CN113449928A - Wind field power prediction method, system, server and computer readable storage medium - Google Patents
Wind field power prediction method, system, server and computer readable storage medium Download PDFInfo
- Publication number
- CN113449928A CN113449928A CN202110794302.0A CN202110794302A CN113449928A CN 113449928 A CN113449928 A CN 113449928A CN 202110794302 A CN202110794302 A CN 202110794302A CN 113449928 A CN113449928 A CN 113449928A
- Authority
- CN
- China
- Prior art keywords
- data
- wind
- server
- historical
- learning model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000007781 pre-processing Methods 0.000 claims abstract description 24
- 238000004891 communication Methods 0.000 claims description 18
- 238000012545 processing Methods 0.000 claims description 13
- 238000004590 computer program Methods 0.000 claims description 10
- 238000004140 cleaning Methods 0.000 claims description 4
- 238000012549 training Methods 0.000 claims description 4
- 230000002787 reinforcement Effects 0.000 claims description 3
- 238000010248 power generation Methods 0.000 abstract description 2
- 230000005540 biological transmission Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000011161 development Methods 0.000 description 2
- 230000005672 electromagnetic field Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 239000013307 optical fiber Substances 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000007637 random forest analysis Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/067—Enterprise or organisation modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Tourism & Hospitality (AREA)
- General Health & Medical Sciences (AREA)
- General Business, Economics & Management (AREA)
- Data Mining & Analysis (AREA)
- Game Theory and Decision Science (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Molecular Biology (AREA)
- Evolutionary Computation (AREA)
- Development Economics (AREA)
- Computational Linguistics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biophysics (AREA)
- Mathematical Physics (AREA)
- Biomedical Technology (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Artificial Intelligence (AREA)
- Educational Administration (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- Primary Health Care (AREA)
- Wind Motors (AREA)
Abstract
The embodiment of the invention provides a wind field power prediction method, a wind field power prediction system, a wind field power prediction server and a computer readable storage medium, and relates to the field of wind power generation. The method comprises the following steps: the method comprises the steps that first data acquired by an acquisition terminal are received, preprocessing is carried out on the first data to obtain second data, prediction is carried out through a preset learning model according to the second data to obtain prediction power, and the learning model is trained through historical wind power data, historical temperature data, historical humidity data, historical wind speed data and historical wind direction data. The method can realize the intelligent prediction of the wind field power, avoids the traditional manual examination and effectively improves the prediction efficiency of the wind field power.
Description
Technical Field
The invention relates to the field of wind power generation, in particular to a wind field power prediction method, a wind field power prediction system, a wind field power prediction server and a computer readable storage medium.
Background
With the continuous development and promotion of new energy technology, wind power starts to be connected to the grid in a large scale. However, due to the continuous change of data such as wind speed and wind direction, the wind power also has a variable characteristic, which provides a great challenge for wind power integration. To improve this situation, wind farm power prediction becomes very important and indispensable.
In the prior art, the judgment of various data of the wind field usually needs manual examination and verification, so that the efficiency of wind field power prediction is greatly reduced.
Disclosure of Invention
The invention aims to provide a wind field power prediction method, a wind field power prediction system, a wind field power prediction server and a readable storage medium, which can effectively improve the intelligent degree of wind field power prediction, thereby improving the efficiency of wind field power prediction.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical solutions:
in a first aspect, a wind farm power prediction method is applied to a server, wherein the server is in communication connection with an acquisition terminal, and the method comprises the following steps of;
receiving first data acquired by the acquisition terminal, wherein the first data comprises temperature data, humidity data, wind speed data and wind direction data;
preprocessing the first data to obtain second data;
predicting through a preset learning model according to the second data to obtain predicted power;
wherein the learning model is trained from historical wind power data, historical temperature data, historical humidity data, historical wind speed data, and historical wind direction data.
