CN112116171A - Novel photovoltaic power generation power prediction method based on neural network - Google Patents
Novel photovoltaic power generation power prediction method based on neural network Download PDFInfo
- Publication number
- CN112116171A CN112116171A CN202011052430.XA CN202011052430A CN112116171A CN 112116171 A CN112116171 A CN 112116171A CN 202011052430 A CN202011052430 A CN 202011052430A CN 112116171 A CN112116171 A CN 112116171A
- Authority
- CN
- China
- Prior art keywords
- neural network
- photovoltaic power
- input
- power generation
- error
- 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
- 238000013528 artificial neural network Methods 0.000 title claims abstract description 56
- 238000000034 method Methods 0.000 title claims abstract description 20
- 238000010248 power generation Methods 0.000 title claims description 31
- 238000012937 correction Methods 0.000 claims abstract description 18
- 238000007781 pre-processing Methods 0.000 claims abstract description 16
- 210000002569 neuron Anatomy 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 8
- 230000006870 function Effects 0.000 claims description 7
- 238000005457 optimization Methods 0.000 claims description 3
- 230000000644 propagated effect Effects 0.000 claims description 3
- 210000000225 synapse Anatomy 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 230000002596 correlated effect Effects 0.000 abstract description 2
- 238000011161 development Methods 0.000 description 5
- 230000000875 corresponding effect Effects 0.000 description 4
- 239000003245 coal Substances 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000003062 neural network model Methods 0.000 description 2
- 239000003208 petroleum Substances 0.000 description 2
- 238000010521 absorption reaction Methods 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 210000004027 cell Anatomy 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- BTCSSZJGUNDROE-UHFFFAOYSA-N gamma-aminobutyric acid Chemical compound NCCCC(O)=O BTCSSZJGUNDROE-UHFFFAOYSA-N 0.000 description 1
- 210000002364 input neuron Anatomy 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000012795 verification 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
- G06N3/045—Combinations of 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
- G06N3/084—Backpropagation, e.g. using gradient descent
-
- 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
- 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)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- Software Systems (AREA)
- Tourism & Hospitality (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Computational Linguistics (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- General Business, Economics & Management (AREA)
- Molecular Biology (AREA)
- Artificial Intelligence (AREA)
- Marketing (AREA)
- Life Sciences & Earth Sciences (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- Public Health (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Photovoltaic Devices (AREA)
Abstract
The invention relates to a photovoltaic power prediction algorithm, which can be used for output power prediction of a single photovoltaic panel and also can be used for output power prediction of a photovoltaic station. Specifically, an improved neural network is used for modeling, power is used as the output of the neural network, and the input is divided into two parts: the first part is that the quantity which is correlated with the power is used as the input, and a correction factor based on the prediction error of the first five minutes is added, and the second part is that the data correlation of the cloud cover coefficient and the relative temperature, the rainfall and the time is found by utilizing a fuzzy preprocessing tool box, and the cloud cover coefficient is obtained and used as the input quantity. The method adopts an error correction factor and a fuzzy preprocessing method, and improves the accuracy of power prediction.
Description
Technical Field
The invention relates to a photovoltaic power prediction algorithm, which can be used for output power prediction of a single photovoltaic panel and also can be used for output power prediction of a photovoltaic station.
Background
At present, the traditional coal energy is increasingly exhausted, the price of petroleum is continuously increased, and meanwhile, people pay more attention to environmental protection, so that people have urgent needs for renewable energy. Photovoltaic power generation is to convert solar energy into electric energy, and solar energy is clean, environment-friendly and renewable clean energy. Under the condition of conventional energy shortage at present, the development of the photovoltaic industry can prevent people from depending on non-renewable energy such as petroleum, coal and the like, so that the effects of maintaining ecological balance and adjusting energy structures are achieved.
