the target area, the power load of the target area in a prediction period, to obtain a second predicted value;
and the fusion module 23 is used for fusing the first predicted value and the second predicted value based on a track fusion algorithm to obtain a power load predicted result of the target area.
In one possible implementation, the user representation includes a user behavior pattern, a user age composition, a population number variation probability, and a behavior pattern variation probability.
In one possible implementation, the target area includes at least one prediction partition, each prediction partition corresponding to a user representation;
the first prediction module 21 is specifically configured to:
for each prediction partition, predicting the power load of the prediction partition in a prediction period based on the user representation of the prediction partition to obtain a partition prediction value of the prediction partition
And adding all the partition predicted values to obtain a first predicted value.
In one possible implementation, the second prediction module 22 is specifically configured to:
acquiring a trained power load prediction model; the trained power load prediction model is obtained by training the power load prediction model based on the historical power load and the historical power utilization characteristics of the target area;
inputting the electricity utilization characteristics of the target area in the prediction period into a trained power load prediction model to obtain a second predicted value; wherein the electricity usage characteristics include date, weather, and temperature.
In one possible implementation, the power usage characteristics further include distributed power supply parameters.
In one possible implementation, the fusion module 23 is specifically configured to:
predicting a first test set based on a user portrait of a power user in a target area to obtain a first error; wherein the first test set includes a historical power load of the target area;
predicting a second test set based on the historical power load of the target area to obtain a second error; wherein the second test set includes a historical power load of the target area;
determining a weight of the first predicted value and a weight of the second predicted value based on the first error and the second error;
and fusing the first predicted value and the second predicted value based on a weighted Kalman filtering track fusion algorithm, the weight of the first predicted value and the weight of the second predicted value to obtain a power load predicted result of the target area.
In one possible implementation, the fusion module 23 is specifically configured to:
calculating the ratio of the first error to the second error, and taking the ratio as the ratio of the weight of the second predicted value to the weight of the first predicted value;
the sum of the weight of the second predicted value and the weight of the first predicted value is 1, and the weight of the second predicted value and the weight of the first predicted value are solved based on the ratio of the weight of the second predicted value to the weight of the first predicted value and the sum of the weight of the second predicted value and the weight of the first predicted value.
On one hand, the embodiment of the application predicts the power load of the target area on the user side by the user portrait, and considers the power consumption behavior mode of the power user; on the other hand, the power load is predicted on the whole level of the target area through the historical power load, and the historical power consumption condition of the user side is considered; and finally, fusing the two predicted values through a track fusion algorithm, wherein the obtained power load predicted result not only relates to the power consumption behavior mode of the user, but also does not deviate from the historical power load of the target area, and compared with the power load in the existing mode, the power load predicted result in a long term is more accurate.
Fig. 3 is a schematic diagram of a terminal according to an embodiment of the present application. As shown in fig. 3, the terminal 3 of this embodiment includes: a processor 30, a memory 31 and a computer program 32 stored in said memory 31 and executable on said processor 30. The processor 30, when executing the computer program 32, implements the steps of the above-described embodiments of the long-term prediction method in each power load, such as steps 101 through 103 shown in fig. 1. Alternatively, the processor 30 may perform the functions of the modules/units of the apparatus embodiments described above, such as the functions of the modules/units 21 to 23 shown in fig. 2, when executing the computer program 32.
Illustratively, the computer program 32 may be partitioned into one or more modules/units that are stored in the memory 31 and executed by the processor 30 to complete the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions for describing the execution of the computer program 32 in the terminal 3. For example, the computer program 32 may be split into modules/units 21 to 23 shown in fig. 2.
The terminal 3 may be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server, etc. The terminal 3 may include, but is not limited to, a processor 30, a memory 31. It will be appreciated by those skilled in the art that fig. 3 is merely an example of the terminal 3 and does not constitute a limitation of the terminal 3, and may include more or less components than illustrated, or may combine certain components, or different components, e.g., the terminal may further include an input-output device, a network access device, a bus, etc.
