CN114036762A - System and method for predicting load of power distribution network based on proportionality coefficient method - Google Patents
System and method for predicting load of power distribution network based on proportionality coefficient method Download PDFInfo
- Publication number
- CN114036762A CN114036762A CN202111349464.XA CN202111349464A CN114036762A CN 114036762 A CN114036762 A CN 114036762A CN 202111349464 A CN202111349464 A CN 202111349464A CN 114036762 A CN114036762 A CN 114036762A
- Authority
- CN
- China
- Prior art keywords
- load
- predicted
- distribution network
- coefficient
- power distribution
- 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.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/04—Power grid distribution networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/06—Power analysis or power optimisation
Abstract
The invention discloses a system and a method for predicting the load of a power distribution network based on a proportionality coefficient method, wherein the method comprises the following steps: acquiring the current load of a line to be predicted of the power distribution network; and inputting the current load into a load prediction model, and calculating to obtain a predicted load. According to the invention, the load prediction model is constructed by a proportionality coefficient method, and when prediction is needed, only the current load of the line to be predicted is input into the load prediction model, so that relatively accurate predicted load can be quickly obtained. Compared with the prior art, the method has the advantages of simple budget, rapidness and accuracy.
Description
Technical Field
The invention relates to a system and a method for predicting the load of a power distribution network based on a proportionality coefficient method, and belongs to the technical field of power distribution network load prediction.
Background
At present, the electric power industry of China develops rapidly, power management gradually moves to the market, and the accuracy of load prediction becomes the primary target of power supply enterprises. The accurate load prediction result is beneficial to improving the safety and the stability of the system, and the power generation cost can be reduced. The load prediction of the power distribution network is related to the development of national economy and the normal development of industrial agriculture, and is easily influenced by factors such as climate, environment, economy, politics and the like, and the uncertain factors increase the complexity of the load prediction of the power distribution network. In the market development at the present stage, the power market adds a large number of self-service distributed power generation and mobile charging electric vehicles, and the power load variation factors of the electric vehicles also have uncertainty. The load of the power distribution network has great randomness, but the load of the power distribution network has certain regularity in general. However, most of the existing power load prediction methods are manual prediction methods, a large amount of calculation is required by technicians, a large amount of manpower and material resources are consumed, the calculation process is long in time consumption, and the prediction precision is low.
Disclosure of Invention
Based on the above, the invention provides a system and a method for predicting the load of a power distribution network based on a proportionality coefficient method, which can accurately and quickly predict the power load of a certain line of the power distribution network so as to overcome the defects of the prior art.
The technical scheme of the invention is as follows: the method for predicting the load of the power distribution network based on the proportionality coefficient method comprises the following steps:
acquiring the current load of a line to be predicted of the power distribution network;
and inputting the current load into a load prediction model, and calculating to obtain a predicted load.
Optionally, the load prediction model is:
y＝(k+b)x
in the formula, y is a predicted load, x is a current load, k is a predicted load proportion coefficient, and b is a predicted comprehensive adjustment coefficient.
Optionally, the method for obtaining the load proportionality coefficient includes:
acquiring historical load of a line to be predicted of the power distribution network;
obtaining seasonal typical load data of different years according to the historical load;
taking the ratio of the typical load data of adjacent seasons in the same year as the load proportionality coefficients of different seasons;
carrying out weighted average calculation on seasonal load proportion coefficients of different years to obtain a predicted load proportion coefficient;
and selecting a corresponding predicted load proportion coefficient according to the prediction time to perform load prediction calculation.
Optionally, the method for obtaining seasonal typical load data of different years according to the historical load comprises:
dividing the historical load into seasonal data of different seasons in different years;
and selecting corresponding seasonal data according to typical dates of different seasons in the same year to perform average processing to obtain seasonal typical load data.
Optionally, the selection method of the comprehensive prediction adjustment coefficient b is as follows:
setting initial values of the comprehensive prediction adjustment coefficients b in different seasons;
and when the predicted load and the actual load are out of the set deviation, adjusting the initial value of the comprehensive predicted adjustment coefficient b in different seasons according to the actual load.
Optionally, the comprehensive prediction adjustment coefficient b is adjusted according to the climate change trend coefficient, the social GDP growth, and the load report.
The invention also provides a system for predicting the load of the power distribution network based on a proportionality coefficient method, which comprises the following steps:
an acquisition module to: acquiring the current load of a line to be predicted of the power distribution network;
a prediction module to: and inputting the current load into a load prediction model, and calculating to obtain a predicted load.
Optionally, the load prediction model is:
y＝(k+b)x
in the formula, y is a predicted load, x is a current load, k is a predicted load proportion coefficient, and b is a predicted comprehensive adjustment coefficient.