In view of the current development situation of the global photovoltaic power generation industry, due to the increasing importance of the world countries on the sustainable development concept, the scale of global photovoltaic power generation is rapidly expanding. With the continuous development of electric power technology, the cost of photovoltaic power generation is remarkably reduced, and the price of photovoltaic power generation products is also continuously reduced. At present, photovoltaic power generation projects are actively promoted in countries in many regions in the world, more and more investors participate in the photovoltaic market, and the global photovoltaic market is developing towards diversification. From the overseas market loading perspective, there are an increasing number of projects loading in excess of one billion watts per year. The competitiveness of photovoltaic power generation in the market is gradually improved, and the photovoltaic power generation is likely to become the most popular new energy technology in the future. One of the key problems limiting the development of photovoltaic power generation at present is the problem of predicting the power of photovoltaic power generation.
Firstly, the accurate prediction of the photovoltaic power can improve the stability of the power grid and increase the photoelectric capacity of the power grid. The photovoltaic power generation has intermittence, randomness and fluctuation, so that a series of problems are brought to the safe operation of a power grid, and the traditional method of a power grid dispatching department can only adopt the action of pulling a gate and limiting the power. With the increase of the proportion of the power structure of the power grid of the photovoltaic power station, a photovoltaic power prediction system becomes more important, the more accurate the photovoltaic power prediction is, the smaller the influence of the photovoltaic grid connection on the safe operation of the power grid is, and the scheduling plan of various power supplies can be effectively made by a power grid scheduling department.
And secondly, the photovoltaic power station is helped to reduce economic loss caused by power limiting, and the operation management efficiency of the photovoltaic power station is improved. The more accurate the photovoltaic power prediction is, the more the photovoltaic power is, the less the photovoltaic power limitation is, so that the sunlight absorption capacity of the power grid is greatly improved, the economic loss of photovoltaic owners caused by power limitation is reduced, and the investment return rate of photovoltaic power stations is increased.
The method predicts the power based on the artificial neural network, adds an error correction factor and a fuzzy preprocessing method, and more accurately predicts the photovoltaic output power.
Disclosure of Invention
The invention relates to a photovoltaic output power prediction algorithm, which utilizes error feedback as the input of the next stage to improve a power prediction neural network, and specifically utilizes the improved neural network to carry out modeling, the power is used as the output of the neural network, the input is divided into two parts, the first part is a quantity which is correlated with the power and is used as the input, a correction factor for predicting errors based on the first five minutes is added, and the second part is a fuzzy preprocessing tool box which is used for finding out the data correlation between a cloud coefficient and relative humidity, rainfall and time to obtain the cloud coefficient as the input quantity. The method adopts an error correction factor and a fuzzy preprocessing method, and improves the accuracy of power prediction.
In order to achieve the above object, the present invention provides the following methods:
a photovoltaic power generation power prediction method based on a neural network is characterized by comprising the following steps:
s1, acquiring historical data required by photovoltaic power generation power prediction;
s2, constructing a photovoltaic power generation power prediction model based on the improved neural network, and training the improved neural network by adopting a least square optimization algorithm to obtain the photovoltaic power generation power prediction model;
s3, the input part of the photovoltaic power generation power prediction model built based on the improved neural network comprises two parts: the first part is to take the historical data as input and add a prediction error correction factor based on the previous five minutes; the second part is that a fuzzy preprocessing tool box is utilized to find out the data correlation of the cloud coefficient with humidity, rainfall and time, the obtained cloud coefficient is used as an input quantity, and the power of the photovoltaic power generation power prediction model constructed based on the improved neural network is output of the neural network;
and S4, outputting the prediction result of the photovoltaic power generation power.
Preferably, the historical data of step S1 includes irradiance, temperature, humidity, air pressure, wind speed, wind direction as one to six inputs to the neural network.
Preferably, the photovoltaic power generation power prediction model of step S2 is composed of three parts, i.e., an input layer, a hidden layer and an output layer, wherein the hidden layer is composed of different numbers of neurons, and the neuron model is composed of a group of connected links called synapses, each with its own weight, and the amplitude range of the output signal is reduced to a finite value by calculation.
