CN115934691A - Method and device for determining short-term photovoltaic power - Google Patents
Method and device for determining short-term photovoltaic power Download PDFInfo
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Abstract
The invention provides a method for determining short-term photovoltaic power, which comprises the following steps: acquiring historical multi-source data of the photovoltaic station, and performing data cleaning on the historical multi-source data; decomposing the washed historical multi-source data to obtain historical decomposed data corresponding to the historical multi-source data; training a pre-constructed extreme learning machine model according to historical decomposition data to obtain a well-trained extreme learning machine model; and acquiring real-time operation data and real-time meteorological data of the photovoltaic array in the online operation process, and inputting the real-time operation data and the real-time meteorological data into the trained extreme learning machine model to obtain short-term photovoltaic power data. Based on the method, the signal data can be effectively decomposed with high efficiency and original physical information is kept aiming at unstable signal data, the problem of photovoltaic power prediction is solved, and the method aims to optimize a photovoltaic power generation system operation strategy, a photovoltaic station site selection scheme and improve photovoltaic unit equipment maintenance efficiency.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a method and a device for determining short-term photovoltaic power.
Background
As an important basis for human survival and development, the development and utilization of energy has been widely regarded. The consumption rate of traditional fossil energy is greatly increased, causing irreversible environmental pollution, which in turn leads to extreme climate disasters. With the social progress and development and the economic construction entering new stages, china puts forward new plans for future energy development strategies and determines the main melody of 'green and low carbon'. Due to the advantages of cleanness, safety, high efficiency and the like, solar energy becomes a renewable new energy source which is developed at the fastest speed in recent years. With the increase of the total installed photovoltaic capacity and large-scale grid-connected operation, the solar energy is difficult to effectively utilize, and the problem of obvious light abandonment exists.
In the related technology, the photovoltaic power prediction method is mainly based on data mining, and models in other fields are adopted to improve the prediction accuracy.
However, in the related art, if other domain models are applied to the photovoltaic power generation domain without selection and improvement, the ideal effect cannot be obtained. The difficulty for short-term photovoltaic power prediction is that the raw time-series data is non-linear and non-stationary. Therefore, a method for determining short-term photovoltaic power is needed to resolve the signal data with high efficiency and retain the original physical information for non-stationary signal data, so as to solve the problem of effectiveness of photovoltaic power prediction.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, a first objective of the present invention is to provide a method for determining short-term photovoltaic power, which resolves signal data with high efficiency and retains original physical information for non-stationary signal data, so as to solve the problem of validity of photovoltaic power prediction.
A second object of the invention is to propose a device for determining the short-term photovoltaic power.
A third object of the invention is to propose a computer device.
A fourth object of the invention is to propose a non-transitory computer-readable storage medium.
In order to achieve the above object, a first aspect of the present invention provides a method for determining short-term photovoltaic power, including:
obtaining historical multi-source data of a photovoltaic station, and performing data cleaning on the historical multi-source data;
decomposing the washed historical multi-source data to obtain historical decomposed data corresponding to the historical multi-source data;
training a pre-constructed extreme learning machine model according to the historical decomposition data to obtain a trained extreme learning machine model;
and acquiring real-time operation data and real-time meteorological data of the photovoltaic array in the online operation process, and inputting the real-time operation data and the real-time meteorological data into the trained extreme learning machine model to obtain short-term photovoltaic power data.
Optionally, in an embodiment of the present invention, the performing decomposition processing on the washed historical multi-source data includes:
decomposing the washed historical multi-source data into oscillation components by a group hunting method;
determining position information and irradiance information according to the oscillation component and predefined driving force and cohesion;
establishing a relation between the position information and the decomposition data, and solving a group decomposition parameter;
and determining the optimal solution of the group decomposition parameters and the group quantity according to preset conditions to obtain the decomposition data.