Optionally, the method for obtaining the load proportionality coefficient includes:
acquiring historical load of a line to be predicted of the power distribution network;
obtaining seasonal typical load data of different years according to the historical load;
taking the ratio of the typical load data of adjacent seasons in the same year as the load proportionality coefficients of different seasons;
carrying out weighted average calculation on seasonal load proportion coefficients of different years to obtain a predicted load proportion coefficient;
and selecting a corresponding predicted load proportion coefficient according to the prediction time to perform load prediction calculation.
Optionally, the selection method of the comprehensive prediction adjustment coefficient b is as follows:
setting initial values of the comprehensive prediction adjustment coefficients b in different seasons;
and when the predicted load and the actual load are out of the set deviation, adjusting the initial value of the comprehensive predicted adjustment coefficient b in different seasons according to the actual load.
The invention has the beneficial effects that: according to the invention, the load prediction model is constructed by a proportionality coefficient method, and when prediction is needed, only the current load of the line to be predicted is input into the load prediction model, so that relatively accurate predicted load can be quickly obtained. The invention has the advantages of simple budget, rapidness and accuracy.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a view showing the structure of the apparatus of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, but rather should be construed as broadly as the present invention is capable of modification in various respects, all without departing from the spirit and scope of the present invention.
Based on the above, referring to fig. 1, an embodiment of the present invention provides a method for predicting a load of a power distribution network based on a scale factor method, where the method includes:
s1, acquiring the current load of the line to be predicted of the power distribution network;
specifically, when load prediction needs to be performed on a certain line of the power distribution network, the current load of the line needs to be acquired first. In this embodiment, the load may be a current. The load obtaining mode can be obtained from the power dispatching automation system.
And S2, inputting the current load into a load prediction model, and calculating to obtain a predicted load.
Specifically, the current load is input to the load prediction model, so that the predicted load of the line can be obtained. It should be noted that the predicted load is the load at some future time after the current load.
The load prediction model is as follows:
y＝(k+b)x
in the formula, y is a predicted load, x is a current load, k is a predicted load proportion coefficient, and b is a predicted comprehensive adjustment coefficient.
During prediction, the current load of the line is used as x to substitute the formula, and the predicted load y can be calculated according to the determined predicted load proportion coefficient k and the predicted comprehensive adjustment coefficient b.
Specifically, the method for obtaining the load proportionality coefficient in the above formula is as follows:
1. acquiring historical load of a line to be predicted of the power distribution network;
the method comprises the steps of firstly acquiring historical load data of each line of the power distribution network, such as historical data of the last 5 years, from a power dispatching automation system.
2. Obtaining seasonal typical load data of different years according to the historical load;
specifically, the historical load is divided into seasonal data of different seasons in different years, such as spring load data, summer load data, autumn load data and winter load data, then corresponding seasonal data is selected according to typical dates of different seasons in the same year to be subjected to average processing to obtain seasonal typical load data, typical dates of different seasons can be selected according to actual experience, the electricity load of the season is indicated to the greatest extent, for example, 1-15 days in 9 months per year can be used as typical dates for selecting autumn load data, and 1-15 days in 1 month can be used as typical dates for selecting winter load data.
3. Taking the ratio of the typical load data of adjacent seasons in the same year as the load proportionality coefficients of different seasons;
specifically, after the typical load data of the seasons is obtained, the ratio of the typical load data of the summer to the typical load data of the spring is used as a summer predicted load proportionality coefficient, the ratio of the typical load data of the autumn to the typical load data of the summer is used as an autumn predicted load proportionality coefficient, the ratio of the typical load data of the winter to the typical load data of the autumn is used as a winter predicted load proportionality coefficient, and the ratio of the typical load data of the spring to the typical load data of the winter is used as a spring predicted load proportionality coefficient.
4. Carrying out weighted average calculation on seasonal load proportion coefficients of different years to obtain a predicted load proportion coefficient;
specifically, the optimal spring predicted load proportionality coefficient can be obtained by performing weighted average calculation on the spring predicted load proportionality coefficients of different years, the optimal summer predicted load proportionality coefficient can be obtained by performing weighted average calculation on the summer predicted load proportionality coefficients of different years, the optimal autumn predicted load proportionality coefficient can be obtained by performing weighted average calculation on the autumn predicted load proportionality coefficients of different years, the optimal winter predicted load proportionality coefficient can be obtained by performing weighted average calculation on the winter predicted load proportionality coefficients of different years, and the corresponding optimal predicted load proportionality coefficient is selected according to the prediction time to perform load prediction calculation. For example, when the load in summer currently belongs to spring and needs to be predicted, the load in summer proportional coefficient can be selected as the predicted load proportional coefficient and substituted into the formula for calculation, and when the load in winter currently belongs to autumn and needs to be predicted, the load in winter proportional coefficient can be selected as the predicted load proportional coefficient and substituted into the formula for calculation.