Preferably, the error correction factor predicted in the first five minutes in step S3 is used as a seventh input of the neural network, and the error correction factor is obtained based on a calculation error formula and fed back to the input layer to predict the photovoltaic power.
Preferably, the fuzzy preprocessing toolbox in step S3 determines the positive correlation between the rainfall coefficient and the humidity, rainfall and time data by using the fuzzy preprocessing toolbox of the MATLAB itself, and obtains the cloud coefficient as the eighth input quantity of the neural network, so as to be used for accurately predicting the photovoltaic power by the neural network.
Preferably, the error between the output of the neural network and the actual photovoltaic output power is obtained through calculation by the prediction error correction factor of the first five minutes, and the obtained error is propagated from the output layer back to the input layer of the neural network, so that the size of the error predicted at the last moment can be known by the neural network at any time, and the prediction error of the neural network in the next five minutes can be reduced.
Preferably, the humidity, the rainfall and the time are all triangular membership functions, fuzzy division is respectively carried out according to the corresponding maximum and minimum values in the historical data, each division corresponds to one fuzzy subset, and the positive correlation among the humidity, the rainfall and the time and the negative correlation among the humidity, the rainfall and the irradiance are more accurately obtained.
The invention discloses the following technical effects:
1. and calculating a prediction error based on prediction data obtained in the first five minutes according to an error calculation formula, and returning the prediction error to the input layer of the neural network to be used as the input of prediction at the next moment and used as an error correction factor for correcting the neural network. The neural network can monitor the prediction error at a moment, so that the prediction at the next moment is more accurate.
2. The cloud covering amount has great correlation with irradiance, so that the correlation between a rainfall coefficient and three data of relative temperature, rainfall and time is found by taking the fuzzy logic theory into consideration and utilizing a fuzzy preprocessing tool box carried by MATLAB, the cloud coefficient is obtained and used as the input quantity of the neural network, and the prediction of the neural network on the photovoltaic power is further accurate.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic diagram of the overall structure of a neural network according to the present invention;
FIG. 2 is a flow chart of the fuzzy pre-processing of the present invention;
FIG. 3 is a schematic diagram of a fuzzy controller according to the present invention.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. 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.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
The invention relates to a photovoltaic output power prediction algorithm, which utilizes error feedback as the input of the next stage to improve a power prediction neural network, and particularly utilizes the improved neural network for modeling, obtains a cloud coefficient as one input of an input layer based on fuzzy logic processing, and feeds back an obtained error factor to the input layer as one input thereof based on a calculation error formula to predict the photovoltaic power.
The predicted body part provides a three-layer (input, hidden, and output) feedforward and back-propagation model. A least square (Levenberg-Marquardt) optimization method is adopted as a neural network training algorithm. The neural network shown in fig. 1 is composed of an input layer, a hidden layer and an output layer. The input layer has 8 input quantities: irradiance, temperature, wind speed, wind direction, air pressure, humidity, error correction factors and cloud cover coefficients; the output layer has an output: power; the hidden layer is composed of different numbers of neurons. The number of neurons in the hidden layer generally needs to be determined according to specific problems.
The neuron model used to design many neural network models consists of a set of connected links called synapses, each with its own weight wkj. This weight is multiplied by its own input vector xiThen all weighted inputs are combined with an external bias
(error adjustment term) addition, the latter being responsible for reducing or increasing the summed output signal
Then activate function f2Applied to the output to output the signal
Is reduced to a limited value. Input vector "x ═ irradiance, temperature, wind speed, wind direction, barometric pressure, humidity, error correction factor, cloud cover coefficient]"applied to the input layer of the network. The net input to the jth hidden unit is:
wherein wjiThe weight on the ith input cell connection,
representing the error of the hidden layer neurons. When the neural network is trained, the input and the output are known quantities, and the error quantity obtained by calculating the result obtained each time and the actual result is the error of the hidden layer neuron.
