CN104268638A - Photovoltaic power generation system power predicting method of elman-based neural network - Google Patents
Photovoltaic power generation system power predicting method of elman-based neural network Download PDFInfo
- Publication number
- CN104268638A CN104268638A CN201410462330.2A CN201410462330A CN104268638A CN 104268638 A CN104268638 A CN 104268638A CN 201410462330 A CN201410462330 A CN 201410462330A CN 104268638 A CN104268638 A CN 104268638A
- Authority
- CN
- China
- Prior art keywords
- neural network
- elman
- data
- output
- hidden layer
- 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 71
- 238000000034 method Methods 0.000 title claims abstract description 34
- 238000010248 power generation Methods 0.000 title claims abstract description 13
- 210000002569 neuron Anatomy 0.000 claims abstract description 10
- 238000013277 forecasting method Methods 0.000 claims description 15
- 238000004364 calculation method Methods 0.000 claims description 4
- 230000001537 neural effect Effects 0.000 claims description 4
- 210000004205 output neuron Anatomy 0.000 claims description 4
- 230000008901 benefit Effects 0.000 abstract description 4
- 238000010606 normalization Methods 0.000 abstract 2
- 230000006870 function Effects 0.000 description 13
- 230000008569 process Effects 0.000 description 6
- 241000196324 Embryophyta Species 0.000 description 5
- 230000005611 electricity Effects 0.000 description 3
- 238000013473 artificial intelligence Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 230000001172 regenerating effect Effects 0.000 description 2
- 241001269238 Data Species 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 230000005622 photoelectricity Effects 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Classifications
-
- 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
- G06N3/082—Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
-
- 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
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A30/00—Adapting or protecting infrastructure or their operation
-
- 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)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Computation (AREA)
- Biophysics (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Data Mining & Analysis (AREA)
- Economics (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Photovoltaic Devices (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a photovoltaic power generation system power predicting method of an elman-based neural network. The method comprises the steps of obtaining generated power historical data and corresponding historical weather parameter information of photovoltaic power generation equipment at the related area, determining input data and output data of the neural network, determining the optimal number of hidden layer neurons, building the elman-based neural network accordingly, carrying out normalization processing on the generated power historical data and the historical weather parameter information, training the built neural network according to the data obtained after normalization processing is carried out, controlling prediction errors of the elman-based neural network to be within the preset range accordingly, regarding generated power historical data of one week before the prediction day and weather parameter data of the prediction day as input, and predicting the generated power of the prediction day through the trained neural network. The method has the advantages of being stable and high in time-varying adaptability and prediction precision, and can be widely applied to the field of photovoltaic power generation.
Description
Technical field
The present invention relates to field of photovoltaic power generation, especially a kind of photovoltaic generating system power forecasting method based on elman neural network.
Background technology
Renewable energy power generation is comparatively efficient and clean renewable energy utilization mode, is also one of the most ripe in current regenerative resource operation technique, mode most with scale exploit condition and commercialized development prospect.Photovoltaic generation is then the main Land use systems of regenerative resource, is the chief component of intelligent grid.The prediction of short-term power generation power is then the key whether photovoltaic generation can successfully be promoted, and Ye Shi power scheduling department formulates the foundation of power scheduling plan, the important leverage of the self-built photovoltaic generating system benefit such as family or enterprise especially.
And current all short-term solar energy power generating Forecasting Methodologies are all based on identical thinking: first utilize mathematics and physics theory and related data to set up predictor formula or model, then by predictor formula or model, photovoltaic power station power generation amount is predicted.According to adopted theory of mathematical physics and prediction output quantity thereof, photovoltaic generation Forecasting Methodology can be divided into two large classes: (1) directly predicts the direct forecast methods (being statistic law again) of electro-optical system output power; (2) first solar radiation is predicted, then obtain the indirect predictions method (being Physical again) of photoelectric yield power according to photoelectric transformation efficiency.
The Forecasting Methodology of Corpus--based Method method has the methods such as probabilistic method, time series method and artificial intelligence method, and its advantage is that program is simple and clear, to photovoltaic plant position and the not requirement of electric power conversion parameter; Shortcoming does not consider to affect the environmental factor of photovoltaic generation, needs a large amount of photovoltaic plant history datas to ensure the degree of accuracy of forecast result, and easily cause the undulatory property of precision of prediction excessive because of the change of environment.The Forecasting Methodology of physically based deformation method is mainly based on photovoltaic generating system physics electricity generating principle.Its advantage does not need history data, just can directly predict after photovoltaic plant builds up; Shortcoming needs the data such as the detailed topomap of photovoltaic plant, power house coordinate, photovoltaic plant powertrace and other relative photo electricity conversion parameter.
