CN109978277B - Regional internet load prediction method and device in photovoltaic power generation - Google Patents
Regional internet load prediction method and device in photovoltaic power generation Download PDFInfo
- Publication number
- CN109978277B CN109978277B CN201910279806.1A CN201910279806A CN109978277B CN 109978277 B CN109978277 B CN 109978277B CN 201910279806 A CN201910279806 A CN 201910279806A CN 109978277 B CN109978277 B CN 109978277B
- Authority
- CN
- China
- Prior art keywords
- predicted
- load
- photovoltaic output
- output load
- time
- 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.)
- Active
Links
- 238000010248 power generation Methods 0.000 title claims abstract description 36
- 238000000034 method Methods 0.000 title claims abstract description 30
- 230000009467 reduction Effects 0.000 claims abstract description 64
- 238000005286 illumination Methods 0.000 claims description 14
- 238000004458 analytical method Methods 0.000 claims description 9
- 238000010276 construction Methods 0.000 claims description 4
- 230000005611 electricity Effects 0.000 abstract description 3
- QHGVXILFMXYDRS-UHFFFAOYSA-N pyraclofos Chemical compound C1=C(OP(=O)(OCC)SCCC)C=NN1C1=CC=C(Cl)C=C1 QHGVXILFMXYDRS-UHFFFAOYSA-N 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 230000001105 regulatory effect Effects 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
- 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
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The application relates to a regional internet load prediction method and device in photovoltaic power generation, and belongs to the technical field of photovoltaic power generation, wherein the method comprises the following steps: acquiring a first meteorological factor of a target area in a time to be predicted; inputting a first meteorological factor into a photovoltaic output load function to obtain a predicted photovoltaic output load of a target area in a time to be predicted; the photovoltaic output load function is obtained by fitting a second meteorological factor of the target area in the history time and an actual photovoltaic output load; determining a predicted load reduction value of the time to be predicted according to the actual load reduction value of the target area; calculating the difference between the predicted load reduction value and the predicted photovoltaic output load to obtain the predicted internet load of the target area at the time to be predicted; the problem that the load regularity in the electricity utilization area of the photovoltaic power generation system is low, and the standard prediction accuracy is difficult to reach by adopting the traditional load prediction method can be solved; the accuracy of the internet load prediction can be improved.
Description
Technical Field
The invention relates to a regional internet load prediction method in photovoltaic power generation, and belongs to the technical field of photovoltaic power generation.
Background
Solar photovoltaic power generation is to directly convert sunlight radiation energy into electric energy according to the photovoltaic effect principle of a solar cell. Solar photovoltaic power generation belongs to energy sources with low energy density, poor stability and poor regulating capability, and the generated energy is greatly influenced by weather and regions.
After grid connection, the photovoltaic power generation can generate a periodic impact on the power grid, and the disturbance of the output power of the photovoltaic power generation can possibly influence the stability of the power grid, so that the load prediction, planning and operation of the power system have larger uncertainty compared with the past.
Therefore, a large number of users use the photovoltaic power generation system to supply power for the photovoltaic power generation system, so that a scheduler can more difficult to accurately predict the increase condition of the load, and the scheduling of the system and the scheduling of the unit output are affected. The research on how to carry out load prediction on a region containing a large-scale distributed photovoltaic grid-connected power generation system is a problem to be solved urgently.
Disclosure of Invention
The invention aims to provide a regional internet load prediction method and device in photovoltaic power generation, which can solve the problem that the regional internet load in the photovoltaic power generation cannot be accurately predicted. In order to achieve the above purpose, the present invention provides the following technical solutions:
in a first aspect, a method for predicting regional internet load in photovoltaic power generation is provided, where the method includes:
acquiring a first meteorological factor of a target area in a time to be predicted;
inputting the first meteorological factors into a photovoltaic output load function to obtain a predicted photovoltaic output load of the target area in the time to be predicted; the photovoltaic output load function is obtained by fitting according to a second meteorological factor of the target area in the history time and the actual photovoltaic output load;
determining a predicted load reduction value of the time to be predicted according to the actual load reduction value of the target area;
and calculating the difference between the predicted load reduction value and the predicted photovoltaic output load to obtain the predicted internet load of the target area at the time to be predicted.
Optionally, the inputting the first meteorological factor into a photovoltaic output load function to obtain the predicted photovoltaic output load of the target area before the time to be predicted, further includes:
acquiring a second meteorological factor of the historical time per hour and an actual photovoltaic output load corresponding to the second meteorological factor, and obtaining historical data of the historical time per hour;
determining the order of each meteorological factor variable in the photovoltaic output load function according to the hourly historical data;
determining the coefficient of each meteorological factor in the photovoltaic output load function and a constant term of the photovoltaic output load function based on a least square method according to the hourly historical data;
and constructing the photovoltaic output load function according to the order of each meteorological factor variable, the coefficient of each meteorological factor and the constant term.
Optionally, the determining the predicted load reduction value of the time to be predicted according to the actual load reduction value of the target area includes:
acquiring actual photovoltaic internet load of historical time per hour;
calculating the sum of the actual photovoltaic internet load and the actual photovoltaic output load of the corresponding time to obtain an actual load reduction value per hour;
the predicted load reduction value is determined from the actual load reduction value per hour based on wavelet analysis.