Alternatively, in an embodiment of the present invention, the driving force is defined as:
F dr (n,i)＝P prey (n)-P i (n-1)
wherein, F dr (n, i) is a driving force, i is a component, and n is the number of steps. The location information of hunting is defined as P prey ，P i And (n-1) is the position information of the step component i at the step n-1.
Optionally, in an embodiment of the present invention, the cohesive force is defined as:
wherein, F coh (n, i) is cohesive force, d and d cr Respectively the distance between the components and the critical distance, M representing the number of clusters, P i [n-1]Is the position information of the component i at step n-1, P j [n-1]Is the position information of the component j at step n-1.
Optionally, in an embodiment of the present invention, the training, according to the historical decomposition data, of the extreme learning machine model constructed in advance to obtain a trained extreme learning machine model includes:
fusing the decomposed data and the meteorological data, and dividing the fused decomposed data and meteorological data into a plurality of subdata sequences;
and training the meta-extreme learning machine model according to the plurality of subdata series to obtain the trained meta-extreme learning machine model.
In order to achieve the above object, a second embodiment of the present invention provides an apparatus for determining short-term photovoltaic power, including:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring historical multi-source data of a photovoltaic station and cleaning the historical multi-source data;
the decomposition module is used for decomposing the cleaned historical multi-source data to obtain historical decomposition data corresponding to the historical multi-source data;
the training module is used for training a pre-constructed extreme meta-learning machine model according to the historical decomposition data to obtain a trained extreme meta-learning machine model;
and the second acquisition module is used for acquiring real-time operation data and real-time meteorological data of the photovoltaic array in the online operation process, and inputting the real-time operation data and the real-time meteorological data into the trained extreme learning machine model to obtain short-term photovoltaic power data.
Optionally, in an embodiment of the present invention, the decomposition module is further configured to:
decomposing the washed historical multi-source data into oscillation components by a group hunting method;
determining position information and irradiance information according to the oscillation component and predefined driving force and cohesion;
establishing a relation between the position information and the decomposition data, and solving a group decomposition parameter;
and determining the optimal solution of the group decomposition parameters and the group quantity according to preset conditions to obtain the decomposition data.
Optionally, in an embodiment of the present invention, the training module is further configured to:
fusing the decomposition data and the meteorological data, and dividing the fused decomposition data and the meteorological data into a plurality of subdata sequences;
and training the meta-extreme learning machine model according to the plurality of subdata series to obtain the trained meta-extreme learning machine model.
In summary, according to the method and the device for determining short-term photovoltaic power provided by the invention, historical multi-source data of a photovoltaic station are obtained and cleaned, the cleaned historical multi-source data are decomposed to obtain historical decomposed data, a pre-constructed extreme learning machine model is trained according to the historical decomposed data to obtain a trained extreme learning machine model, and finally real-time operation data and real-time meteorological data in the online operation process of a photovoltaic array are obtained and input into the trained extreme learning machine model to obtain the short-term photovoltaic power data. Based on the method, the signal data can be effectively decomposed with high efficiency and original physical information is reserved aiming at non-stable signal data, the problem of photovoltaic power prediction is solved based on a fusion model combining a group decomposition algorithm and a meta-extreme learning machine, and the method is used for optimizing a photovoltaic power generation system operation strategy, a photovoltaic station site selection scheme and improving photovoltaic unit equipment maintenance efficiency.
In order to achieve the above object, a third aspect of the present invention provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the computer device implements the method according to the first aspect of the present invention.
To achieve the above object, a fourth aspect of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method according to the first aspect of the present invention.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic flowchart of a method for determining short-term photovoltaic power according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a short-term photovoltaic power determination apparatus according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
A method and apparatus for short-term photovoltaic power determination according to embodiments of the present invention is described below with reference to the accompanying drawings.
Fig. 1 is a schematic flowchart of a method for determining short-term photovoltaic power according to an embodiment of the present invention.