At present widely used is in the industry Forecasting Methodology (one of artificial intelligence method) based on BP neural network, but the Forecasting Methodology based on BP neural network still exists following defect:
(1) only have feedforward and without feedback, excessively poor to the susceptibility of historical data, easily cause the information of the mode of learning remembered to disappear, stable not;
(2) multidate information ability is processed excessively weak, cannot the direct characteristic of photovoltaic generating system in dynamic reflection dynamic process, do not possess the ability of adaptation time-varying characteristics, and the undulatory property of precision of prediction is larger.
Summary of the invention
In order to solve the problems of the technologies described above, the object of the invention is: provide a kind of and stablize, have adapt to time-varying characteristics ability and precision of prediction high, based on the photovoltaic generating system power forecasting method of elman neural network.
The technical solution adopted for the present invention to solve the technical problems is:
Based on a photovoltaic generating system power forecasting method for elman neural network, comprising:
A, obtain at the generated output historical data of relevant regional photovoltaic power generation equipment and corresponding weather history parameter information;
B, determine the input and output data of neural network, and determine optimum hidden layer neuron number, thus the neural network set up based on elman, the described neural network based on elman comprises input layer, hidden layer, undertaking layer and output layer, and described undertaking layer is for remembering the output valve of hidden layer previous moment and this output valve being returned to the input of hidden layer;
C, generated output historical data and weather history parameter information to be normalized, then according to the data after normalized, the neural network set up is trained, thus the predicated error of the neural network based on elman is controlled in the scope preset;
D, using the generated output historical data predicting day the last week and prediction day weather parameters data as input, adopt training after neural network to prediction day generated output predict.
Further, described default scope is 5%-10%.
Further, described generated output historical data comprises generated output per hour and effective generating dutation section, and described weather history parameter information comprises temperature, air pressure, wind direction, wind speed, cloud amount, rainfall, sunshine-duration and weather pattern.
Further, described step B, it comprises:
The generated output historical data that B1, statistics obtain and weather history parameter information, using the actual power power of one day as the output data of neural network, using this day the last week generated output W per hour in effective time section f and the weather parameters data of this day as the input data of neural network;
B2, initialization is carried out to elman neural network, input node unit vector, hidden layer node unit vector, feedback states vector sum output node vector is determined according to input and output sequence, thus the neural metwork training model set up based on elman, wherein, the nodes of hidden layer draws by increasing progressively method of trial and error gradually.
Further, the non-linear state space expression formula of described elman neural network is:
，
Wherein,
yfor m ties up output node vector;
lfor
ndimension hidden layer node unit vector;
xfor u ties up input vector;
cfor n ties up feedback states vector; w
3for hidden layer connects weights to output layer; w
2for input layer connects weights to hidden layer; w
1for accepting the connection weights of layer to hidden layer;
g(*) be the transport function of output neuron;
f(*) be the transport function of hidden layer neuron.
Further, described step C, it comprises:
C1, employing minimax method are normalized generated output historical data and weather history parameter information, and the formula of described normalized is:
，
Wherein,
x maxfor the maximum number in data sequence,
x minfor the minimum number in data sequence;
C2, according to the data after normalized, error calculation, right value update and threshold values are carried out to the neural network set up and upgrade, thus the predicated error of the neural network based on elman to be controlled in the scope of 5%-10%.
Further, described elman neural network adopts BP algorithm to carry out modified weight renewal, and adopts sum of squared errors function to carry out target function study, described target function
e (w)the formula of study is:
，
Wherein,
for target input vector.
The invention has the beneficial effects as follows: by the structure of Elman neural network, in conjunction with at the corresponding weather history parameter information of relevant regional photovoltaic power generation equipment generated output historical data, obtain the generated output predicting day, wherein, Elman neural network comprises input layer, hidden layer, output layer and for the output valve of remembering hidden layer previous moment and layer is accepted in the input this output valve being returned to hidden layer, add feedback, comparatively responsive to historical data, comparatively stable; Need when training to carry out control errors, any Nonlinear Mapping can be approached with arbitrary accuracy, do not consider the impact of external noise on system, thus make system have higher precision, and there is the ability adapting to time-varying characteristics, the characteristic of the direct dynamic reflection dynamic process system of energy, decreases the undulatory property of precision of prediction.