Optionally, the preprocessing the first data to obtain second data includes:
processing the missing value of the first data to obtain first preprocessing data;
cleaning the first preprocessing data to obtain second preprocessing data;
normalizing the second preprocessed data to obtain third preprocessed data;
and formatting the third preprocessed data to obtain second data.
Optionally, the learning model includes: an LSTM short-wavelength memory model or a deep reinforcement learning model.
Optionally, the method further includes: storing the first data.
In a second aspect, a wind farm power prediction system includes: the system comprises an acquisition terminal and a server, wherein the acquisition terminal is in communication connection with the server;
the acquisition terminal is used for acquiring first data, wherein the first data comprises temperature data, humidity data, wind speed data and wind direction data;
the server is used for receiving first data acquired by the acquisition terminal; preprocessing the first data to obtain second data; predicting through a preset learning model according to the second data to obtain predicted power; the learning model is formed by training historical wind power data, historical temperature data, historical humidity data, historical wind speed data and historical wind direction data.
Optionally, the server is in communication connection with the acquisition terminal through an RS485 bus.
Optionally, the server includes a computing center and a service terminal, and the computing center is in communication connection with the service terminal;
the computing center is used for receiving first data acquired by the acquisition terminal; preprocessing the first data to obtain second data; sending the second data to the service terminal;
and the service terminal is used for receiving the second data and predicting through a preset learning model according to the second data to obtain predicted power.
Optionally, the computing center and the service terminal are in communication connection through a 5G network.
In a third aspect, a server comprises a memory storing a computer program and a processor implementing the steps of the wind farm power prediction method when executing the computer program.
In a fourth aspect, a computer readable storage medium has stored thereon a computer program which, when executed by a processor, performs the steps of the wind farm power prediction method.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a wind field power prediction method, a wind field power prediction system, a server and a readable storage medium. The server receives first data acquired by the acquisition terminal, preprocesses the first data to acquire second data, and predicts through a preset learning model according to the second data to acquire predicted power. Therefore, the intelligent prediction of the wind field power can be realized, the traditional manual examination is avoided, and the prediction efficiency of the wind field power is effectively improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is one of system block diagrams of a wind farm power prediction system provided in the present embodiment;
fig. 2 is a second system block diagram of the wind farm power prediction system provided in this embodiment;
fig. 3 is a schematic diagram of an application scenario provided in the embodiment of the present invention;
FIG. 4 is a flow chart of a wind farm power prediction method according to an embodiment of the present invention;
FIG. 5 is a flow chart of a pre-process provided by an embodiment of the present invention.
Icon: 10-wind farm power prediction system; 100-a server; 110-a computing center; 120-a service terminal; 130-a memory; 140-a processor; 150-a communication unit; 200-collecting terminal;
Detailed Description
As noted in the background art, with the continuous development and promotion of new energy technologies, wind power starts to be incorporated into the power grid on a large scale. However, due to the continuous change of data such as wind speed and wind direction, the wind power also has a variable characteristic, which provides a great challenge for wind power integration. To improve this situation, wind farm power prediction becomes very important and indispensable. In the prior art, the judgment of various data of the wind field usually needs manual examination and verification, so that the efficiency of wind field power prediction is greatly reduced.
The problems existing in the prior art are all the results obtained after the inventor practices and researches, so that the discovery process of the problems and the solution proposed by the embodiment of the invention in the following for the problems are all the contributions of the inventor in the invention process.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Furthermore, the appearances of the terms "first," "second," and the like, if any, are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
Some embodiments of the invention are described in detail below with reference to the accompanying drawings. It should be noted that the features of the embodiments of the present invention may be combined with each other without conflict.
Referring to fig. 1, according to an embodiment of the present invention, a wind farm power prediction system 10 is provided, which includes an acquisition terminal 200 and a server 100, wherein the acquisition terminal 200 is in communication connection with the server 100.
The collection terminal 200 is configured to collect first data, where the first data includes temperature data, humidity data, wind speed data, and wind direction data.