Optionally, the first and second meteorological factors include illumination intensity, rainfall, and/or average temperature.
In a second aspect, there is provided an area internet load prediction apparatus in photovoltaic power generation, the apparatus comprising:
the weather factor acquisition module is used for acquiring a first weather factor of the target area in the time to be predicted;
the output load prediction module is used for inputting the first meteorological factors into a photovoltaic output load function to obtain a predicted photovoltaic output load of the target area at the time to be predicted; the photovoltaic output load function is obtained by fitting according to a second meteorological factor of the target area in the history time and the actual photovoltaic output load;
the load reduction value determining module is used for determining a predicted load reduction value of the time to be predicted according to the actual load reduction value of the target area;
and the internet surfing load prediction module is used for calculating the difference between the predicted load reduction value and the predicted photovoltaic output load to obtain the predicted internet surfing load of the target area at the time to be predicted.
Optionally, the inputting the first meteorological factor into a photovoltaic output load function to obtain a predicted photovoltaic output load of the target area before the time to be predicted, where the apparatus further includes:
the historical data acquisition module is used for acquiring a second meteorological factor of the historical time per hour and an actual photovoltaic output load corresponding to the second meteorological factor to obtain historical data of the historical time per hour;
the variable order determining module is used for determining the order of each meteorological factor variable in the photovoltaic output load function according to the hourly historical data;
the constant term determining module is used for determining the coefficient of each meteorological factor in the photovoltaic output load function and the constant term of the photovoltaic output load function based on a least square method according to the hourly historical data;
and the function construction module is used for constructing the photovoltaic output load function according to the order of each meteorological factor variable, the coefficient of each meteorological factor and the constant term.
Optionally, the load reduction value determining module is configured to:
acquiring actual photovoltaic internet load of historical time per hour;
calculating the sum of the actual photovoltaic internet load and the actual photovoltaic output load of the corresponding time to obtain an actual load reduction value per hour;
the predicted load reduction value is determined from the actual load reduction value per hour based on wavelet analysis.
Optionally, the first and second meteorological factors include illumination intensity, rainfall, and/or average temperature.
The invention has the beneficial effects that: acquiring a first meteorological factor of a target area in a time to be predicted; inputting a first meteorological factor into a photovoltaic output load function to obtain a predicted photovoltaic output load of a target area in a time to be predicted; the photovoltaic output load function is obtained by fitting a second meteorological factor of the target area in the history time and an actual photovoltaic output load; determining a predicted load reduction value of the time to be predicted according to the actual load reduction value of the target area; calculating the difference between the predicted load reduction value and the predicted photovoltaic output load to obtain the predicted internet load of the target area at the time to be predicted; the problem that the load regularity in the electricity utilization area of the photovoltaic power generation system is low, and the standard prediction accuracy is difficult to reach by adopting the traditional load prediction method can be solved; the photovoltaic power generation output load is stripped from the internet load, and then modeling prediction is carried out on the illumination intensity, the rainfall and the average temperature of the photovoltaic power generation output load combined region; predicting an actual load reduction value by adopting a conventional wavelet analysis method; thus, the prediction accuracy of the two partial loads is obviously improved, and the overall internet load prediction accuracy can be improved.
The foregoing description is only an overview of the present invention, and is intended to provide a better understanding of the present invention, as it is embodied in the following description, with reference to the preferred embodiments of the present invention and the accompanying drawings.
Drawings
Fig. 1 is a flowchart of a method for predicting regional internet load in photovoltaic power generation according to an embodiment of the present disclosure;
fig. 2 is a block diagram of a regional internet load prediction device in photovoltaic power generation according to an embodiment of the present application.
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
First, several terms involved in the present application are explained.
Fig. 1 is a flowchart of a method for predicting regional internet load in photovoltaic power generation according to an embodiment of the present application. The method at least comprises the following steps:
The first meteorological factors include illumination intensity, rainfall, and/or average temperature.
Step 102, inputting the first meteorological factors into a photovoltaic output load function to obtain a predicted photovoltaic output load of the target area in the time to be predicted.
The photovoltaic output load function is obtained by fitting the second meteorological factors of the target area in the historical time and the actual photovoltaic output load.
Optionally, a photovoltaic output load function needs to be constructed prior to this step. Building a photovoltaic output load function, comprising: acquiring a second meteorological factor of the historical time per hour and an actual photovoltaic output load corresponding to the second meteorological factor, and acquiring historical data of the historical time per hour; determining the order of each meteorological factor variable in the photovoltaic output load function according to the historical data of each hour; determining the coefficient of each meteorological factor in the photovoltaic output load function and a constant term of the photovoltaic output load function based on a least square method according to the historical data of each hour; and constructing a photovoltaic output load function according to the order of each meteorological factor variable, the coefficient and constant term of each meteorological factor.
The second weather factor is the same as the first weather factor in kind, including illumination intensity, rainfall and/or average temperature.