Accompanying drawing explanation
Below in conjunction with drawings and Examples, the invention will be further described.
Fig. 1 is the overall flow figure of a kind of photovoltaic generating system power forecasting method based on elman neural network of the present invention;
Fig. 2 is the structural representation of the neural network that the present invention is based on elman;
Fig. 3 is the process flow diagram of step B of the present invention;
Fig. 4 is the process flow diagram of step C of the present invention.
Embodiment
See figures.1.and.2, a kind of photovoltaic generating system power forecasting method based on elman neural network, comprising:
A, obtain at the generated output historical data of relevant regional photovoltaic power generation equipment and corresponding weather history parameter information;
B, determine the input and output data of neural network, and determine optimum hidden layer neuron number, thus the neural network set up based on elman, the described neural network based on elman comprises input layer, hidden layer, undertaking layer and output layer, and described undertaking layer is for remembering the output valve of hidden layer previous moment and this output valve being returned to the input of hidden layer;
C, generated output historical data and weather history parameter information to be normalized, then according to the data after normalized, the neural network set up is trained, thus the predicated error of the neural network based on elman is controlled in the scope preset;
D, using the generated output historical data predicting day the last week and prediction day weather parameters data as input, adopt training after neural network to prediction day generated output predict.
Be further used as preferred embodiment, described default scope is 5%-10%.
Be further used as preferred embodiment, described generated output historical data comprises generated output per hour and effective generating dutation section, and described weather history parameter information comprises temperature, air pressure, wind direction, wind speed, cloud amount, rainfall, sunshine-duration and weather pattern.
With reference to Fig. 3, be further used as preferred embodiment, described step B, it comprises:
The generated output historical data that B1, statistics obtain and weather history parameter information, using the actual power power of one day as the output data of neural network, using this day the last week generated output W per hour in effective time section f and the weather parameters data of this day as the input data of neural network;
B2, initialization is carried out to elman neural network, input node unit vector, hidden layer node unit vector, feedback states vector sum output node vector is determined according to input and output sequence, thus the neural metwork training model set up based on elman, wherein, the nodes of hidden layer draws by increasing progressively method of trial and error gradually.
Be further used as preferred embodiment, the non-linear state space expression formula of described elman neural network is:
，
Wherein,
yfor m ties up output node vector;
lfor
ndimension hidden layer node unit vector;
xfor u ties up input vector;
cfor n ties up feedback states vector; w
3for hidden layer connects weights to output layer; w
2for input layer connects weights to hidden layer; w
1for accepting the connection weights of layer to hidden layer;
g(*) be the transport function of output neuron;
f(*) be the transport function of hidden layer neuron.
With reference to Fig. 4, be further used as preferred embodiment, described step C, it comprises:
C1, employing minimax method are normalized generated output historical data and weather history parameter information, and the formula of described normalized is:
，
Wherein,
x maxfor the maximum number in data sequence,
x minfor the minimum number in data sequence;
C2, according to the data after normalized, error calculation, right value update and threshold values are carried out to the neural network set up and upgrade, thus the predicated error of the neural network based on elman to be controlled in the scope of 5%-10%.
Be further used as preferred embodiment, described elman neural network adopts BP algorithm to carry out modified weight renewal, and adopts sum of squared errors function to carry out target function study, described target function
e (w)the formula of study is:
，
Wherein,
for target input vector.
Below in conjunction with Figure of description and specific embodiment, the present invention is described in further detail.
Embodiment one
With reference to Fig. 2, the first embodiment of the present invention:
The present invention adopts the forecast model based on Elman neural network to realize the short-term forecasting of photovoltaic system electricity generation power, and the structure of Elman neural network model as shown in Figure 2.Wherein, X
1, X
2xu is the node of input layer, the prediction day weather parameters and upper one week photovoltaic system electricity generation power of corresponding input; Y
1the node of output layer, the corresponding prediction day system generated output exported; l
1, l
2l
nthe node of hidden layer, the hidden layer neuron number that wherein node in hidden layer n(is namely optimum) determine according to the way increasing exploration gradually; C
1, C
2c
nbe accept the node of layer, be used for remembering the output valve of hidden layer unit previous moment and return to the input of hidden layer.