The server 100 is configured to receive first data acquired by the acquisition terminal 200, preprocess the first data to obtain second data, and predict according to the second data through a preset learning model to obtain predicted power. The learning model is formed by training historical wind power data, historical temperature data, historical humidity data, historical wind speed data and historical wind direction data.
In some embodiments, the acquisition period of the acquisition terminal 200, the reporting time of the acquired first data, and the priority of the acquisition terminal 200 may be preset in the server 100. The server 100 controls the acquisition terminal 200 to acquire data according to a preset acquisition cycle, a preset reporting time and a preset priority. Through setting of the acquisition period, the reporting time and the priority, the server 100 can more intelligently control the acquisition terminal 200 to acquire data. In this embodiment, the server 100 controls the acquisition terminal 200 to acquire data through the Modbus question-answering communication protocol.
In the existing wind field, there is often very strong electromagnetic interference, the collection terminal 200 is often disposed inside the wind turbine, the electromagnetic interference received by the collection terminal is particularly strong, if the data collected by the collection terminal 200 is wirelessly transmitted, the data can be seriously interfered by electromagnetic interference, and if the server 100 is located in an electromagnetic field, the data processing by the server can also be strongly interfered by electromagnetic interference, so that the stability and accuracy of data processing are affected.
In this embodiment, the server 100 is communicatively connected to the acquisition terminal 200 through an RS485 bus. And the server 100 is located away from electromagnetic interference from the wind farm. The RS485 bus is adopted to connect the server 100 and the acquisition terminal 200 in a communication manner, so that the interference of an electromagnetic field can be effectively avoided, and the stability and the accuracy of the server 100 on data processing are improved.
In order to ensure the sample size of data processing, in this embodiment, one acquisition terminal 200 is deployed for each unit in the wind farm, so that independent data sampling of each unit is realized, the sample size of data processing in the later period can be ensured, and the randomness is reduced.
Wherein, in this embodiment, collection terminal 200 can include microprocessor and sensor, and in this embodiment, microprocessor adopts the STM32 singlechip, and the sensor includes air humidity sensor, scope at least: -30-70 ℃, precision: . + -. 0.2 ℃ resolution: 0.01 ℃; air humidity sensor, range 0 ~ 100%, precision: ± 3%, resolution: 0.1 percent; wind speed measurement sensor, range: 0-30 m/s, precision: ± 0.5%, resolution: 0.1 m/s; wind direction measuring sensor, range: 16 azimuth (360 °) accuracy: ± 0.5% resolution: 0.1 percent.
In another embodiment, referring to fig. 2, the server 100 includes a computing center 110 and a service terminal 120, and the computing center 110 is communicatively connected to the service terminal 120.
The computing center 110 is used for receiving first data acquired by the acquisition terminal; preprocessing the first data to obtain second data; sending the second data to the service terminal;
the service terminal 120 is configured to receive the second data, and perform prediction according to a preset learning model according to the second data to obtain predicted power.
By providing the computing center 110 and the service terminal 120 separately, remote prediction of wind farm power can be achieved.
In the present embodiment, the computing center 110 and the service terminal 120 are communicatively connected through a 5G network.
Specifically, the computing center 110 and the service terminal 120 are both provided with a 5G communication module, the computing center 110 transmits the second data to the service provider base station through the 5G communication module, the service provider base station transmits the second data to the service provider core network through an optical fiber according to a predetermined protocol format, and the service provider core network transmits the second data to the service terminal 120 through the optical fiber. The transmission between the computing center 110 and the service terminal 120 through the 5G network can improve the time efficiency of data transmission.
The embodiment also provides a server 100 capable of predicting the power of the wind field. In one possible implementation, the server 100 may be a user terminal, for example, the server 100 may be, but is not limited to, a smart phone, a Personal Computer (PC), a tablet PC, a Personal Digital Assistant (PDA), a Mobile Internet Device (MID), and the like.