As shown in fig. 1, the method for determining short-term photovoltaic power includes the following steps:
step S10: historical multi-source data of the photovoltaic station are obtained, and data cleaning is carried out on the historical multi-source data.
In the embodiment of the present invention, the data cleaning of the historical multi-source data includes:
unifying the format of the multi-source data of each photovoltaic station;
and carrying out normalization processing on the multi-source data.
Step S20: and decomposing the washed historical multi-source data to obtain historical decomposed data corresponding to the historical multi-source data.
In the embodiment of the present invention, the decomposing process of the washed historical multi-source data includes:
decomposing the washed historical multi-source data into oscillation components by a group hunting method;
determining position information and irradiance information according to the oscillation component and predefined driving force and cohesion;
establishing a relation between the position information and the decomposition data, and solving a group decomposition parameter;
and determining the optimal solution of the group decomposition parameters and the group quantity according to preset conditions to obtain decomposition data.
And, in an embodiment of the present invention, the driving force is defined as:
F dr (n,i)＝P prey (n)-P i (n-1)
wherein, F dr (n, i) is the driving force, i is the component, and n is the number of steps. The location information of hunting is defined as P prey ，P i And (n-1) is the position information of the step component i at the step n-1.
And, in embodiments of the present invention, cohesion is defined as:
wherein, F coh (n, i) is cohesive force, d and d cr Respectively the distance between the components and the critical distance, M representing the number of clusters, P i [n-1]Is the position information of the component i at step n-1, P j [n-1]Is the position information of the component j at step n-1.
Further, in the embodiment of the present invention, constructing a relationship between the location information and the decomposition data, and solving the group decomposition parameter includes:
P i [n]＝P i [n-1]+δ(IR i [n])
wherein IR is irradiance information, IR i [n-1]Is the irradiance information at step n-1, component i, and δ is one of the important parameters for cluster decomposition, determining the cluster adaptability. Based on this, decomposed data y [ n ] can be constructed]And solving a parameter delta in relation to the position information:
where β is a parameter that affects the number of components, in order to determine the values of these parameters from the input data, the following condition needs to be satisfied:
|Y δ,M [k]i and I S [ k ]]Respectively represents Y δ,M [k]And S [ k ]]The amplitude of the original sequence after discrete fourier transformation. Y is δ,M [n]Is SWF, S [ n ] expressed by two parameters of delta, M]Is a signal that includes a single component that is not stationary.
Obtaining decomposition data Y by finding the optimal solution of delta and M δ,M [n]。
And step S3: and training the pre-constructed extreme meta-learning machine model according to historical decomposition data to obtain the trained extreme meta-learning machine model.
In the embodiment of the present invention, training a meta-extreme learning machine model constructed in advance according to historical decomposition data to obtain a trained meta-extreme learning machine model, includes:
fusing the decomposed data and the meteorological data, and dividing the fused decomposed data and the meteorological data into a plurality of subdata sequences;
and training the extreme meta-learning machine model according to the plurality of sub-data series to obtain the trained extreme meta-learning machine model.
Specifically, in the embodiment of the present invention, training the meta-extreme learning machine model according to a plurality of sub-data series to obtain a trained meta-extreme learning machine model, includes:
the input subsystem data was used to train a single hidden layer feed-forward network with an extreme learning machine:
wherein,
is the connection weight, ω j Is an input weight, b j Is a deviation, N h Is the number of hidden layers.
In addition, except for the connection weight, the values are randomly selected, and the connection weight is calculated by the following steps.
Inputting N training data, the above described equation can be further expressed as a matrix vector H:
the output weights and the target for each output may be expressed as:
T＝Hγ
and estimating output connection weight by taking an inverse matrix of the Morse-Penrose H matrix, wherein each extreme learning machine in the meta-extreme learning machine network is obtained by training the subseries of data, and the output connection weight is obtained by using all data through the learning rule of the extreme learning machine.
