CN117422259A - Distributed resource prediction aggregation method, device, equipment and storage medium - Google Patents
Distributed resource prediction aggregation method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a distributed resource prediction aggregation method, a device, equipment and a storage medium. The method comprises the following steps: acquiring a target data acquisition table acquired in advance for a region to be predicted; constructing a distributed energy map model corresponding to distributed energy in the region to be predicted based on the target data acquisition table, and determining historical energy information corresponding to each distributed energy device in the region to be predicted based on the distributed energy map model; aiming at each distributed energy device, determining device prediction power generation data corresponding to the distributed energy device based on historical energy information and a target power generation prediction model obtained through pre-training; and determining target prediction power generation data corresponding to the region to be predicted according to the distributed energy map model and the prediction power generation data of each device, so that accurate prediction of distributed resource prediction aggregation can be realized by combining the power grid system map model.
Description
Technical Field
The present invention relates to the field of energy prediction technologies, and in particular, to a distributed resource prediction aggregation method, device, equipment, and storage medium.
Background
With the release of the "two carbon" goal, countries are strongly pushing the construction of distributed energy sources. When large-scale distributed energy is connected into the power distribution network, the fluctuation, randomness and intermittence of the distributed energy bring great challenges to the operation of the power grid system, so that the safe, stable and reliable operation of the power grid is further enhanced, the construction of new energy is planned better, the distributed power supply is subjected to aggregation management, and the distributed energy and each aggregation node are accurately and comprehensively predicted.
In the related art, the distributed prediction research technology of the power system is mainly based on relational data, and in the development of energy prediction based on graph data, the topological relation of the distributed power sources in the power distribution network is considered less in the existing related research, so that the aggregate power generation capacity of each distributed energy source cannot be determined according to the power generation characteristics of the distributed energy sources and the topological structure of the power grid system, and the problems of poor timeliness of energy prediction, poor accuracy and the like are caused.
Disclosure of Invention
The invention provides a method, a device, equipment and a storage medium for predicting and aggregating distributed resources, which are used for realizing accurate prediction of the predicting and aggregating the distributed resources by combining a power grid system graph model.
According to one aspect of the invention, a distributed resource prediction aggregation method is provided. The method comprises the following steps:
acquiring a target data acquisition table acquired in advance for a region to be predicted;
constructing a distributed energy map model corresponding to distributed energy in the region to be predicted based on the target data acquisition table, and determining historical energy information corresponding to each distributed energy device in the region to be predicted based on the distributed energy map model;
aiming at each distributed energy device, determining device prediction power generation data corresponding to the distributed energy device based on the historical energy information and a target power generation prediction model obtained through pre-training;
and determining target prediction power generation data corresponding to the region to be predicted according to the distributed energy map model and the prediction power generation data of each device.
According to another aspect of the present invention, a distributed resource prediction aggregation apparatus is provided. The device comprises:
the target data acquisition table acquisition module is used for acquiring a target data acquisition table acquired in advance for the area to be predicted;
the distributed energy map model construction module is used for constructing a distributed energy map model corresponding to distributed energy in the region to be predicted based on the target data acquisition table, and determining historical energy information corresponding to each distributed energy device in the region to be predicted based on the distributed energy map model;
The equipment prediction power generation data determining module is used for determining equipment prediction power generation data corresponding to the distributed energy equipment based on the historical energy information and a target power generation prediction model obtained through pre-training for each distributed energy equipment;
and the target prediction power generation data determining module is used for determining target prediction power generation data corresponding to the area to be predicted according to the distributed energy map model and the equipment prediction power generation data.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the distributed resource prediction aggregation method of any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the distributed resource prediction aggregation method according to any one of the embodiments of the present invention when executed.