The output of the hidden layer neurons is:
the net inputs to the output layer neurons are noted as:
wherein wkjThe weight on the jth input neuron,
error for the second layer neurons is represented.
Second layer
The outputs of (1) are the net outputs we have found last, these outputs are labeled yk。
f2(n)＝purelin(n)＝n (6)
As shown in fig. 2, a fuzzy preprocessing tool box is introduced into the neural system model to fuzzify the input and output variables: the method comprises the steps of converting input and output accurate quantities into fuzzy sets corresponding to linguistic variables, using membership functions in different areas, and obtaining the relation between relative humidity and rainfall at different times after the membership functions are set by using matlab so as to search the data correlation between the humidity, the rainfall and the time of the day. Classification of cloud indices into neural networks (i)8) To the other input. The fuzzy preprocessing comprehensively considers the influence of humidity, rainfall and time on irradiance, simplifies the input of a neural network, and simultaneously more accurately obtains the positive correlation between the humidity and the rainfall and the negative correlation between the humidity, the rainfall and the irradiance.
Three input variables, humidity, rainfall and time, are selected, and the data are divided into sample data and verification data. To ensure the randomness of the results, 20 percent of the data is randomly selected as sample data.
The three variables are all selected from a triangular membership function, fuzzy division is respectively carried out according to the corresponding maximum and minimum values in the sample data, and each division corresponds to a fuzzy subset (as shown in figure 2). 3 fuzzy language variable values of low, normal and high are taken for humidity, rainfall and time. The meteorological factors also select 3 fuzzy language variable values: low, normal, and high, as shown in fig. 3, all of the three variables are selected from a triangular membership function, and fuzzy division is performed according to the corresponding maximum and minimum values in the sample data.
Calculating an error correction factor, proposing an error (n) between the output of the neural network and the actual photovoltaic output powerthInterval), the calculated error is output from the inputThe output layer is propagated back to the input layer of the neural network, so that the neural network can know the predicted error at any time, the network can be automatically adjusted, the weight value between input quantities can be corrected, and the prediction (n + 1) of the neural network in the next 5 minutes can be reducedthInterval) of the prediction error. Error correction factor i for improving neural network model7The inputs of (a) are:
error factor calculation formula:
wherein m represents the number of samples, AtIs the predicted value of the photovoltaic power, FtIs the value of the actual measured photovoltaic power.
The fuzzy preprocessing comprehensively considers the influence of relative humidity, rainfall and time on irradiance, simplifies the input of a neural network, and simultaneously obtains the relation between the common relation among the relative humidity, the rainfall and the time and the irradiance more accurately.
The above-described embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solutions of the present invention can be made by those skilled in the art without departing from the spirit of the present invention, and the technical solutions of the present invention are within the scope of the present invention defined by the claims.
Claims (7)
1. A photovoltaic power generation power prediction method based on a neural network is characterized by comprising the following steps:
s1, acquiring historical data required by photovoltaic power generation power prediction;
s2, constructing a photovoltaic power generation power prediction model based on the improved neural network, and training the improved neural network by adopting a least square optimization algorithm to obtain the photovoltaic power generation power prediction model;
s3, the input part of the photovoltaic power generation power prediction model built based on the improved neural network comprises two parts: the first part is to take the historical data as input and add a prediction error correction factor based on the previous five minutes; the second part is that a fuzzy preprocessing tool box is utilized to find out the data correlation of the cloud coefficient with humidity, rainfall and time, the obtained cloud coefficient is used as an input quantity, and the power of the photovoltaic power generation power prediction model constructed based on the improved neural network is output of the neural network;
and S4, outputting the prediction result of the photovoltaic power generation power.
2. The method according to claim 1, wherein the historical data of step S1 includes irradiance, temperature, humidity, air pressure, wind speed, and wind direction as one to six inputs of the neural network.
3. The method according to claim 1, wherein the photovoltaic generation power prediction model of step S2 is composed of three parts, i.e. an input layer, a hidden layer and an output layer, wherein the hidden layer is composed of different numbers of neurons, and the neuron model is composed of a set of connected links called synapses, each with its own weight, and the amplitude range of the output signal is reduced to a finite value by calculation.