And step S4: and acquiring real-time operation data and real-time meteorological data of the photovoltaic array in the online operation process, and inputting the real-time operation data and the real-time meteorological data into the trained extreme learning machine model to obtain short-term photovoltaic power data.
In summary, according to the method for determining short-term photovoltaic power provided by the invention, historical multi-source data of a photovoltaic station are obtained and cleaned, the cleaned historical multi-source data are decomposed to obtain historical decomposed data, a pre-constructed extreme learning machine model is trained according to the historical decomposed data to obtain a trained extreme learning machine model, and finally real-time operation data and real-time meteorological data in the online operation process of a photovoltaic array are obtained and input into the trained extreme learning machine model to obtain the short-term photovoltaic power data. Based on the method, the signal data can be effectively decomposed with high efficiency and original physical information is reserved aiming at non-stable signal data, the problem of photovoltaic power prediction is solved based on a fusion model combining a group decomposition algorithm and a meta-extreme learning machine, and the method is used for optimizing a photovoltaic power generation system operation strategy, a photovoltaic station site selection scheme and improving photovoltaic unit equipment maintenance efficiency.
Fig. 2 is a schematic structural diagram of a short-term photovoltaic power determination apparatus according to an embodiment of the present invention.
As shown in fig. 2, the short-term photovoltaic power determination apparatus includes the following modules:
the first acquisition module 100 is used for acquiring historical multi-source data of the photovoltaic station and cleaning the historical multi-source data;
the decomposition module 200 is used for performing decomposition processing on the washed historical multi-source data to obtain historical decomposition data corresponding to the historical multi-source data;
the training module 300 is used for training the pre-constructed extreme meta-learning machine model according to the historical decomposition data to obtain a trained extreme meta-learning machine model;
the second obtaining module 400 is configured to obtain real-time operation data and real-time meteorological data of the photovoltaic array during online operation, and input the real-time operation data and the real-time meteorological data to the trained extreme learning machine model to obtain short-term photovoltaic power data.
In an embodiment of the present invention, the decomposition module 200 is further configured to:
decomposing the washed historical multi-source data into oscillation components by a group hunting method;
determining position information and irradiance information according to the oscillation component and predefined driving force and cohesion;
establishing a relation between the position information and the decomposition data, and solving a group decomposition parameter;
and determining the optimal solution of the group decomposition parameters and the group quantity according to preset conditions to obtain decomposition data.
And, in an embodiment of the present invention, the training module 300 is further configured to:
fusing the decomposed data and the meteorological data, and dividing the fused decomposed data and the meteorological data into a plurality of subdata sequences;
and training the meta-extreme learning machine model according to the plurality of sub-data series to obtain the trained meta-extreme learning machine model.
It should be noted that the foregoing explanation of the embodiment of the short-term photovoltaic power determination method is also applicable to the apparatus of the embodiment, and reference may be made to the related description of the foregoing embodiment, which is not repeated herein.
In summary, according to the short-term photovoltaic power determining device provided by the invention, historical multi-source data of a photovoltaic station are obtained and cleaned, the cleaned historical multi-source data are decomposed to obtain historical decomposed data, a pre-constructed extreme learning machine model is trained according to the historical decomposed data to obtain a trained extreme learning machine model, and finally real-time running data and real-time meteorological data in the online running process of a photovoltaic array are obtained and input into the trained extreme learning machine model to obtain short-term photovoltaic power data. Based on the method, the signal data can be effectively decomposed with high efficiency and original physical information is reserved aiming at non-stable signal data, the problem of photovoltaic power prediction is solved based on a fusion model combining a group decomposition algorithm and a meta-extreme learning machine, and the method is used for optimizing a photovoltaic power generation system operation strategy, a photovoltaic station site selection scheme and improving photovoltaic unit equipment maintenance efficiency.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are well known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A method for determining short-term photovoltaic power, comprising:
acquiring historical multi-source data of a photovoltaic station, and performing data cleaning on the historical multi-source data;
decomposing the washed historical multi-source data to obtain historical decomposed data corresponding to the historical multi-source data;
training a pre-constructed extreme learning machine model according to the historical decomposition data to obtain a trained extreme learning machine model;
and acquiring real-time operation data and real-time meteorological data of the photovoltaic array in the online operation process, and inputting the real-time operation data and the real-time meteorological data into the trained extreme learning machine model to obtain short-term photovoltaic power data.