According to the technical scheme, the target data acquisition table which is acquired in advance for the area to be predicted is obtained. Based on the target data acquisition table, a distributed energy map model corresponding to the distributed energy in the area to be predicted is constructed, and based on the distributed energy map model, historical energy information corresponding to each distributed energy device in the area to be predicted can be rapidly inquired and determined, so that the probability of human inquiry errors is reduced, and the efficiency and accuracy of determining the historical energy information are improved. Aiming at each distributed energy device, based on the historical energy information and a target power generation prediction model obtained through pre-training, the device prediction power generation data corresponding to the determined distributed energy device is further improved. And according to the distributed energy map model and the power generation data predicted by each device, performing aggregation calculation upwards based on the topological relation of the distributed energy, and realizing accurate prediction of the distributed resource prediction aggregation.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a distributed resource prediction aggregation method according to a first embodiment of the present invention;
FIG. 2 is a flow chart of a distributed resource prediction aggregation method according to a second embodiment of the present invention;
FIG. 3 is a block diagram of a distributed resource prediction aggregation device according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device implementing a distributed resource prediction aggregation method according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a distributed resource prediction aggregation method according to an embodiment of the present invention, where the method may be performed by a distributed resource prediction aggregation device, and the distributed resource prediction aggregation device may be implemented in hardware and/or software, and the distributed resource prediction aggregation device may be configured in an electronic device. As shown in fig. 1, the method includes:
S101, acquiring a target data acquisition table acquired in advance for a region to be predicted.
The region to be predicted may refer to a region where distributed energy sources are deployed and the amount of power generation of the distributed energy sources to be predicted. The target data acquisition table can refer to various information data tables of the regional power grid system to be predicted. Illustratively, the target data acquisition table includes at least a substation model definition table, a line model definition table, a distribution substation model definition table, and a distributed energy model definition table.
Illustratively, the substation model definition table is shown in table 1:
TABLE 1
Line model definition table, as shown in table 2:
TABLE 2
The configuration station model definition table is shown in table 3:
TABLE 3 Table 3
A distributed energy model definition table, as shown in table 4:
TABLE 4 Table 4
S102, constructing a distributed energy map model corresponding to distributed energy in the region to be predicted based on the target data acquisition table, and determining historical energy information corresponding to each distributed energy device in the region to be predicted based on the distributed energy map model.
It should be noted that the graph model is a data model for representing and describing the graph structure. It is commonly used to represent and analyze complex systems. The graph model may use graph nodes and edges to represent entities and relationships to more intuitively understand and analyze the structure and properties of the system, while the distributed energy graph model may more intuitively understand and analyze the structure and properties of the grid system. The historical energy information may refer to historical parameter information of the distributed energy, for example, the historical energy information may include a subarea to which the distributed energy belongs, historical weather, date, historical power generation time sequence data corresponding to the subarea, and the like. The historical power generation timing data may be 96 power generation data points every 15 minutes for a short period of time over the past 1 day.
Specifically, useful features such as time series features, weather features, load demand features, and the like are extracted from the target data acquisition table. The extracted characteristics can be utilized to construct a graph model, the generated energy of the distributed energy system is taken as a node, the affected factors of the generated energy are taken as edges, and the distributed energy graph model corresponding to the distributed energy in the area to be predicted is constructed. And inquiring historical energy information corresponding to each distributed energy device in the region to be predicted from the distributed energy graph model by utilizing convenient inquiry property and easy analysis property of the distributed energy graph model.
Exemplary, the constructing a distributed energy map model corresponding to the distributed energy in the to-be-predicted area based on the target data acquisition table includes: performing table analysis processing on the target data acquisition table to obtain equipment model sets corresponding to all types of equipment and equipment parameter data corresponding to the equipment model sets; determining the equipment model set as a model node of a distributed energy map model to be constructed aiming at each type of equipment model set, and taking equipment parameter data corresponding to the equipment model set as a model edge of the distributed energy map model to be constructed; and determining the constructed distributed energy map model to be constructed as the distributed energy map model.
The device model set may include a distributed energy device model set, a substation device model set, and a feeder device model set, among others. The device parameter data may refer to parameter data corresponding to a set of device models. The device parameter data may include the number of devices, connected superior devices, device IDs, device levels, and the like.