4. The method for predicting photovoltaic power generation according to claim 1, wherein the error correction factor predicted in the first five minutes in step S3 is used as a seventh input of the neural network, and the error correction factor is obtained based on a calculation error formula and fed back to the input layer to predict the photovoltaic power.
5. The method for predicting photovoltaic power generation based on neural network as claimed in claim 1, wherein the fuzzy preprocessing toolbox of step S3 is to determine the positive correlation between the rainfall coefficient and the humidity, rainfall and time data by using the fuzzy preprocessing toolbox of MATLAB itself, and obtain the cloud coefficient as the eighth input of the neural network, so as to be used for predicting the photovoltaic power by the neural network accurately.
6. The method according to claim 4, wherein the error correction factor is obtained by calculating the error between the output of the neural network and the actual photovoltaic output power in the first five minutes, and the obtained error is propagated from the output layer back to the input layer of the neural network, so that the neural network knows the size of the error predicted in the last moment at any time, and the error is used for reducing the prediction error of the neural network in the next five minutes.
7. The photovoltaic power generation power prediction method based on the neural network as claimed in claim 6, wherein the humidity, the rainfall and the time are all triangular membership functions, fuzzy division is respectively performed according to the corresponding maximum and minimum values in the historical data, each division corresponds to one fuzzy subset, and the positive correlation among the humidity, the rainfall and the time and the negative correlation among the humidity, the rainfall and the irradiance are more accurately obtained.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011052430.XA CN112116171A (en) | 2020-09-29 | 2020-09-29 | Novel photovoltaic power generation power prediction method based on neural network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011052430.XA CN112116171A (en) | 2020-09-29 | 2020-09-29 | Novel photovoltaic power generation power prediction method based on neural network |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112116171A true CN112116171A (en) | 2020-12-22 |
Family
ID=73798454
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011052430.XA Pending CN112116171A (en) | 2020-09-29 | 2020-09-29 | Novel photovoltaic power generation power prediction method based on neural network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112116171A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113065982A (en) * | 2021-04-22 | 2021-07-02 | 东北电力大学 | System and method for predicting power of dust-accumulated photovoltaic panel based on dense residual error network |
| CN116050187A (en) * | 2023-03-30 | 2023-05-02 | 国网安徽省电力有限公司电力科学研究院 | TS fuzzy outlier self-correction method and system for second-level photovoltaic power prediction |
| WO2023178328A3 (en) * | 2022-03-17 | 2024-04-25 | Utopus Insights, Inc. | Systems and methods for ramp predictions for forecasting power using neighboring sites |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104268638A (en) * | 2014-09-11 | 2015-01-07 | 广州市香港科大霍英东研究院 | Photovoltaic power generation system power predicting method of elman-based neural network |
| CN106067077A (en) * | 2016-06-01 | 2016-11-02 | 新奥泛能网络科技股份有限公司 | A kind of load forecasting method based on neutral net and device |
| CN106961249A (en) * | 2017-03-17 | 2017-07-18 | 广西大学 | A kind of diagnosing failure of photovoltaic array and method for early warning |
| CN110148068A (en) * | 2019-05-23 | 2019-08-20 | 福州大学 | One kind being based on meteorological data similarity analysis and LSTM neural fusion photovoltaic plant ultra-short term power forecasting method |
-
2020
- 2020-09-29 CN CN202011052430.XA patent/CN112116171A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104268638A (en) * | 2014-09-11 | 2015-01-07 | 广州市香港科大霍英东研究院 | Photovoltaic power generation system power predicting method of elman-based neural network |