Please refer to fig. 3, which illustrates a schematic structure of the server 100. The server 100 includes a memory 130, a processor 140, and a communication unit 150.
The memory 130, processor 140 and communication unit 150 are electrically connected to each other directly or indirectly to enable data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. Processor 140 is operative to execute executable instructions stored in memory 130. The executable instructions, when executed by the processor 140, implement the wind farm power prediction method.
The Memory 130 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 130 is used for storing a program, and the processor 140 executes the program after receiving the execution instruction. The communication unit 150 is used for transceiving data through a network.
The processor 140 may be an integrated circuit chip having signal processing capabilities. The Processor 140 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor 140 may be any conventional processor or the like.
It should be understood that the configuration shown in fig. 3 is merely a schematic diagram of the configuration of the server 100, and that the server 100 may include more or less components than those shown in fig. 3, or have a different configuration than that shown in fig. 3. The components shown in fig. 3 may be implemented in hardware, software, or a combination thereof.
Referring to fig. 4, a wind farm power prediction method according to an embodiment of the present invention is applied to a server 100, and the server 100 is in communication connection with an acquisition terminal. The wind farm power prediction method may be performed by the server 100 described in fig. 1, for example, may be performed by the processor 140 in the server 100.
The wind field power prediction method comprises the following steps:
step 101: and receiving the first data acquired by the acquisition terminal 200.
Wherein the first data comprises temperature data, humidity data, wind speed data, and wind direction data.
Step 102: and preprocessing the first data to obtain second data.
Step 103: and predicting through a preset learning model according to the second data to obtain predicted power.
Wherein the learning model is trained from historical wind power data, historical temperature data, historical humidity data, historical wind speed data, and historical wind direction data.
In the above method, the server 100 receives the collected first data, preprocesses the first data to obtain second data, and obtains the predicted power through a preset learning model according to the second data. The method can effectively improve the intelligent degree of wind field power prediction, thereby improving the efficiency of wind field power prediction.
In another embodiment, before the step 101, an acquisition cycle of the acquisition terminal 200, a reporting time of the acquired first data, and a priority of the acquisition terminal 200 may be preset in the server 100. The server 100 controls the acquisition terminal 200 to acquire data according to a preset acquisition cycle, a preset reporting time and a preset priority.
In this embodiment, referring to fig. 5 in combination, in the step 102, the preprocessing the first data to obtain the second data specifically includes the following steps:
step 201: and processing the missing value of the first data to obtain first preprocessing data.
Step 202: and cleaning the first preprocessing data to obtain second preprocessing data.
Step 203: and normalizing the second preprocessing data to obtain third preprocessing data.
Step 204: and formatting the third preprocessed data to obtain second data.
Specifically, in step 201, the missing value processing is automatically performed by using a mathematical model, which includes, but is not limited to, an overhead mapping model, a homogeneous mean interpolation, a modeling prediction, and the like. In step 202, data points with obvious anomalies can be effectively cleaned by cleaning, and the embodiment cleans the first preprocessed data by using a random forest model in machine learning. In step 203, the second preprocessed data is normalized to [0,1] or other ranges by using a model such as minmaxscale. In step 204, the third preprocessed data is formatted into JSON or XML format, which can facilitate direct processing by computer software.
The first data is preprocessed to obtain the second data, the situation that data formats in the first data are disordered and nonuniform can be effectively improved through the preprocessing step, delay of data transmission is reduced, the transmission quantity of the data is reduced, meanwhile, the processing quantity of power prediction in a subsequent learning model is reduced, and efficiency is improved.
In this embodiment, the preset learning model in step 103 includes: an LSTM short-wavelength memory model or a deep reinforcement learning model. And training of the learning model is performed through historical wind power data, historical temperature data, historical humidity data, historical wind speed data and historical wind direction data of the wind field.