Specifically, the distributed energy source equipment model set, the transformer substation equipment model set, the distribution station equipment model set and the feeder equipment model set which are obtained through analysis are respectively used as model nodes of a distributed energy source diagram model to be constructed, and meanwhile, the equipment parameter data obtained through analysis are used as model edges of the distributed energy source diagram model to be constructed. And determining the constructed distributed energy map model to be constructed as the distributed energy map model.
Illustratively, the training process of the target power generation prediction model includes: obtaining sample energy information and a predicted power generation data result corresponding to the sample energy information; inputting the sample energy information into a preset power generation prediction model to predict the power generation amount, and obtaining an output prediction result based on the output of the preset power generation prediction model; determining a training error based on the output prediction result and the predicted power generation data result, and reversely transmitting the training error to the preset power generation prediction model to adjust network parameters in the preset power generation prediction model; and when the preset convergence condition is met, determining that the training of the preset power generation prediction model is finished, and obtaining the target power generation prediction model.
Specifically, the training error may be determined according to an output decision result and a label decision result of the preset power generation prediction model based on the training function, and the training error is reversely propagated to the preset power generation prediction model, so as to adjust network parameters in the preset power generation prediction model, until a preset convergence condition is met, for example, when the iteration number reaches a preset number or the training error converges, the preset power generation prediction model is determined to be trained, and at this time, the preset power generation prediction model after the training is used as the target power generation prediction model. By utilizing the overlapping interaction sample data and the corresponding prediction power generation data result to carry out model training, the prediction accuracy of the target power generation prediction model can be ensured, and the accuracy of the target prediction power generation data is further ensured.
S103, aiming at each distributed energy device, determining device prediction power generation data corresponding to the distributed energy device based on the historical energy information and a target power generation prediction model obtained through pre-training.
The target power generation prediction model may be an LSTM deep neural network model. The target power generation prediction model at least comprises a forgetting door, an input door and an output door. The device predicted power generation data may be predicted power generation data obtained by predicting the distributed energy device by a pointer.
The input gate controls which information enters the LSTM unit, and updates the function input by the memory unit into a new memory unit. The forget gate controls which information is forgotten. The output value is based on the memory cells produced in the previous step, but there is a filtering process. Two part operations are also included here: a first part, a control signal between 0 and 1 generated by an output gate composed of sigmoid; and a second part for multiplying the finally generated output information by the control signal to obtain a final output value. The output gate controls the effect of the memory unit on the current output. Through these gating units, the LSTM can effectively control the flow and storage of information, thereby enabling modeling and prediction of sequence data.
Specifically, for each distributed energy device, historical energy information is input to a target power generation prediction model obtained through training in advance, and device prediction power generation data corresponding to each distributed energy device is obtained based on the output of the target power generation prediction model.
S104, determining target prediction power generation data corresponding to the region to be predicted according to the distributed energy map model and the prediction power generation data of each device.
The target predicted power generation data may refer to predicted power generation amounts of all distributed energy devices in the area to be predicted.
Specifically, a power generation aggregation path of the distributed energy equipment is determined according to the distributed energy map model, and the predicted power generation data of each equipment are aggregated according to the power generation aggregation path, so that the target predicted power generation data of the area to be predicted is obtained.
According to the technical scheme, the target data acquisition table which is acquired in advance for the area to be predicted is obtained. Based on the target data acquisition table, a distributed energy map model corresponding to the distributed energy in the area to be predicted is constructed, and based on the distributed energy map model, historical energy information corresponding to each distributed energy device in the area to be predicted can be rapidly inquired and determined, so that the probability of human inquiry errors is reduced, and the efficiency and accuracy of determining the historical energy information are improved. Aiming at each distributed energy device, based on the historical energy information and a target power generation prediction model obtained through pre-training, the device prediction power generation data corresponding to the determined distributed energy device is further improved. And according to the distributed energy map model and the power generation data predicted by each device, performing aggregation calculation upwards based on the topological relation of the distributed energy, and realizing accurate prediction of the distributed energy prediction.
Example two
Fig. 2 is a flowchart of a distributed resource prediction aggregation method according to a second embodiment of the present invention, where, based on the foregoing embodiments, target prediction power generation data corresponding to the determined to-be-predicted area is further refined. As shown in fig. 2, the method includes:
s201, acquiring a target data acquisition table acquired in advance for a region to be predicted.