The non-linear state space expression formula of this Elman neural network is:
Wherein, g(*) be the transport function of output neuron, be the linear combination that hidden layer exports; F(*) be the transport function of hidden layer neuron, often adopt S function.
Embodiment two
With reference to Fig. 1-4, the second embodiment of the present invention:
The main implementation step of photovoltaic generating system power forecasting method of the present invention is as follows:
Step 1, obtains in relevant regional photovoltaic power generation equipment generated output historical data, comprises generated output W per hour and effective generating dutation section f, thus obtains effective generating dutation section of prediction generated output;
Step 2, obtains corresponding weather history parameter information, includes but not limited to temperature T, air pressure P, wind direction WD, wind speed WS, cloud amount C, rainfall R, sunshine-duration t and weather pattern P;
Step 3, the generated output historical data that statistics obtains and weather history parameter information, using actual power power on the one output data as neural network, using this day the last week generated output W per hour in time period effective time f and the weather parameters data of prediction day as input data;
Step 4, netinit, determine u dimension input node unit vector x according to input and output sequence (X, Y), n ties up hidden layer node unit vector l, n ties up feedback states vector c, m ties up output node vector y, and wherein, node in hidden layer n determines optimum hidden layer neuron number according to the way increasing exploration gradually, until stop when network performance reaches threshold value or the optimum of setting, thus set up the neural metwork training model based on Elman;
Step 5, minimax method is used to be normalized generated output historical data and weather parameters historical information, recycle it to train (comprising the process that error calculation, right value update and threshold values upgrade) neural network, the predicated error of network is controlled in 5% ~ 10%;
Step 6, can utilize neural network to carry out the generated power forecasting of prediction day after having trained, thus obtains the result of prediction.
Compared with prior art, the present invention is by a kind of photovoltaic generating system power forecasting method based on Elman neural network, set up photovoltaic generating system neural network power prediction model, any Nonlinear Mapping can be approached with arbitrary precision, do not consider the impact of external noise on system, make system have higher precision, and there is the ability adapting to time-varying characteristics, the characteristic of the direct dynamic reflection dynamic process system of energy.
More than that better enforcement of the present invention is illustrated, but the invention is not limited to described embodiment, those of ordinary skill in the art also can make all equivalent variations or replacement under the prerequisite without prejudice to spirit of the present invention, and these equivalent distortion or replacement are all included in the application's claim limited range.
Claims (7)
1., based on a photovoltaic generating system power forecasting method for elman neural network, it is characterized in that: comprising:
A, obtain at the generated output historical data of relevant regional photovoltaic power generation equipment and corresponding weather history parameter information;
B, determine the input and output data of neural network, and determine optimum hidden layer neuron number, thus the neural network set up based on elman, the described neural network based on elman comprises input layer, hidden layer, undertaking layer and output layer, and described undertaking layer is for remembering the output valve of hidden layer previous moment and this output valve being returned to the input of hidden layer;
C, generated output historical data and weather history parameter information to be normalized, then according to the data after normalized, the neural network set up is trained, thus the predicated error of the neural network based on elman is controlled in the scope preset;
D, using the generated output historical data predicting day the last week and prediction day weather parameters data as input, adopt training after neural network to prediction day generated output predict.
2. a kind of photovoltaic generating system power forecasting method based on elman neural network according to claim 1, is characterized in that: described default scope is 5%-10%.
3. a kind of photovoltaic generating system power forecasting method based on elman neural network according to claim 2, it is characterized in that: described generated output historical data comprises generated output per hour and effective generating dutation section, and described weather history parameter information comprises temperature, air pressure, wind direction, wind speed, cloud amount, rainfall, sunshine-duration and weather pattern.
4. a kind of photovoltaic generating system power forecasting method based on elman neural network according to claim 3, it is characterized in that: described step B, it comprises:
The generated output historical data that B1, statistics obtain and weather history parameter information, using the actual power power of one day as the output data of neural network, using this day the last week generated output W per hour in effective time section f and the weather parameters data of this day as the input data of neural network;
B2, initialization is carried out to elman neural network, input node unit vector, hidden layer node unit vector, feedback states vector sum output node vector is determined according to input and output sequence, thus the neural metwork training model set up based on elman, wherein, the nodes of hidden layer draws by increasing progressively method of trial and error gradually.