In the step 102, after receiving the first data acquired by the acquisition terminal 200, the method further includes: the first data is stored.
The embodiment of the present invention further provides a server 100, which includes a memory 130 and a processor 140, where the memory 130 stores a computer program, and the processor 140 implements the wind farm power prediction method when executing the computer program.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by the processor 140, the wind farm power prediction method is implemented.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A wind field power prediction method is applied to a server, wherein the server is in communication connection with an acquisition terminal, and the method comprises the following steps of;
receiving first data acquired by the acquisition terminal, wherein the first data comprises temperature data, humidity data, wind speed data and wind direction data;
preprocessing the first data to obtain second data;
predicting through a preset learning model according to the second data to obtain predicted power;
wherein the learning model is trained from historical wind power data, historical temperature data, historical humidity data, historical wind speed data, and historical wind direction data.
2. The method of claim 1, wherein the preprocessing the first data to obtain second data comprises:
processing the missing value of the first data to obtain first preprocessing data;
cleaning the first preprocessing data to obtain second preprocessing data;
normalizing the second preprocessed data to obtain third preprocessed data;
and formatting the third preprocessed data to obtain second data.
3. The wind farm power prediction method according to claim 1, wherein the learning model comprises: an LSTM short-wavelength memory model or a deep reinforcement learning model.
4. The method of claim 1, further comprising: storing the first data.
5. A wind farm power prediction system, comprising: the system comprises an acquisition terminal and a server, wherein the acquisition terminal is in communication connection with the server;
the acquisition terminal is used for acquiring first data, wherein the first data comprises temperature data, humidity data, wind speed data and wind direction data;
the server is used for receiving first data acquired by the acquisition terminal; preprocessing the first data to obtain second data; predicting through a preset learning model according to the second data to obtain predicted power; the learning model is formed by training historical wind power data, historical temperature data, historical humidity data, historical wind speed data and historical wind direction data.
6. The wind farm power prediction system of claim 5, wherein the server is communicatively coupled to the collection terminal via an RS485 bus.
7. The wind farm power prediction system of claim 5, wherein the server comprises a computing center and a service terminal, the computing center being communicatively connected to the service terminal;
the computing center is used for receiving first data acquired by the acquisition terminal; preprocessing the first data to obtain second data; sending the second data to the service terminal;
and the service terminal is used for receiving the second data and predicting through a preset learning model according to the second data to obtain predicted power.
8. The wind farm power prediction system of claim 7, wherein the computing center and the service terminal are communicatively coupled via a 5G network.
9. A server, characterized by comprising a memory storing a computer program and a processor implementing the steps of the method according to any of claims 1-4 when executing the computer program.
10. A computer-readable storage medium, on 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 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110794302.0A CN113449928A (en) | 2021-07-14 | 2021-07-14 | Wind field power prediction method, system, server and computer readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110794302.0A CN113449928A (en) | 2021-07-14 | 2021-07-14 | Wind field power prediction method, system, server and computer readable storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113449928A true CN113449928A (en) | 2021-09-28 |
Family
ID=77816170
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110794302.0A Pending CN113449928A (en) | 2021-07-14 | 2021-07-14 | Wind field power prediction method, system, server and computer readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113449928A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114139791A (en) * | 2021-11-24 | 2022-03-04 | 北京华能新锐控制技术有限公司 | Wind generating set power prediction method, system, terminal and storage medium |