S202, constructing a distributed energy map model corresponding to distributed energy in the area to be predicted based on the target data acquisition table, and determining historical energy information corresponding to each distributed energy device in the area to be predicted based on the distributed energy map model.
S203, aiming at each distributed energy device, determining device prediction power generation data corresponding to the distributed energy device based on the historical energy information and a target power generation prediction model obtained through pre-training.
S204, constructing a distributed energy aggregation path diagram according to the distributed energy diagram model.
The distributed energy aggregation path diagram may refer to an aggregation path diagram of the distributed energy devices incorporated into the power grid. Specifically, according to the distributed energy map model, the membership of each distributed energy device is queried, and then a distributed energy aggregation path map is constructed according to the membership.
Illustratively, the constructing a distributed energy aggregation path graph according to the distributed energy graph model includes:
performing data analysis processing on the target data acquisition table, and determining equipment attribute data corresponding to each model node in the distributed energy map model; determining, for each device in the set of device models, a device aggregation node to which the device belongs based on the device attribute data; and constructing a distributed energy aggregation path diagram based on the connection relation among the equipment aggregation nodes.
The device attribute data at least comprises connection relations between each device in the current type device model set and other types of devices.
Specifically, the data analysis processing is performed on the target data acquisition table, and the equipment attribute data corresponding to each model node in the distributed energy map model, that is, the equipment attribute data corresponding to each equipment model set, is determined. And determining the device aggregation node to which each device belongs based on the connection relation between each device and other types of devices in the device attribute data. And constructing a distributed energy aggregation path diagram based on the connection relation among the equipment aggregation nodes.
Illustratively, the apparatus includes at least: distributed energy devices, substation devices, and feeder devices. The process of determining device aggregation nodes is different for different types of devices. Illustratively, the determining, based on the device attribute data, a device aggregation node to which the device belongs includes: under the condition that the equipment is distributed energy equipment, determining substation equipment affiliated to each distributed energy equipment based on the equipment attribute data, and determining the substation equipment as an equipment aggregation node; if the equipment is substation equipment, determining substation equipment affiliated to each substation equipment based on the equipment attribute data, and determining the substation equipment as equipment aggregation nodes; and determining feeder equipment affiliated to each power distribution station equipment based on the equipment attribute data under the condition that the equipment is the power distribution station equipment, and determining the feeder equipment as equipment aggregation nodes.
That is, for the distributed energy devices, the affiliated substation devices of each distributed energy device are determined from the device attribute data. And aiming at the substation equipment, determining the affiliated distribution substation equipment of each substation according to the equipment attribute data. For substation equipment, determining affiliated feeder equipment of each substation according to the equipment attribute data. And then the grid-connected main network line can be determined according to the feeder equipment. Based on the membership of each device, a distributed energy aggregation path graph may then be determined.
And S205, according to the distributed energy aggregation path diagram, aggregating the predicted power generation data of each device to determine the target predicted power generation data.
Specifically, according to a distributed energy aggregation path diagram, the predicted power generation data of each device are gradually aggregated upwards, and then the target predicted power generation data is determined.
Illustratively, the process of determining the target predicted power generation data is as follows: and determining a distributed energy grid-connected path corresponding to each distributed energy device according to the distributed energy aggregation path diagram. And aiming at each distribution substation node, carrying out aggregation processing on the predicted power generation data of each equipment belonging to the distribution substation node to obtain the predicted power generation data of the distribution substation corresponding to the distribution substation node. And aiming at each feeder node, aggregating the predicted power generation data of each distribution transformer station belonging to the feeder node to obtain the predicted power generation data of the feeder corresponding to the feeder node. And carrying out aggregation processing on all the feeder line predicted power generation data to obtain the target predicted power generation data.
The distributed energy grid-connected path at least comprises a distribution substation node and a feeder line node, wherein the distribution substation node comprises at least one distributed energy source, and the feeder line node comprises at least one distribution substation node.