The processor 30 may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 31 may be an internal storage unit of the terminal 3, such as a hard disk or a memory of the terminal 3. The memory 31 may be an external storage device of the terminal 3, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the terminal 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the terminal 3. The memory 31 is used for storing the computer program as well as other programs and data required by the terminal. The memory 31 may also be used for temporarily storing data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment 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, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal and method may be implemented in other manners. For example, the apparatus/terminal embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or 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 may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network 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 the embodiments 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 modules/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 present application may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of the long-term prediction method embodiment in each power load. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium may include content that is subject to appropriate increases and decreases as required by jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is not included as electrical carrier signals and telecommunication signals.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (10)
1. A method of medium-to-long term prediction of electrical load, comprising:
based on a user portrait of a power user in a target area, predicting the power load of the target area in a prediction period to obtain a first prediction value;
predicting the power load of the target area in a prediction period based on the historical power load of the target area to obtain a second prediction value;
and fusing the first predicted value and the second predicted value based on a track fusion algorithm to obtain a power load predicted result of the target area.
2. The method of claim 1, wherein the user representation includes a user behavior pattern, a user age composition, a population change probability, and a behavior pattern change probability.
3. The method of claim 2, wherein the target area comprises at least one prediction partition, each prediction partition corresponding to a user representation;
the predicting the power load of the target area in the prediction period based on the user portrait of the power user in the target area, and obtaining a first predicted value comprises:
for each prediction partition, predicting the power load of the prediction partition in a prediction period based on the user representation of the prediction partition to obtain a partition prediction value of the prediction partition
And adding all the partition predicted values to obtain a first predicted value.
4. The method of claim 1, wherein predicting the power load of the target area in the prediction period based on the historical power load of the target area, to obtain a second prediction value includes:
acquiring a trained power load prediction model; the trained power load prediction model is obtained by training the power load prediction model based on the historical power load and the historical power utilization characteristics of the target area;
inputting the electricity utilization characteristics of the target area in a prediction period into a trained power load prediction model to obtain a second predicted value; wherein the electricity usage characteristics include date, weather, and temperature.
5. The method of mid-to-long term prediction of power load of claim 4, wherein said power usage characteristics further comprise distributed power supply parameters.
6. The method according to any one of claims 1 to 5, wherein the fusing the first predicted value and the second predicted value based on the track fusion algorithm to obtain the power load prediction result of the target area includes
Predicting a first test set based on a user portrait of the power user in the target area to obtain a first error; wherein the first test set includes a historical power load of the target area;
predicting a second test set based on the historical power load of the target area to obtain a second error; wherein the second test set includes a historical power load of the target area;
determining a weight of the first predicted value and a weight of the second predicted value based on the first error and the second error;
and fusing the first predicted value and the second predicted value based on a weighted Kalman filtering track fusion algorithm, the weight of the first predicted value and the weight of the second predicted value to obtain a power load predicted result of the target area.
7. The method of mid-to-long term prediction of a power load of claim 6, wherein the determining weights of the first predictor and the second predictor based on the first error and the second error comprises:
calculating the ratio of the first error to the second error, and taking the ratio as the ratio of the weight of the second predicted value to the weight of the first predicted value;
and solving the weight of the second predicted value and the weight of the first predicted value based on the ratio of the weight of the second predicted value to the weight of the first predicted value and the sum of the weight of the second predicted value and the weight of the first predicted value, wherein the sum of the weight of the second predicted value and the weight of the first predicted value is 1.
8. An apparatus for medium-to-long term prediction of an electrical load, comprising:
the first prediction module is used for predicting the power load of the target area in a prediction period based on the user portrait of the power user in the target area to obtain a first prediction value;
the second prediction module is used for predicting the power load of the target area in a prediction period based on the historical power load of the target area to obtain a second predicted value;
and the fusion module is used for fusing the first predicted value and the second predicted value based on a track fusion algorithm to obtain a power load predicted result of the target area.
9. A terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of the preceding claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any of the preceding claims 1 to 7.
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| CN202310608906.0A CN116646923A (en) | 2023-05-26 | 2023-05-26 | Method, device, terminal and storage medium for predicting medium and long term power load |
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| CN202310608906.0A CN116646923A (en) | 2023-05-26 | 2023-05-26 | Method, device, terminal and storage medium for predicting medium and long term power load |
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