Specifically, the selection method of the comprehensive prediction adjustment coefficient b in the formula is as follows:
the total prediction adjustment coefficient b for each season may be set to an initial value, for example, 0, or may be set to another value. And when the predicted load and the actual load are out of the set deviation, adjusting the initial value of the comprehensive predicted adjustment coefficient b in different seasons according to the actual load. That is, the value of the predicted integrated adjustment coefficient b is calculated by substituting the actual load into the above equation as the basis of the subsequent prediction. It should be noted that the predictive synthesis adjustment coefficient may be different for each season.
Specifically, the predicted comprehensive adjustment coefficient b is adjusted according to the climate change trend coefficient, social GDP growth and load reporting. In the present embodiment, when the climate change tendency coefficient exceeds the set value, the predicted integrated adjustment coefficient b is appropriately increased, for example, increased by 0.1 or 0.2, when the change is made in the direction of increasing the power demand, and the predicted integrated adjustment coefficient b is appropriately decreased, for example, decreased by 0.1 or 0.2, when the change is made in the direction of decreasing the power demand. When the social GDP grows, the influence of the growth rate on the power demand is considered, and the comprehensive predictive adjustment coefficient b is adjusted and increased appropriately, for example, increased by 0.3 and 0.4. And when the line has new load reporting in the prediction period, adjusting and predicting the comprehensive adjustment coefficient b according to the load added by the load reporting.
The working principle is as follows: the operating load of the distribution network generally varies in each season of the year, and the main reasons are different climatic seasons, weekends, distribution of legal annual holidays and the like. In the long term, the load change has certain cyclic characteristics and regularity. The basic idea of the proportionality coefficient method is to predict the load current in the future season by grasping the cyclic characteristics and regularity of seasonal changes. The scale factor method is to calculate the change ratio of the load in the seasonal change according to the monthly operation data statistics of the past years, and then predict the operation value of each quarter in the future. The prediction result shows that the method can obtain high-quality load prediction.
After the method is adopted, the following steps are as follows: the autumn load average current of a 10kV certain line is 240A, the winter load is predicted to reach 415A through a load prediction formula, at the moment, line parameters can be compared, risk analysis is carried out, the line parameters are submitted to an equipment management department, measures such as load transfer are taken in advance, and the operation risk is reduced. The load of a certain district in the Guizhou Xingzi urban area is selected for verification, and the result accuracy of the method reaches more than 95%. The research method is proved to have practicability.
Referring to fig. 2, the system for predicting the load of the distribution network based on the proportionality coefficient method of the present embodiment includes: an acquisition module to: acquiring the current load of a line to be predicted of the power distribution network; a prediction module to: and inputting the current load into a load prediction model, and calculating to obtain a predicted load.
The implementation process of the functions and actions of each module in the above device is specifically described in the implementation process of the corresponding step in the above method, and is not described herein again.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. The method for predicting the load of the power distribution network based on the proportionality coefficient method is characterized by comprising the following steps:
acquiring the current load of a line to be predicted of the power distribution network;
and inputting the current load into a load prediction model, and calculating to obtain a predicted load.
2. The method for predicting the load of the power distribution network based on the proportionality coefficient method as recited in claim 1, wherein the load prediction model is:
y＝(k+b)x
in the formula, y is a predicted load, x is a current load, k is a predicted load proportion coefficient, and b is a predicted comprehensive adjustment coefficient.
3. The method for predicting the load of the power distribution network based on the proportionality coefficient method as claimed in claim 2, wherein the load proportionality coefficient is obtained by:
acquiring historical load of a line to be predicted of the power distribution network;
obtaining seasonal typical load data of different years according to the historical load;
taking the ratio of the typical load data of adjacent seasons in the same year as the load proportionality coefficients of different seasons;
carrying out weighted average calculation on seasonal load proportion coefficients of different years to obtain a predicted load proportion coefficient;
and selecting a corresponding predicted load proportion coefficient according to the prediction time to perform load prediction calculation.
4. The method for predicting the load of the power distribution network based on the proportionality coefficient method as claimed in claim 3, wherein the method for obtaining seasonal typical load data of different years according to the historical load is as follows:
dividing the historical load into seasonal data of different seasons in different years;
and selecting corresponding seasonal data according to typical dates of different seasons in the same year to perform average processing to obtain seasonal typical load data.