Specifically, a distributed energy grid-connected path of each distributed energy is determined according to a distributed energy aggregation path diagram. And according to the distributed energy grid-connected path, carrying out aggregation processing based on the predicted power generation data of the predicted equipment to obtain the predicted power generation data of the distribution transformer stations corresponding to the nodes of the distribution transformer stations. And carrying out aggregation treatment on the predicted power generation data of the distribution transformer station to obtain the predicted power generation data of the feeder corresponding to the feeder node. And then, carrying out aggregation processing on all the feeder line predicted power generation data to obtain the target predicted power generation data. The topological relation between the feeder line and the belonging substation can be used for splicing with a main network in the future, and the data communication and regulation of the main distribution network are supported.
According to the technical scheme, the distributed energy aggregation path diagram is constructed according to the distributed energy diagram model. And supporting a large amount of data access according to the distributed energy aggregation path diagram, and considering topological relation and distributed energy types. Based on the distributed energy power grid topology, the time sequence prediction on the distributed resource aggregation path is performed by utilizing the graph data, so that the prediction can be performed on each node on the aggregation path according to the condition that the power grid data acquisition is lost. And (3) carrying out aggregation processing on the predicted power generation data of each device, and determining the target predicted power generation data, so that the distributed energy prediction capability can be improved.
Example III
Fig. 3 is a schematic structural diagram of a distributed resource prediction aggregation device according to a third embodiment of the present invention. As shown in fig. 3, the apparatus includes:
the target data acquisition table acquisition module 301 is configured to acquire a target data acquisition table acquired in advance for a region to be predicted;
the distributed energy map model construction module 302 is configured to construct a distributed energy map model corresponding to distributed energy in the to-be-predicted area based on the target data acquisition table, and determine historical energy information corresponding to each distributed energy device in the to-be-predicted area based on the distributed energy map model;
a device prediction power generation data determining module 303, configured to determine, for each of the distributed energy devices, device prediction power generation data corresponding to the distributed energy device based on the historical energy information and a target power generation prediction model obtained by training in advance;
and the target prediction power generation data determining module 304 is configured to determine target prediction power generation data corresponding to the to-be-predicted area according to the distributed energy map model and each piece of equipment prediction power generation data.
According to the technical scheme, the target data acquisition table which is acquired in advance for the area to be predicted is obtained. Based on the target data acquisition table, a distributed energy map model corresponding to the distributed energy in the area to be predicted is constructed, and based on the distributed energy map model, historical energy information corresponding to each distributed energy device in the area to be predicted can be rapidly inquired and determined, so that the probability of human inquiry errors is reduced, and the efficiency and accuracy of determining the historical energy information are improved. Aiming at each distributed energy device, based on the historical energy information and a target power generation prediction model obtained through pre-training, the device prediction power generation data corresponding to the determined distributed energy device is further improved. And according to the distributed energy map model and the power generation data predicted by each device, performing aggregation calculation upwards based on the topological relation of the distributed energy, and realizing accurate prediction of the distributed resource prediction aggregation.
Optionally, the distributed energy map model building module 302 may be further specifically configured to:
performing table analysis processing on the target data acquisition table to obtain equipment model sets corresponding to all types of equipment and equipment parameter data corresponding to the equipment model sets;
determining the equipment model set as a model node of a distributed energy map model to be constructed aiming at each type of equipment model set, and taking equipment parameter data corresponding to the equipment model set as a model edge of the distributed energy map model to be constructed;
and determining the constructed distributed energy map model to be constructed as the distributed energy map model.
Optionally, the apparatus further comprises a model training module for:
obtaining sample energy information and a predicted power generation data result corresponding to the sample energy information;
inputting the sample energy information into a preset power generation prediction model to predict the power generation amount, and obtaining an output prediction result based on the output of the preset power generation prediction model;
determining a training error based on the output prediction result and the predicted power generation data result, and reversely transmitting the training error to the preset power generation prediction model to adjust network parameters in the preset power generation prediction model;
And when the preset convergence condition is met, determining that the training of the preset power generation prediction model is finished, and obtaining the target power generation prediction model.