| CN116150135A (en) * | 2022-12-27 | 2023-05-23 | 北京东润环能科技股份有限公司 | Wind measurement data processing method and device and electronic equipment |
| CN114006905B (en) * | 2021-10-27 | 2023-12-05 | 远景智能国际私人投资有限公司 | Information transmission method, device and system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110443725A (en) * | 2019-08-14 | 2019-11-12 | 国网甘肃省电力公司信息通信公司 | Wind power prediction model based on CNN+LSTM |
| CN111461444A (en) * | 2020-04-07 | 2020-07-28 | 上海电气风电集团股份有限公司 | Prediction method, system, medium and electronic device for unit power of wind power plant |
| CN112288157A (en) * | 2020-10-27 | 2021-01-29 | 华能酒泉风电有限责任公司 | Wind power plant power prediction method based on fuzzy clustering and deep reinforcement learning |
-
2021
- 2021-07-14 CN CN202110794302.0A patent/CN113449928A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110443725A (en) * | 2019-08-14 | 2019-11-12 | 国网甘肃省电力公司信息通信公司 | Wind power prediction model based on CNN+LSTM |
| CN111461444A (en) * | 2020-04-07 | 2020-07-28 | 上海电气风电集团股份有限公司 | Prediction method, system, medium and electronic device for unit power of wind power plant |
| CN112288157A (en) * | 2020-10-27 | 2021-01-29 | 华能酒泉风电有限责任公司 | Wind power plant power prediction method based on fuzzy clustering and deep reinforcement learning |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114006905B (en) * | 2021-10-27 | 2023-12-05 | 远景智能国际私人投资有限公司 | Information transmission method, device and system |
| CN114139791A (en) * | 2021-11-24 | 2022-03-04 | 北京华能新锐控制技术有限公司 | Wind generating set power prediction method, system, terminal and storage medium |
| CN116150135A (en) * | 2022-12-27 | 2023-05-23 | 北京东润环能科技股份有限公司 | Wind measurement data processing method and device and electronic equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Huan et al. | Design of water quality monitoring system for aquaculture ponds based on NB-IoT | |
| CN113449928A (en) | Wind field power prediction method, system, server and computer readable storage medium | |
| CN111601270A (en) | Internet of things power monitoring system and method of wireless sensor network topological structure | |
| CN104184745A (en) | Intelligent front-end equipment communication system | |
| CN108153623B (en) | Method and device for testing energy efficiency ratio of SATA interface hard disk | |
| CN107291211A (en) | The electric quantity consumption of application program determines method and device | |
| CN110362055A (en) | A kind of industrial data acquisition system that terminal is expansible and method | |
| CN105298751B (en) | The vertical axis wind power generation test device of survey is sentenced based on Distributed Detection and data | |
| Chen et al. | Big data collection gateway for vision-based smart meter reading network | |
| CN115329814B (en) | Low-voltage user link identification method and device based on image signal processing | |
| CN115825649A (en) | Cable fault point midpoint traveling wave distance measurement online positioning system based on current magnetic signal and parameter optimization variational modal decomposition | |
| CN114660504B (en) | Insulation monitoring loop detection method and device, electronic equipment and storage medium | |
| CN115684908A (en) | State monitoring method for power GIS equipment, storage medium and electronic device | |
| CN115713434A (en) | Cloud-edge-coordinated online calibration method and device for power load | |
| CN205015079U (en) | Little wind vibration of transmission line moves monitoring devices | |
| CN201637865U (en) | Network automatic meteorological station | |
| CN105221337B (en) | Vertical axis wind power generation monitoring device based on multiple networks fusion and distributed sensing | |
| CN111859802B (en) | Method, system and equipment for calculating steady-state temperature of overhead conductor | |
| Zhu et al. | Acquisition and communication system for condition data of transmission line of smart distribution network | |
| CN111586616A (en) | Regional environment parameter acquisition system, method and equipment | |
| CN110276855A (en) | Motor device intelligence supervisory systems and method based on technology of Internet of things | |
| CN111031099A (en) | Energy efficiency real-time monitoring and control management system | |
| CN212646862U (en) | Resident electricity consumption collection system | |
| CN115100843B (en) | Method, system and equipment for testing communication reliability of intelligent electric energy meter | |
| CN202855055U (en) | Real-time power negative control management system based on ARM (Advanced RISC Machine) and GPRS (General Packet Radio Service) |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210928 |