5. a kind of photovoltaic generating system power forecasting method based on elman neural network according to claim 4, is characterized in that: the non-linear state space expression formula of described elman neural network is:
，
Wherein,
yfor m ties up output node vector;
lfor
ndimension hidden layer node unit vector;
xfor u ties up input vector;
cfor n ties up feedback states vector; w
3for hidden layer connects weights to output layer; w
2for input layer connects weights to hidden layer; w
1for accepting the connection weights of layer to hidden layer;
g(*) be the transport function of output neuron;
f(*) be the transport function of hidden layer neuron.
6. a kind of photovoltaic generating system power forecasting method based on elman neural network according to claim 4, it is characterized in that: described step C, it comprises:
C1, employing minimax method are normalized generated output historical data and weather history parameter information, and the formula of described normalized is:
，
Wherein,
x maxfor the maximum number in data sequence,
x minfor the minimum number in data sequence;
C2, according to the data after normalized, error calculation, right value update and threshold values are carried out to the neural network set up and upgrade, thus the predicated error of the neural network based on elman to be controlled in the scope of 5%-10%.
7. a kind of photovoltaic generating system power forecasting method based on elman neural network according to claim 6, it is characterized in that: described elman neural network adopts BP algorithm to carry out modified weight renewal, and adopt sum of squared errors function to carry out target function study, described target function
e (w)the formula of study is:
，
Wherein,
for target input vector.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410462330.2A CN104268638A (en) | 2014-09-11 | 2014-09-11 | Photovoltaic power generation system power predicting method of elman-based neural network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410462330.2A CN104268638A (en) | 2014-09-11 | 2014-09-11 | Photovoltaic power generation system power predicting method of elman-based neural network |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN104268638A true CN104268638A (en) | 2015-01-07 |
Family
ID=52160158
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410462330.2A Pending CN104268638A (en) | 2014-09-11 | 2014-09-11 | Photovoltaic power generation system power predicting method of elman-based neural network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104268638A (en) |
Cited By (28)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104636822A (en) * | 2015-01-21 | 2015-05-20 | 广州市香港科大霍英东研究院 | Residential load prediction method of elman-based neural network |
| CN104850914A (en) * | 2015-05-29 | 2015-08-19 | 东南大学 | Feature modeling based new energy power generation capacity forecasting method |
| CN105139264A (en) * | 2015-06-06 | 2015-12-09 | 安徽工程大学 | Photovoltaic generation capacity prediction method based on particle swarm algorithm wavelet neural network |
| CN105928292A (en) * | 2016-04-20 | 2016-09-07 | 山东三九制冷设备有限公司 | Photovoltaic refrigerator system controlled based on load prediction and demand response of neural network |
| CN106202920A (en) * | 2016-07-08 | 2016-12-07 | 中国石油大学(华东) | A kind of application and interpretation method of single sea level pressure of standing |
| CN106845770A (en) * | 2016-12-16 | 2017-06-13 | 深圳市麦澜创新科技有限公司 | A kind of energy storage management system based on neutral net |
| CN107292386A (en) * | 2016-04-11 | 2017-10-24 | 福特全球技术公司 | Detected using the rainwater of the view-based access control model of deep learning |
| CN107291994A (en) * | 2017-05-25 | 2017-10-24 | 天津大学 | A kind of stalk calorific value Forecasting Methodology based on Elman neural network models |
| JP2018007312A (en) * | 2016-06-27 | 2018-01-11 | 藤崎電機株式会社 | Generated power prediction apparatus, server, computer program, and generated power prediction method |
| CN107967541A (en) * | 2017-12-21 | 2018-04-27 | 联想(北京)有限公司 | A kind of information forecasting method, information prediction device and server cluster |
| CN108256668A (en) * | 2017-12-13 | 2018-07-06 | 上海电力学院 | Photovoltaic power generation output forecasting method based on harmony search-Elman algorithms |