5. The method for predicting the load of the power distribution network based on the proportionality coefficient method as claimed in claim 2, wherein the selection method of the comprehensive prediction adjustment coefficient b is as follows:
setting initial values of the comprehensive prediction adjustment coefficients b in different seasons;
and when the predicted load and the actual load are out of the set deviation, adjusting the initial value of the comprehensive predicted adjustment coefficient b in different seasons according to the actual load.
6. The method for forecasting loads on a power distribution network based on the proportionality coefficient method as recited in claim 5, wherein said forecast total adjustment coefficient b is adjusted according to climate change trend coefficient, social GDP growth and load reporting.
7. System for predicting distribution network load based on a proportionality coefficient method, the system comprising:
an acquisition module to: acquiring the current load of a line to be predicted of the power distribution network;
a prediction module to: and inputting the current load into a load prediction model, and calculating to obtain a predicted load.
8. The system for predicting the load of the power distribution network based on the proportionality coefficient method as recited in claim 7, wherein said load prediction model is:
y＝(k+b)x
in the formula, y is a predicted load, x is a current load, k is a predicted load proportion coefficient, and b is a predicted comprehensive adjustment coefficient.
9. The system for predicting the load of the power distribution network based on the proportionality coefficient method as claimed in claim 8, wherein the load proportionality coefficient is obtained by:
acquiring historical load of a line to be predicted of the power distribution network;
obtaining seasonal typical load data of different years according to the historical load;
taking the ratio of the typical load data of adjacent seasons in the same year as the load proportionality coefficients of different seasons;
carrying out weighted average calculation on seasonal load proportion coefficients of different years to obtain a predicted load proportion coefficient;
and selecting a corresponding predicted load proportion coefficient according to the prediction time to perform load prediction calculation.
10. The system for predicting the load of the power distribution network based on the proportionality coefficient method as recited in claim 8, wherein the selection method of the comprehensive prediction adjustment coefficient b is as follows:
setting initial values of the comprehensive prediction adjustment coefficients b in different seasons;
and when the predicted load and the actual load are out of the set deviation, adjusting the initial value of the comprehensive predicted adjustment coefficient b in different seasons according to the actual load.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111349464.XA CN114036762B (en) | 2021-11-15 | 2021-11-15 | System and method for predicting load of power distribution network based on proportionality coefficient method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111349464.XA CN114036762B (en) | 2021-11-15 | 2021-11-15 | System and method for predicting load of power distribution network based on proportionality coefficient method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114036762A true CN114036762A (en) | 2022-02-11 |
| CN114036762B CN114036762B (en) | 2022-07-19 |
Family
ID=80144457
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111349464.XA Active CN114036762B (en) | 2021-11-15 | 2021-11-15 | System and method for predicting load of power distribution network based on proportionality coefficient method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114036762B (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103268524A (en) * | 2013-06-03 | 2013-08-28 | 国家电网公司 | Method for improving power grid short-term load forecasting accuracy |
| CN103632031A (en) * | 2013-10-22 | 2014-03-12 | 国家电网公司 | Rural load type load modeling method based on load curve decomposition |
| CN104091293A (en) * | 2014-07-30 | 2014-10-08 | 国家电网公司 | ower grid long-term load characteristic predication method based on variation of electricity consumption structure |
| CN104933630A (en) * | 2015-05-21 | 2015-09-23 | 国家电网公司 | Load characteristic analysis method and system |
| CN105930930A (en) * | 2016-04-21 | 2016-09-07 | 国家电网公司 | Load data calibration method based on load characteristic analysis and load prediction technology |
| CN106874630A (en) * | 2017-03-28 | 2017-06-20 | 上海理工大学 | Based on the regional power grid new energy development potential evaluation method that electricity is dissolved |
| CN110492488A (en) * | 2019-09-09 | 2019-11-22 | 国网天津市电力公司 | A kind of area power grid low-frequency and low-voltage load-reducing tagmeme automatic generation method |
| CN112003278A (en) * | 2020-08-21 | 2020-11-27 | 国核电力规划设计研究院重庆有限公司 | Method and system for realizing distributed photovoltaic self-power utilization ratio prediction |
| CN113361785A (en) * | 2021-06-10 | 2021-09-07 | 国网河北省电力有限公司经济技术研究院 | Power distribution network short-term load prediction method and device, terminal and storage medium |