| CN106067077A (en) * | 2016-06-01 | 2016-11-02 | 新奥泛能网络科技股份有限公司 | A kind of load forecasting method based on neutral net and device |
| CN106961249A (en) * | 2017-03-17 | 2017-07-18 | 广西大学 | A kind of diagnosing failure of photovoltaic array and method for early warning |
| CN110148068A (en) * | 2019-05-23 | 2019-08-20 | 福州大学 | One kind being based on meteorological data similarity analysis and LSTM neural fusion photovoltaic plant ultra-short term power forecasting method |
Non-Patent Citations (3)
| Title |
|---|
| 刘引鸽等: "我国总云量时空特征及其影响因素分析", 《宝鸡文理学院学报》, vol. 34, no. 1, pages 3 * |
| 徐继业等: "《气象大数据》", 上海科学技术出版社, pages: 111 * |
| 算法工程师的学习日志: "matlab模糊控制工具箱使用和模糊控制pid实例参考（一）", pages 1, Retrieved from the Internet <URL:https://mp.weixin.qq.com/s/tiAMlRVn3rmk8M9RkuZtOg> * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113065982A (en) * | 2021-04-22 | 2021-07-02 | 东北电力大学 | System and method for predicting power of dust-accumulated photovoltaic panel based on dense residual error network |
| WO2023178328A3 (en) * | 2022-03-17 | 2024-04-25 | Utopus Insights, Inc. | Systems and methods for ramp predictions for forecasting power using neighboring sites |
| CN116050187A (en) * | 2023-03-30 | 2023-05-02 | 国网安徽省电力有限公司电力科学研究院 | TS fuzzy outlier self-correction method and system for second-level photovoltaic power prediction |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112116171A (en) | Novel photovoltaic power generation power prediction method based on neural network | |
| CN112215428B (en) | Photovoltaic power generation power prediction method and system based on error correction and fuzzy logic | |
| CN111008728A (en) | Method for predicting short-term output of distributed photovoltaic power generation system | |
| CN112036611B (en) | Power grid optimization planning method considering risks | |
| Akinci | Short term wind speed forecasting with ANN in Batman, Turkey | |
| CN111027775A (en) | Step hydropower station generating capacity prediction method based on long-term and short-term memory network | |
| CN110880789A (en) | Economic dispatching method for wind power and photovoltaic combined power generation system | |
| Ashraf et al. | Artificial neural network based models for forecasting electricity generation of grid connected solar PV power plant | |
| CN114707767B (en) | New energy power system low-valley period adjustable peak power prediction method | |
| CN104050517A (en) | Photovoltaic power generation forecasting method based on GRNN | |
| Yongsheng et al. | A short-term power output forecasting model based on correlation analysis and ELM-LSTM for distributed PV system | |
| CN112101626A (en) | Distributed photovoltaic power generation power prediction method and system | |
| Mukherjee et al. | Solar irradiance prediction from historical trends using deep neural networks | |
| Yadav et al. | Short-term pv power forecasting using adaptive neuro-fuzzy inference system | |
| Galphade | Electrical characterization of a photovoltaic module through artificial neural network: a review | |
| CN113393119B (en) | Stepped hydropower short-term scheduling decision method based on scene reduction-deep learning | |
| CN115765044A (en) | Wind, light and water power system combined operation and risk analysis method and system | |
| CN114638396A (en) | Photovoltaic power prediction method and system based on neural network instantiation | |
| Sulaiman et al. | Optimizing three-layer neural network model for grid-connected photovoltaic system output prediction | |
| Mahmudah et al. | Photovoltaic Power Forecasting Using Cascade Forward Neural Network Based On Levenberg-Marquardt Algorithm | |
| Zhang et al. | A Convolutional Neural Network for Regional Photovoltaic Generation Point Forecast | |
| CN111915084A (en) | Hybrid photovoltaic power generation power prediction method and system based on neural network | |
| Mellit et al. | Application of neural networks and genetic algorithms for predicting the optimal sizing coefficient of photovoltaic supply (PVS) systems | |
| Wang et al. | A new improved combined model algorithm for the application of photovoltaic power prediction | |
| CN112734073A (en) | Photovoltaic power generation short-term prediction method based on long and short-term memory network |
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 |