Optionally, the target prediction power generation data determination module 304 includes:
the aggregation path diagram construction unit is used for constructing a distributed energy aggregation path diagram according to the distributed energy diagram model;
and the predicted power generation data determining unit is used for carrying out aggregation processing on the predicted power generation data of each device according to the distributed energy aggregation path diagram and determining the target predicted power generation data.
Optionally, the aggregate path graph construction unit includes:
the equipment attribute data determining subunit is used for carrying out data analysis processing on the target data acquisition table and determining equipment attribute data corresponding to each model node in the distributed energy map model, wherein the equipment attribute data at least comprises the connection relation between each equipment in the current type equipment model set and other types of equipment;
a device aggregation node determining subunit, configured to determine, for each device in the device model set, a device aggregation node to which the device belongs based on the device attribute data;
And the aggregation path diagram constructing subunit is used for constructing a distributed energy aggregation path diagram based on the connection relation among the equipment aggregation nodes.
Optionally, the device comprises at least: distributed energy equipment, substation equipment, distribution station equipment and feeder equipment; the device aggregation node determining subunit may further be specifically configured to:
under the condition that the equipment is distributed energy equipment, determining substation equipment affiliated to each distributed energy equipment based on the equipment attribute data, and determining the substation equipment as an equipment aggregation node;
if the equipment is substation equipment, determining substation equipment affiliated to each substation equipment based on the equipment attribute data, and determining the substation equipment as equipment aggregation nodes;
and determining feeder equipment affiliated to each power distribution station equipment based on the equipment attribute data under the condition that the equipment is the power distribution station equipment, and determining the feeder equipment as equipment aggregation nodes.
Optionally, the predicted power generation data determining unit may be further specifically configured to:
determining a distributed energy grid-connected path corresponding to each distributed energy device according to the distributed energy aggregation path diagram, wherein the distributed energy grid-connected path at least comprises a distribution substation node and a feeder line node, the distribution substation node comprises at least one distributed energy, and the feeder line node comprises at least one distribution substation node;
Aiming at each distribution substation node, carrying out aggregation processing on the predicted power generation data of each equipment belonging to the distribution substation node to obtain the predicted power generation data of the distribution substation corresponding to the distribution substation node;
aiming at each feeder node, aggregating the predicted power generation data of each distribution transformer station belonging to the feeder node to obtain the predicted power generation data of the feeder corresponding to the feeder node;
and carrying out aggregation processing on all the feeder line predicted power generation data to obtain the target predicted power generation data.
The distributed resource prediction aggregation device provided by the embodiment of the invention can execute the distributed resource prediction aggregation method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example IV
Fig. 4 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as method distributed resource prediction aggregation.
In some embodiments, the method distributed resource prediction aggregation may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more of the steps of the method distributed resource prediction aggregation described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the method distributed resource prediction aggregation in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method for predictive aggregation of distributed resources, comprising:
acquiring a target data acquisition table acquired in advance for a region to be predicted;
constructing a distributed energy map model corresponding to distributed energy in the region to be predicted based on the target data acquisition table, and determining historical energy information corresponding to each distributed energy device in the region to be predicted based on the distributed energy map model;
Aiming at each distributed energy device, determining device prediction power generation data corresponding to the distributed energy device based on the historical energy information and a target power generation prediction model obtained through pre-training;
and determining target prediction power generation data corresponding to the region to be predicted according to the distributed energy map model and the prediction power generation data of each device.
2. The method according to claim 1, wherein the constructing a distributed energy map model corresponding to the distributed energy in the region to be predicted based on the target data acquisition table includes:
performing table analysis processing on the target data acquisition table to obtain equipment model sets corresponding to all types of equipment and equipment parameter data corresponding to the equipment model sets;
determining the equipment model set as a model node of a distributed energy map model to be constructed aiming at each type of equipment model set, and taking equipment parameter data corresponding to the equipment model set as a model edge of the distributed energy map model to be constructed;
and determining the constructed distributed energy map model to be constructed as the distributed energy map model.
3. The method of claim 1, wherein the training process of the target power generation predictive model comprises:
obtaining sample energy information and a predicted power generation data result corresponding to the sample energy information;
inputting the sample energy information into a preset power generation prediction model to predict the power generation amount, and obtaining an output prediction result based on the output of the preset power generation prediction model;
determining a training error based on the output prediction result and the predicted power generation data result, and reversely transmitting the training error to the preset power generation prediction model to adjust network parameters in the preset power generation prediction model;
and when the preset convergence condition is met, determining that the training of the preset power generation prediction model is finished, and obtaining the target power generation prediction model.
4. The method according to claim 1, wherein the determining target predicted power generation data corresponding to the region to be predicted from the distributed energy map model and each of the device predicted power generation data includes:
constructing a distributed energy aggregation path diagram according to the distributed energy diagram model;
and according to the distributed energy aggregation path diagram, aggregating the predicted power generation data of each device to determine the target predicted power generation data.
5. The method of claim 4, wherein constructing a distributed energy aggregation path graph from the distributed energy graph model comprises:
performing data analysis processing on the target data acquisition table, and determining equipment attribute data corresponding to each model node in the distributed energy map model, wherein the equipment attribute data at least comprises the connection relation between each equipment in the current type equipment model set and other types of equipment;
determining, for each device in the set of device models, a device aggregation node to which the device belongs based on the device attribute data;
and constructing a distributed energy aggregation path diagram based on the connection relation among the equipment aggregation nodes.
6. The method according to claim 5, characterized in that the device comprises at least: distributed energy equipment, substation equipment, distribution station equipment and feeder equipment; the determining, based on the device attribute data, a device aggregation node to which the device belongs, includes:
under the condition that the equipment is distributed energy equipment, determining substation equipment affiliated to each distributed energy equipment based on the equipment attribute data, and determining the substation equipment as an equipment aggregation node;
If the equipment is substation equipment, determining substation equipment affiliated to each substation equipment based on the equipment attribute data, and determining the substation equipment as equipment aggregation nodes;
and determining feeder equipment affiliated to each power distribution station equipment based on the equipment attribute data under the condition that the equipment is the power distribution station equipment, and determining the feeder equipment as equipment aggregation nodes.
7. The method of claim 4, wherein aggregating each of the device-predicted power generation data according to the distributed energy aggregation path graph, determining the target-predicted power generation data, comprises:
determining a distributed energy grid-connected path corresponding to each distributed energy device according to the distributed energy aggregation path diagram, wherein the distributed energy grid-connected path at least comprises a distribution substation node and a feeder line node, the distribution substation node comprises at least one distributed energy, and the feeder line node comprises at least one distribution substation node;
aiming at each distribution substation node, carrying out aggregation processing on the predicted power generation data of each equipment belonging to the distribution substation node to obtain the predicted power generation data of the distribution substation corresponding to the distribution substation node;
Aiming at each feeder node, aggregating the predicted power generation data of each distribution transformer station belonging to the feeder node to obtain the predicted power generation data of the feeder corresponding to the feeder node;
and carrying out aggregation processing on all the feeder line predicted power generation data to obtain the target predicted power generation data.
8. A distributed resource prediction aggregation apparatus, comprising:
the target data acquisition table acquisition module is used for acquiring a target data acquisition table acquired in advance for the area to be predicted;
the distributed energy map model construction module is used for constructing a distributed energy map model corresponding to distributed energy in the region to be predicted based on the target data acquisition table, and determining historical energy information corresponding to each distributed energy device in the region to be predicted based on the distributed energy map model;
the equipment prediction power generation data determining module is used for determining equipment prediction power generation data corresponding to the distributed energy equipment based on the historical energy information and a target power generation prediction model obtained through pre-training for each distributed energy equipment;
and the target prediction power generation data determining module is used for determining target prediction power generation data corresponding to the area to be predicted according to the distributed energy map model and the equipment prediction power generation data.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the distributed resource prediction aggregation method of any one of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing a processor to implement the distributed resource prediction aggregation method of any one of claims 1-7 when executed.
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