| CN113452033A (en) * | 2021-06-29 | 2021-09-28 | 佛山电力设计院有限公司 | Method for controlling voltage of high-proportion photovoltaic power distribution network with partition autonomy and storage medium |
-
2021
- 2021-11-15 CN CN202111349464.XA patent/CN114036762B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103268524A (en) * | 2013-06-03 | 2013-08-28 | 国家电网公司 | Method for improving power grid short-term load forecasting accuracy |
| CN103632031A (en) * | 2013-10-22 | 2014-03-12 | 国家电网公司 | Rural load type load modeling method based on load curve decomposition |
| CN104091293A (en) * | 2014-07-30 | 2014-10-08 | 国家电网公司 | ower grid long-term load characteristic predication method based on variation of electricity consumption structure |
| CN104933630A (en) * | 2015-05-21 | 2015-09-23 | 国家电网公司 | Load characteristic analysis method and system |
| CN105930930A (en) * | 2016-04-21 | 2016-09-07 | 国家电网公司 | Load data calibration method based on load characteristic analysis and load prediction technology |
| CN106874630A (en) * | 2017-03-28 | 2017-06-20 | 上海理工大学 | Based on the regional power grid new energy development potential evaluation method that electricity is dissolved |
| CN110492488A (en) * | 2019-09-09 | 2019-11-22 | 国网天津市电力公司 | A kind of area power grid low-frequency and low-voltage load-reducing tagmeme automatic generation method |
| CN112003278A (en) * | 2020-08-21 | 2020-11-27 | 国核电力规划设计研究院重庆有限公司 | Method and system for realizing distributed photovoltaic self-power utilization ratio prediction |
| CN113361785A (en) * | 2021-06-10 | 2021-09-07 | 国网河北省电力有限公司经济技术研究院 | Power distribution network short-term load prediction method and device, terminal and storage medium |
| CN113452033A (en) * | 2021-06-29 | 2021-09-28 | 佛山电力设计院有限公司 | Method for controlling voltage of high-proportion photovoltaic power distribution network with partition autonomy and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| 冯晋: "《成都地区负荷预测》", 《第四届输配电技术国际会议2003》 * |
| 常征: "《河北省电力市场用电分析预测系统研究》", 《中国优秀傅硕士学位论文全文数据库(硕士)社会科学I辑》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114036762B (en) | 2022-07-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111552923B (en) | Load prediction method and load prediction system based on universal distribution | |
| CN104616079A (en) | Temperature change based power grid daily electricity consumption prediction method | |
| CN102426674A (en) | Power system load prediction method based on Markov chain | |
| CN104778503A (en) | Method for forecasting public building air conditioner short-time base wire load with consideration of real-time weather factors | |
| CN107403015B (en) | Short-term optical power prediction method based on time series similarity | |
| CN106600037B (en) | Multi-parameter auxiliary load prediction method based on principal component analysis | |
| CN111680937B (en) | Small and micro enterprise rework rate evaluation method based on power data grading and empowerment | |
| CN102509173A (en) | Markov chain based method for accurately forecasting power system loads | |
| CN110490409B (en) | DNN-based low-voltage transformer area line loss rate benchmarking value setting method | |
| CN105184388A (en) | Non-linear regression method for urban power load short-period prediction | |
| EP1748529A1 (en) | Method and system for the forecast of needed energy quantities of a consumer, in particular industrial consumer, from an energy provider or energy dispatcher, and device for energy need forecast | |
| CN103985000A (en) | Medium-and-long term typical daily load curve prediction method based on function type nonparametric regression | |
| CN114372360A (en) | Method, terminal and storage medium for power load prediction | |
| CN107748972A (en) | A kind of method based on Dual-energy source index prediction Analyzing Total Electricity Consumption | |
| CN110909958A (en) | Short-term load prediction method considering photovoltaic grid-connected power | |
| CN112054507A (en) | Power distribution low-voltage distribution area theoretical line loss interval calculation method based on convolutional neural network | |
| CN103853939A (en) | Combined forecasting method for monthly load of power system based on social economic factor influence | |
| CN113205252A (en) | Aggregated load scheduling method based on demand side load peak regulation potential parameter prediction | |
| CN115545333A (en) | Method for predicting load curve of multi-load daily-type power distribution network | |
| CN113610296B (en) | Method for predicting peak value of electrical load in region and method for planning investment of power grid | |
| CN104252647B (en) | Electro-load forecast method based on anti-distance weighting interpolation method | |
| CN109636033B (en) | Spontaneous self-use comprehensive electricity price prediction method for distributed photovoltaic project | |
| CN111708987B (en) | Method for predicting load of multiple parallel transformers of transformer substation | |
| CN114036762B (en) | System and method for predicting load of power distribution network based on proportionality coefficient method | |
| CN104915727A (en) | Multi-dimensional isomorphic heterogeneous BP neural network optical power ultrashort-term prediction method |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |