CN113592528A - Baseline load estimation method and device and terminal equipment - Google Patents
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Abstract
The invention provides a baseline load estimation method, a baseline load estimation device and terminal equipment, wherein the baseline load estimation method is applied to the technical field of load prediction and comprises the following steps: dividing each user according to the time of each user participating in demand response in a target area to obtain at least one user cluster, and distributing corresponding predictability for each user cluster; updating the predictability corresponding to each user cluster based on the historical load data corresponding to each user cluster; and determining the baseline load of the target area by performing different prediction processing on the historical load data corresponding to the user clusters with different predictability. The baseline load estimation method, the baseline load estimation device and the terminal equipment can improve the accuracy and efficiency of baseline load estimation.
Description
Technical Field
The invention belongs to the technical field of load prediction, and particularly relates to a baseline load estimation method, a baseline load estimation device and terminal equipment.
Background
With the rapid development and mutual fusion of the power market and distributed power generation, the load mode and the power utilization behavior of users become increasingly complex, and many new complex scenes emerge continuously to have deep influence on the baseline load estimation. Distributed photovoltaics have seen explosive growth, with more and more users installing distributed photovoltaics. Most small distributed photovoltaic systems are installed after the meter, the customer meter measures only the net load (i.e., customer load minus photovoltaic contribution), while the photovoltaic contribution is "invisible" to the system operator and the load aggregator. This unobservability introduces additional uncertainty into the baseline load estimation of the photovoltaic users, while the user load also has random fluctuation characteristics due to the randomness and intermittency of the photovoltaic output, greatly increasing the difficulty of baseline load estimation.
Therefore, how to improve the accuracy of the baseline load estimation becomes an urgent problem to be solved by those skilled in the art.
Disclosure of Invention
The invention aims to provide a baseline load estimation method, a baseline load estimation device and terminal equipment so as to improve the accuracy of baseline load estimation.
In a first aspect of the embodiments of the present invention, a baseline load estimation method is provided, including:
dividing each user according to the time of each user participating in demand response in a target area to obtain at least one user cluster, and distributing corresponding predictability for each user cluster;
updating the predictability corresponding to each user cluster based on the historical load data corresponding to each user cluster; recording the updated user cluster with the predictability degree larger than the first predictive value as a first user cluster, recording the updated user cluster with the predictability degree not larger than the first predictive value as a second user cluster, and acquiring first historical load data corresponding to the first user cluster and second historical load data corresponding to the second user cluster;
inputting the first historical load data into a preset curve fitting model, predicting to obtain a first baseline load, inputting the second historical load data into a preset neural network model, predicting to obtain a second baseline load data, and determining the baseline load of the target area based on the first baseline load and the second baseline load.
In a second aspect of the embodiments of the present invention, there is provided a baseline load estimation apparatus, including:
the prediction degree distribution module is used for dividing each user according to the time of each user participating in demand response in the target area to obtain at least one user cluster and distributing corresponding prediction degree for each user cluster;
the data acquisition module is used for updating the predictability corresponding to each user cluster based on the historical load data corresponding to each user cluster; recording the updated user cluster with the predictability degree larger than the first predictive value as a first user cluster, recording the updated user cluster with the predictability degree not larger than the first predictive value as a second user cluster, and acquiring first historical load data corresponding to the first user cluster and second historical load data corresponding to the second user cluster;
the load prediction module is used for inputting the first historical load data into a preset curve fitting model, predicting to obtain a first baseline load, inputting the second historical load data into a preset neural network model, predicting to obtain second baseline load data, and determining the baseline load of the target area based on the first baseline load and the second baseline load.
In a third aspect of the embodiments of the present invention, there is provided a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the baseline load estimation method when executing the computer program.
In a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, which stores a computer program, and the computer program, when executed by a processor, implements the steps of the baseline load estimation method described above.
The baseline load estimation method, the baseline load estimation device and the terminal equipment provided by the embodiment of the invention have the beneficial effects that:
according to the method, firstly, users are divided according to the time of each user participating in demand response to obtain a plurality of user clusters, the predictability of each user cluster is distributed, the predictability of the user clusters is updated based on historical load data, and finally, the load estimation is carried out on the user clusters with different predictability through different prediction models. The load estimation method is different from the scheme that the load estimation is carried out on all users by adopting the same model without considering the predictability of the user load in the prior art, and the predictability of the user load is considered, so that the prediction precision is effectively improved. On the basis, for a user cluster with higher predictability, the load estimation method directly adopts the curve fitting model to carry out load estimation, and compared with the scheme of training the neural network by adopting a large amount of data in the prior art, the load estimation method can effectively reduce the operation amount and improve the load estimation efficiency.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions 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 without creative efforts.
FIG. 1 is a schematic flow chart of a baseline load estimation method according to an embodiment of the invention;
fig. 2 is a block diagram of a baseline load estimation apparatus according to an embodiment of the present invention;
fig. 3 is a schematic block diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic flow chart of a baseline load estimation method according to an embodiment of the present invention, the method including:
s101: and dividing each user according to the time of each user participating in demand response in the target area to obtain at least one user cluster, and distributing corresponding predictability for each user cluster.
In this embodiment, each user may be divided according to the time of each user participating in the demand response, for example, the time of each user participating in the demand response and the time of each user belonging to a preset time range may be regarded as a user cluster. Specifically, a user who participates in the demand response time less than 0.5 year may be regarded as a user cluster, a user who participates in the demand response time more than 0.5 year and less than 1 year may be regarded as a user cluster, …, and so on.
In this embodiment, the predictability may be allocated to the user cluster according to the time for each user in the user cluster to participate in the demand response, wherein the longer the time for each user in the user cluster to participate in the demand response, the greater the predictability corresponding to the user cluster is.
S102: and updating the predictability corresponding to each user cluster based on the historical load data corresponding to each user cluster. And marking the updated user cluster with the predictability degree larger than the first predictive value as a first user cluster, marking the updated user cluster with the predictability degree not larger than the first predictive value as a second user cluster, and acquiring first historical load data corresponding to the first user cluster and second historical load data corresponding to the second user cluster.
S103: inputting the first historical load data into a preset curve fitting model, predicting to obtain a first baseline load, inputting the second historical load data into a preset neural network model, predicting to obtain a second baseline load data, and determining the baseline load of the target area based on the first baseline load and the second baseline load.
In this embodiment, the first historical load data may be divided into the first historical data and the second historical data according to time, and a curve fitting model determined by a weight coefficient may be obtained by training from the first historical data based on a least square fitting method. After the curve fitting model is trained, the second historical data can be input into the trained curve fitting model, and the first baseline load is obtained through prediction. Similarly, the second historical load data can be divided into third historical data and fourth historical data according to time, and the weight coefficient determination neural network model can be obtained by training based on the third historical data. After the training of the neural network model is completed, the fourth historical data can be input into the trained neural network model to obtain a second baseline load. And finally, determining the final estimated baseline load of the target area according to the first baseline load and the second baseline load.
According to the embodiment of the invention, the users are divided according to the time of each user participating in demand response to obtain a plurality of user clusters, the predictability is distributed to each user cluster, the predictability of the user clusters is updated based on historical load data, and finally the load estimation is carried out on the user clusters with different predictability through different prediction models. Compared with the scheme that load estimation is carried out on all users by adopting the same model without considering the predictability of the user load in the prior art, the embodiment of the invention considers the predictability of the user load, thereby effectively improving the prediction precision. On the basis, for a user cluster with high predictability, the load estimation is carried out by directly adopting the curve fitting model, and compared with a scheme of training a neural network by adopting a large amount of data in the prior art, the load estimation method can effectively reduce the operation amount and improve the load estimation efficiency.
Optionally, as a specific implementation manner of the baseline load estimation method provided in the embodiment of the present invention, allocating corresponding predictability to each user cluster includes:
and determining the average time of the participation demand response corresponding to each user cluster.
And allocating corresponding predictability for each user cluster based on the average time.
In this embodiment, the longer the average time is, the greater the degree of predictability corresponding to each user cluster is.
Optionally, as a specific implementation manner of the baseline load estimation method provided in the embodiment of the present invention, updating the predictability corresponding to each user cluster based on the historical load data corresponding to each user cluster includes:
and marking the user cluster with the predictability degree larger than the second preset value as a third user cluster, acquiring third history load data corresponding to the third user cluster, and updating the predictability degree corresponding to the third user cluster based on the third history load data.
In this embodiment, if the predictability of a certain user cluster is not greater than the second preset value, the predictability of the user cluster is not updated.
Optionally, as a specific implementation manner of the baseline load estimation method provided in the embodiment of the present invention, the updating the degree of predictability corresponding to the third user cluster based on the third historical load data includes:
the third history load data is divided into training data and verification data based on the chronological order.
And training based on the training data to obtain a load prediction model, and determining the prediction accuracy of the load prediction model based on the verification data.
And updating the corresponding predictability of the third user cluster according to the prediction accuracy.
In this embodiment, the lower the prediction accuracy, the lower the degree of predictability corresponding to the third user cluster is, and the specific quantitative relationship between the two may be determined according to an actual test.
In the above embodiments, there is more than one first user cluster, second user cluster, and third user cluster, and as long as corresponding conditions are met, the first user cluster, second user cluster, and third user cluster may be referred to as a first user cluster, a second user cluster, and a third user cluster. And finally, synthesizing the first baseline load or the second baseline load of all the user clusters to obtain the finally estimated baseline load of the target area.
Optionally, as a specific implementation manner of the baseline load estimation method provided by the embodiment of the present invention, the determining the baseline load of the target area based on the first baseline load and the second baseline load includes:
the sum of the first baseline load and the second baseline load is taken as the baseline load of the target zone.
Or determining the base line load range of the target area according to the preset error range, the first base line load and the second base line load.
In this embodiment, the baseline load finally estimated in the target area may be a specific value or a range.
Optionally, as a specific implementation manner of the baseline load estimation method provided in the embodiment of the present invention, the method for determining the preset error range includes:
and acquiring the maximum load and the minimum load in the historical load data corresponding to each user cluster.
An error range is determined based on the maximum load and the minimum load.
In this embodiment, the fluctuation range of the load may be determined according to the maximum load and the minimum load in the historical load data, and the error range may be determined.
Fig. 2 is a block diagram of a baseline load estimation apparatus according to an embodiment of the present invention, corresponding to the baseline load estimation method of the foregoing embodiment. For convenience of explanation, only portions related to the embodiments of the present invention are shown. Referring to fig. 2, the baseline load estimation apparatus 20 includes: a prediction degree distribution module 21, a data acquisition module 22 and a load prediction module 23.
The prediction degree allocation module 21 is configured to divide each user according to the time for each user in the target area to participate in the demand response, obtain at least one user cluster, and allocate a corresponding prediction degree to each user cluster.
And the data acquisition module 22 is configured to update the predictability corresponding to each user cluster based on the historical load data corresponding to each user cluster. And marking the updated user cluster with the predictability degree larger than the first predictive value as a first user cluster, marking the updated user cluster with the predictability degree not larger than the first predictive value as a second user cluster, and acquiring first historical load data corresponding to the first user cluster and second historical load data corresponding to the second user cluster.
The load prediction module 23 is configured to input the first historical load data into a preset curve fitting model, predict to obtain a first baseline load, input the second historical load data into a preset neural network model, predict to obtain second baseline load data, and determine a baseline load of the target area based on the first baseline load and the second baseline load.
Optionally, as a specific implementation manner of the baseline load estimation apparatus provided in the embodiment of the present invention, the prediction degree allocation module 21 is specifically configured to:
and determining the average time of the participation demand response corresponding to each user cluster.
And allocating corresponding predictability for each user cluster based on the average time.
Optionally, as a specific implementation manner of the baseline load estimation apparatus provided in the embodiment of the present invention, the data obtaining module 22 is specifically configured to:
and marking the user cluster with the predictability degree larger than the second preset value as a third user cluster, acquiring third history load data corresponding to the third user cluster, and updating the predictability degree corresponding to the third user cluster based on the third history load data.
Optionally, as a specific implementation manner of the baseline load estimation apparatus provided in the embodiment of the present invention, the data obtaining module 22 is specifically configured to:
the third history load data is divided into training data and verification data based on the chronological order.
And training based on the training data to obtain a load prediction model, and determining the prediction accuracy of the load prediction model based on the verification data.
And updating the corresponding predictability of the third user cluster according to the prediction accuracy.
Optionally, as a specific implementation manner of the baseline load estimation apparatus provided in the embodiment of the present invention, the load prediction module 23 is specifically configured to:
the sum of the first baseline load and the second baseline load is taken as the baseline load of the target zone.
Or determining the base line load range of the target area according to the preset error range, the first base line load and the second base line load.
Optionally, as a specific implementation manner of the baseline load estimation apparatus provided in the embodiment of the present invention, the load prediction module 23 is further configured to:
and acquiring the maximum load and the minimum load in the historical load data corresponding to each user cluster.
An error range is determined based on the maximum load and the minimum load.
Referring to fig. 3, fig. 3 is a schematic block diagram of a terminal device according to an embodiment of the present invention. The terminal 300 in the present embodiment as shown in fig. 3 may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processor 301, the input device 302, the output device 303, and the memory 304 are in communication with each other via a communication bus 305. The memory 304 is used to store a computer program comprising program instructions. Processor 301 is operative to execute program instructions stored in memory 304. Wherein the processor 301 is configured to call program instructions to perform the following functions of operating the modules/units in the above-described device embodiments, such as the functions of the modules 21 to 23 shown in fig. 2.
It should be understood that, in the embodiment of the present invention, the Processor 301 may be a Central Processing Unit (CPU), and the Processor may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The input device 302 may include a touch pad, a fingerprint sensor (for collecting fingerprint information of a user and direction information of the fingerprint), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.
The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store device type information.