2. The determination method of claim 1, wherein the performing decomposition processing on the washed historical multi-source data comprises:
decomposing the washed historical multi-source data into oscillation components by a group hunting method;
determining position information and irradiance information according to the oscillation component and predefined driving force and cohesion;
establishing a relation between the position information and the decomposition data, and solving a group decomposition parameter;
and determining the optimal solution of the group decomposition parameters and the group quantity according to preset conditions to obtain the decomposition data.
3. The determination method according to claim 2, wherein the driving force is defined as:
F dr (n,i)＝P prey (n)-P i (n-1)
wherein, F dr (n, i) is the driving force, i is the component, and n is the number of steps. The location information of hunting is defined as P prey ，P i And (n-1) is the position information of the step component i at the step n-1.
4. The determination method according to claim 2, wherein the cohesion is defined as:
wherein, F coh (n, i) is cohesive force, d and d cr Respectively the distance between the components and the critical distance, M representing the number of clusters, P i [n-1]Is the position information of the component i at step n-1, P j [n-1]Is the position information of the component j at step n-1.
5. The determination method according to any one of claims 2 to 4, wherein the training of the previously constructed meta-extreme learning machine model according to the historical decomposition data to obtain a trained meta-extreme learning machine model comprises:
fusing the decomposed data and the meteorological data, and dividing the fused decomposed data and meteorological data into a plurality of subdata sequences;
and training the meta-extreme learning machine model according to the plurality of subdata series to obtain the trained meta-extreme learning machine model.
6. An apparatus for short-term photovoltaic power determination, comprising:
the first acquisition module is used for acquiring historical multi-source data of the photovoltaic station and cleaning the historical multi-source data;
the decomposition module is used for decomposing the cleaned historical multi-source data to obtain historical decomposition data corresponding to the historical multi-source data;
the training module is used for training a pre-constructed extreme meta-learning machine model according to the historical decomposition data to obtain a trained extreme meta-learning machine model;
and the second acquisition module is used for acquiring real-time operation data and real-time meteorological data of the photovoltaic array in the online operation process, and inputting the real-time operation data and the real-time meteorological data into the trained extreme learning machine model to obtain short-term photovoltaic power data.
7. The determination apparatus of claim 6, wherein the decomposition module is further configured to:
decomposing the washed historical multi-source data into oscillation components by a group hunting method;
determining position information and irradiance information according to the oscillation component and predefined driving force and cohesion;
establishing a relation between the position information and the decomposition data, and solving a group decomposition parameter;
and determining the optimal solution of the group decomposition parameters and the group quantity according to preset conditions to obtain the decomposition data.
8. The determination apparatus of claim 6, wherein the training module is further configured to:
fusing the decomposition data and the meteorological data, and dividing the fused decomposition data and the meteorological data into a plurality of subdata sequences;
and training the meta-extreme learning machine model according to the plurality of subdata series to obtain the trained meta-extreme learning machine model.
9. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method according to any of claims 1-5 when executing the computer program.
10. A non-transitory computer-readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the method of any one of claims 1-5.
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|---|---|---|---|---|
| CN117252448A (en) * | 2023-11-20 | 2023-12-19 | 华东交通大学 | Wind power prediction method based on space-time feature extraction and secondary decomposition polymerization |
| CN117252448B (en) * | 2023-11-20 | 2024-02-20 | 华东交通大学 | Wind power prediction method based on space-time feature extraction and secondary decomposition polymerization |
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