| CN108446799A (en) * | 2018-03-12 | 2018-08-24 | 浙江大学 | Waste pressure turbine device generated power forecasting method based on Elman neural networks |
| CN108537327A (en) * | 2018-03-28 | 2018-09-14 | 北京航空航天大学 | One kind being based on time series BP neural network prediction technique and device |
| CN108596405A (en) * | 2018-05-09 | 2018-09-28 | 中电国瑞物流有限公司 | The prediction technique and system of grey library yield |
| CN108876013A (en) * | 2018-05-29 | 2018-11-23 | 福州大学 | One kind being based on best similar day and Elman neural fusion photovoltaic plant short term power prediction technique |
| CN109858665A (en) * | 2018-12-06 | 2019-06-07 | 国网河北省电力有限公司 | Photovoltaic short term power prediction technique based on Feature Selection and ANFIS-PSO |
| CN109978283A (en) * | 2019-04-25 | 2019-07-05 | 中国人民解放军国防科技大学 | Photovoltaic power generation power prediction method based on branch evolution neural network |
| CN110046765A (en) * | 2019-04-15 | 2019-07-23 | 国网甘肃省电力公司电力科学研究院 | A kind of power system stabilizer, PSS implementation method based on Elman neural network |
| CN110349680A (en) * | 2019-06-27 | 2019-10-18 | 武汉科技大学 | A kind of prediction technique of opioid drug crisis explosion time |
| CN110414748A (en) * | 2019-08-12 | 2019-11-05 | 合肥阳光新能源科技有限公司 | Photovoltaic power prediction technique |
| CN111022206A (en) * | 2018-10-09 | 2020-04-17 | 丰田自动车株式会社 | Control device and control method for vehicle drive device, in-vehicle electronic control unit, learned model, and machine learning system |
| CN111242355A (en) * | 2020-01-06 | 2020-06-05 | 中国电力科学研究院有限公司 | Photovoltaic probability prediction method and system based on Bayesian neural network |
| CN111428926A (en) * | 2020-03-23 | 2020-07-17 | 国网江苏省电力有限公司镇江供电分公司 | Regional power load prediction method considering meteorological factors |
| CN112116171A (en) * | 2020-09-29 | 2020-12-22 | 山东建筑大学 | Novel photovoltaic power generation power prediction method based on neural network |
| CN112215428A (en) * | 2020-10-20 | 2021-01-12 | 国网山东省电力公司电力科学研究院 | Photovoltaic power generation power prediction method and system based on error correction and fuzzy logic |
| CN112348247A (en) * | 2020-11-03 | 2021-02-09 | 深圳大学 | Prediction method and device for light energy power generation, computer equipment and storage medium |
| CN112541633A (en) * | 2020-12-15 | 2021-03-23 | 浙江浙能嘉兴发电有限公司 | Ultrahigh-precision photovoltaic power generation power prediction method and system based on neural network |
| CN114004405A (en) * | 2021-11-03 | 2022-02-01 | 国网山东省电力公司东营供电公司 | Photovoltaic power prediction method and system based on Elman neural network and satellite cloud picture |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008132066A1 (en) * | 2007-04-27 | 2008-11-06 | Siemens Aktiengesellschaft | A method for computer-assisted learning of one or more neural networks |
| CN102567781A (en) * | 2011-12-20 | 2012-07-11 | 西北师范大学 | Network behavior prediction method based on neural network |
| CN103500366A (en) * | 2013-09-29 | 2014-01-08 | 上海电机学院 | System and method for predicating short-time wind power |
| CN103903067A (en) * | 2014-04-09 | 2014-07-02 | 上海电机学院 | Short-term combination forecasting method for wind power |
-
2014
- 2014-09-11 CN CN201410462330.2A patent/CN104268638A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008132066A1 (en) * | 2007-04-27 | 2008-11-06 | Siemens Aktiengesellschaft | A method for computer-assisted learning of one or more neural networks |
| CN102567781A (en) * | 2011-12-20 | 2012-07-11 | 西北师范大学 | Network behavior prediction method based on neural network |
| CN103500366A (en) * | 2013-09-29 | 2014-01-08 | 上海电机学院 | System and method for predicating short-time wind power |
| CN103903067A (en) * | 2014-04-09 | 2014-07-02 | 上海电机学院 | Short-term combination forecasting method for wind power |
Cited By (37)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104636822A (en) * | 2015-01-21 | 2015-05-20 | 广州市香港科大霍英东研究院 | Residential load prediction method of elman-based neural network |
| CN104636822B (en) * | 2015-01-21 | 2018-08-31 | 广州市香港科大霍英东研究院 | A kind of resident load prediction technique based on elman neural networks |