In a specific implementation, the processor 301, the input device 302, and the output device 303 described in this embodiment of the present invention may execute the implementation manners described in the first embodiment and the second embodiment of the baseline load estimation method provided in this embodiment of the present invention, and may also execute the implementation manners of the terminal described in this embodiment of the present invention, which is not described herein again.
In another embodiment of the present invention, a computer-readable storage medium is provided, in which a computer program is stored, where the computer program includes program instructions, and the program instructions, when executed by a processor, implement all or part of the processes in the method of the above embodiments, and may also be implemented by a computer program instructing associated hardware, and the computer program may be stored in a computer-readable storage medium, and the computer program, when executed by a processor, may implement the steps of the above methods embodiments. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like. It should be noted that the computer readable medium may include any suitable increase or decrease as required by legislation and patent practice in the jurisdiction, for example, in some jurisdictions, computer readable media may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The computer readable storage medium may be an internal storage unit of the terminal of any of the foregoing embodiments, for example, a hard disk or a memory of the terminal. The computer readable storage medium may also be an external storage device of the terminal, such as a plug-in hard disk provided on the terminal, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the computer-readable storage medium may also include both an internal storage unit and an external storage device of the terminal. The computer-readable storage medium is used for storing a computer program and other programs and data required by the terminal. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. 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 invention.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the terminal and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed terminal and method can be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces or units, and may also be an electrical, mechanical or other form of connection.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A baseline load estimation method, comprising:
dividing each user according to the time of each user participating in demand response in a target area to obtain at least one user cluster, and distributing corresponding predictability for each user cluster;
updating the predictability corresponding to each user cluster based on the historical load data corresponding to each user cluster; recording the updated user cluster with the predictability degree larger than the first predictive value as a first user cluster, recording the updated user cluster with the predictability degree not larger than the first predictive value as a second user cluster, and acquiring first historical load data corresponding to the first user cluster and second historical load data corresponding to the second user cluster;
inputting the first historical load data into a preset curve fitting model, predicting to obtain a first baseline load, inputting the second historical load data into a preset neural network model, predicting to obtain a second baseline load data, and determining the baseline load of the target area based on the first baseline load and the second baseline load.
2. The baseline load estimation method of claim 1, wherein said assigning a corresponding degree of predictability for each user cluster comprises:
determining the average time of the participation demand response corresponding to each user cluster;
and allocating corresponding predictability for each user cluster based on the average time.
3. The baseline load estimation method of claim 1, wherein updating the degree of predictability for each user cluster based on historical load data for each user cluster comprises:
and marking the user cluster with the predictability degree larger than the second preset value as a third user cluster, acquiring third history load data corresponding to the third user cluster, and updating the predictability degree corresponding to the third user cluster based on the third history load data.
4. The baseline load estimation method of claim 3, wherein said updating the degree of predictability corresponding to a third cluster of users based on said third historical load data comprises:
dividing the third historical load data into training data and verification data based on a chronological order;
training based on the training data to obtain a load prediction model, and determining the prediction accuracy of the load prediction model based on the verification data;
and updating the predictability corresponding to the third user cluster according to the prediction accuracy.
5. The baseline load estimation method of claim 1, wherein determining the baseline load for the target zone based on the first baseline load and the second baseline load comprises:
setting a sum of the first baseline load and the second baseline load as a baseline load of a target area;
or determining the base line load range of the target area according to a preset error range, the first base line load and the second base line load.
6. The baseline load estimation method of claim 5, wherein the predetermined error range is determined by:
acquiring the maximum load and the minimum load in historical load data corresponding to each user cluster;
an error range is determined based on the maximum load and the minimum load.
7. A baseline load estimation apparatus, comprising:
the prediction degree distribution module is used for dividing each user according to the time of each user participating in demand response in the target area to obtain at least one user cluster and distributing corresponding prediction degree for each user cluster;
the data acquisition module is used for updating the predictability corresponding to each user cluster based on the historical load data corresponding to each user cluster; recording the updated user cluster with the predictability degree larger than the first predictive value as a first user cluster, recording the updated user cluster with the predictability degree not larger than the first predictive value as a second user cluster, and acquiring first historical load data corresponding to the first user cluster and second historical load data corresponding to the second user cluster;
the load prediction module is used for inputting the first historical load data into a preset curve fitting model, predicting to obtain a first baseline load, inputting the second historical load data into a preset neural network model, predicting to obtain second baseline load data, and determining the baseline load of the target area based on the first baseline load and the second baseline load.
8. The baseline load estimation apparatus of claim 7, wherein the prediction allocation module is specifically configured to:
determining the average time of the participation demand response corresponding to each user cluster;
and allocating corresponding predictability for each user cluster based on the average time.
9. A terminal device 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 claims 1 to 6 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
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