| CN104850914A (en) * | 2015-05-29 | 2015-08-19 | 东南大学 | Feature modeling based new energy power generation capacity forecasting method |
| CN104850914B (en) * | 2015-05-29 | 2018-07-17 | 东南大学 | A kind of generation of electricity by new energy amount prediction technique of feature based modeling |
| CN105139264A (en) * | 2015-06-06 | 2015-12-09 | 安徽工程大学 | Photovoltaic generation capacity prediction method based on particle swarm algorithm wavelet neural network |
| CN107292386A (en) * | 2016-04-11 | 2017-10-24 | 福特全球技术公司 | Detected using the rainwater of the view-based access control model of deep learning |
| CN105928292B (en) * | 2016-04-20 | 2018-05-22 | 山东三九制冷设备有限公司 | A kind of photovoltaic refrigeration storage system of load prediction based on neutral net and demand response control |
| CN105928292A (en) * | 2016-04-20 | 2016-09-07 | 山东三九制冷设备有限公司 | Photovoltaic refrigerator system controlled based on load prediction and demand response of neural network |
| JP2018007312A (en) * | 2016-06-27 | 2018-01-11 | 藤崎電機株式会社 | Generated power prediction apparatus, server, computer program, and generated power prediction method |
| CN106202920A (en) * | 2016-07-08 | 2016-12-07 | 中国石油大学(华东) | A kind of application and interpretation method of single sea level pressure of standing |
| CN106845770A (en) * | 2016-12-16 | 2017-06-13 | 深圳市麦澜创新科技有限公司 | A kind of energy storage management system based on neutral net |
| CN107291994A (en) * | 2017-05-25 | 2017-10-24 | 天津大学 | A kind of stalk calorific value Forecasting Methodology based on Elman neural network models |
| CN108256668A (en) * | 2017-12-13 | 2018-07-06 | 上海电力学院 | Photovoltaic power generation output forecasting method based on harmony search-Elman algorithms |
| CN107967541A (en) * | 2017-12-21 | 2018-04-27 | 联想(北京)有限公司 | A kind of information forecasting method, information prediction device and server cluster |
| CN108446799A (en) * | 2018-03-12 | 2018-08-24 | 浙江大学 | Waste pressure turbine device generated power forecasting method based on Elman neural networks |
| CN108446799B (en) * | 2018-03-12 | 2021-08-03 | 浙江大学 | Residual pressure turbine device power generation power prediction method based on Elman neural network |
| CN108537327A (en) * | 2018-03-28 | 2018-09-14 | 北京航空航天大学 | One kind being based on time series BP neural network prediction technique and device |
| CN108596405A (en) * | 2018-05-09 | 2018-09-28 | 中电国瑞物流有限公司 | The prediction technique and system of grey library yield |
| CN108876013A (en) * | 2018-05-29 | 2018-11-23 | 福州大学 | One kind being based on best similar day and Elman neural fusion photovoltaic plant short term power prediction technique |
| CN111022206A (en) * | 2018-10-09 | 2020-04-17 | 丰田自动车株式会社 | Control device and control method for vehicle drive device, in-vehicle electronic control unit, learned model, and machine learning system |
| CN111022206B (en) * | 2018-10-09 | 2022-06-21 | 丰田自动车株式会社 | Control device and method for vehicle drive device, and vehicle-mounted electronic control unit |
| CN109858665A (en) * | 2018-12-06 | 2019-06-07 | 国网河北省电力有限公司 | Photovoltaic short term power prediction technique based on Feature Selection and ANFIS-PSO |
| CN110046765A (en) * | 2019-04-15 | 2019-07-23 | 国网甘肃省电力公司电力科学研究院 | A kind of power system stabilizer, PSS implementation method based on Elman neural network |
| CN109978283B (en) * | 2019-04-25 | 2021-05-11 | 中国人民解放军国防科技大学 | Photovoltaic power generation power prediction method based on branch evolution neural network |
| CN109978283A (en) * | 2019-04-25 | 2019-07-05 | 中国人民解放军国防科技大学 | Photovoltaic power generation power prediction method based on branch evolution neural network |
| CN110349680A (en) * | 2019-06-27 | 2019-10-18 | 武汉科技大学 | A kind of prediction technique of opioid drug crisis explosion time |
| CN110414748A (en) * | 2019-08-12 | 2019-11-05 | 合肥阳光新能源科技有限公司 | Photovoltaic power prediction technique |
| CN110414748B (en) * | 2019-08-12 | 2022-04-08 | 阳光新能源开发股份有限公司 | Photovoltaic power prediction method |
| CN111242355A (en) * | 2020-01-06 | 2020-06-05 | 中国电力科学研究院有限公司 | Photovoltaic probability prediction method and system based on Bayesian neural network |
| CN111428926A (en) * | 2020-03-23 | 2020-07-17 | 国网江苏省电力有限公司镇江供电分公司 | Regional power load prediction method considering meteorological factors |
| CN112116171A (en) * | 2020-09-29 | 2020-12-22 | 山东建筑大学 | Novel photovoltaic power generation power prediction method based on neural network |
| CN112215428A (en) * | 2020-10-20 | 2021-01-12 | 国网山东省电力公司电力科学研究院 | Photovoltaic power generation power prediction method and system based on error correction and fuzzy logic |
| CN112215428B (en) * | 2020-10-20 | 2022-08-26 | 国网山东省电力公司电力科学研究院 | Photovoltaic power generation power prediction method and system based on error correction and fuzzy logic |
| CN112348247A (en) * | 2020-11-03 | 2021-02-09 | 深圳大学 | Prediction method and device for light energy power generation, computer equipment and storage medium |
| CN112348247B (en) * | 2020-11-03 | 2022-04-15 | 深圳大学 | Prediction method and device for light energy power generation, computer equipment and storage medium |
| CN112541633A (en) * | 2020-12-15 | 2021-03-23 | 浙江浙能嘉兴发电有限公司 | Ultrahigh-precision photovoltaic power generation power prediction method and system based on neural network |
| CN114004405A (en) * | 2021-11-03 | 2022-02-01 | 国网山东省电力公司东营供电公司 | Photovoltaic power prediction method and system based on Elman neural network and satellite cloud picture |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104268638A (en) | Photovoltaic power generation system power predicting method of elman-based neural network | |
| Dong et al. | A novel convolutional neural network framework based solar irradiance prediction method | |
| Liu et al. | Prediction short-term photovoltaic power using improved chicken swarm optimizer-extreme learning machine model | |
| Gao et al. | Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using LSTM | |
| Ye et al. | Predicting electricity consumption in a building using an optimized back-propagation and Levenberg–Marquardt back-propagation neural network: Case study of a shopping mall in China | |
| CN102930358B (en) | A kind of neural net prediction method of photovoltaic power station power generation power | |
| CN103489038A (en) | Photovoltaic ultra-short-term power prediction method based on LM-BP neural network | |
| Cadenas et al. | Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks | |
| CN110490385A (en) | The unified prediction of electric load and thermic load in a kind of integrated energy system | |
| CN105139264A (en) | Photovoltaic generation capacity prediction method based on particle swarm algorithm wavelet neural network | |
| CN103699944A (en) | Wind and photovoltaic generation power prediction system with multiple prediction modes | |
| CN104636822A (en) | Residential load prediction method of elman-based neural network | |
| CN103218674A (en) | Method for predicating output power of photovoltaic power generation system based on BP (Back Propagation) neural network model | |
| CN103390116A (en) | Method for predicting electricity generation power of photovoltaic power station in step-by-step way | |
| CN104376389A (en) | Master-slave type micro-grid power load prediction system and master-slave type micro-grid power load prediction method based on load balancing | |
| CN105426956A (en) | Ultra-short-period photovoltaic prediction method | |
| CN104050517A (en) | Photovoltaic power generation forecasting method based on GRNN | |
| WO2024046137A1 (en) | Power prediction model construction method for multi-energy combined power generation system and power prediction method for multi-energy combined power generation system | |
| Kolhe et al. | GA-ANN for short-term wind energy prediction | |
| CN104598765A (en) | Building energy consumption prediction method based on elastic adaptive neural network | |
| Li et al. | Day-ahead hourly photovoltaic generation forecasting using extreme learning machine | |
| Jia et al. | An improved artificial bee colony-BP neural network algorithm in the short-term wind speed prediction | |
| Shah et al. | A novel prediction error-based power forecasting scheme for real pv system using pvusa model: A grey box-based neural network approach | |
| Olaofe et al. | Wind power estimation using recurrent neural network technique | |
| Chen et al. | Research on wind power prediction method based on convolutional neural network and genetic algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20150107 |
|
| RJ01 | Rejection of invention patent application after publication |