Assuming that the volt-age load function is a function of the illumination intensity, the rainfall and the average temperature, denoted as f (I, R, T), wherein I is the illumination intensity; r is rainfall; and T is the average temperature, the load function of the photovoltaic output is as follows:
f(I,R,T)＝αI n +βR m +γT l +δ
wherein, alpha is the coefficient of the illumination intensity of the meteorological factor variable, beta is the coefficient of the rainfall of the meteorological factor variable, gamma is the coefficient of the average temperature of the meteorological factor variable, delta is a constant term, n is the order of the illumination intensity of the meteorological factor variable, m is the order of the rainfall of the meteorological factor variable, and l is the order of the average temperature of the meteorological factor variable.
Suppose that the history data of N days per hour is acquired as follows, N being a positive integer:
I N ＝{i 1 ,i 2 ,…,i 24N-1 ,i 24N }
R N ＝{r 1 ,r 2 ,…,r 24N-1 ,r 24N }
T N ＝{t 1 ,t 2 ,…,t 24N-1 ,t 24N }
G N ＝{g 1 ,g 2 ,…,g 24N-1 ,g 24N }
wherein I is N The illumination intensity per hour for the historical time; r is R N Rainfall per hour for historical time; t (T) N When it is historyAverage temperature per hour; g N Is the actual photovoltaic output load per hour of historical time.
Thereafter, for I N 、R N 、T N 、G N And carrying out data fitting in the substitute photovoltaic output load function to obtain the order of the illumination intensity of the meteorological factor variable, the order of the rainfall capacity of the meteorological factor variable and the order of the average temperature of the meteorological factor variable.
The result after fitting is assumed to be:
the volt-age load function is:
the values of α, β, γ, and δ are then determined using a least squares method.
Assume that:
G＝G N T ＝[g 1 ,g 2 ,…,g 24N-1 ,g 24N ] T
wherein:
X＝[(i 1 ,r 1 ,t 1 ),(i 2 ,r 2 ,t 2 ),(i 3 ,r 3 ,t 3 ),…(i 24N ,r 24N ,t 24N )] T
the objective function is:
using dwell conditions
Solving to obtain S, wherein S is the set of alpha, beta, gamma and delta.
The final fit resulted in a volt-age load function of:
and then, inputting the first meteorological factor into the photovoltaic output load function obtained by final fitting to obtain the predicted photovoltaic output load of the target area in the time to be predicted.
And step 103, determining a predicted load reduction value of the time to be predicted according to the actual load reduction value of the target area.
Optionally, determining the predicted load reduction value of the time to be predicted according to the actual load reduction value of the target area includes: acquiring actual photovoltaic internet load of historical time per hour; calculating the sum of the actual photovoltaic internet load and the actual photovoltaic output load of the corresponding time to obtain an actual load reduction value per hour; the predicted load reduction value is determined from the actual load reduction value per hour based on wavelet analysis.
Assume an actual photovoltaic internet load L of historical time per hour N The method comprises the following steps:
L N ＝{l 1 ,l 2 ,…,l 24N-1 ,l 24N }
actual load reduction value=l N +F(I，R，T)。
Since the change of the actual load reduction value is regular, the predicted load reduction value of the time to be predicted can be obtained by wavelet analysis based on the historical actual load reduction value.
And 104, calculating the difference between the predicted load reduction value and the predicted photovoltaic output load to obtain the predicted internet load of the target area at the time to be predicted.
In summary, according to the method for predicting the regional internet load in the photovoltaic power generation provided by the embodiment, the first meteorological factors of the target region in the time to be predicted are obtained; inputting a first meteorological factor into a photovoltaic output load function to obtain a predicted photovoltaic output load of a target area in a time to be predicted; the photovoltaic output load function is obtained by fitting a second meteorological factor of the target area in the history time and an actual photovoltaic output load; determining a predicted load reduction value of the time to be predicted according to the actual load reduction value of the target area; calculating the difference between the predicted load reduction value and the predicted photovoltaic output load to obtain the predicted internet load of the target area at the time to be predicted; the problem that the load regularity in the electricity utilization area of the photovoltaic power generation system is low, and the standard prediction accuracy is difficult to reach by adopting the traditional load prediction method can be solved; the photovoltaic power generation output load is stripped from the internet load, and then modeling prediction is carried out on the illumination intensity, the rainfall and the average temperature of the photovoltaic power generation output load combined region; predicting an actual load reduction value by adopting a conventional wavelet analysis method; thus, the prediction accuracy of the two partial loads is obviously improved, and the overall internet load prediction accuracy can be improved.
Fig. 2 is a block diagram of a regional internet load prediction device in photovoltaic power generation according to an embodiment of the present application. The device at least comprises the following modules: the system comprises a meteorological factor acquisition module 210, an output load prediction module 220, a load reduction value determination module 230 and a surfing load prediction module 240.
A weather factor obtaining module 210, configured to obtain a first weather factor of the target area at a time to be predicted;
the output load prediction module 220 is configured to input the first meteorological factor into a photovoltaic output load function, so as to obtain a predicted photovoltaic output load of the target area at the time to be predicted; the photovoltaic output load function is obtained by fitting according to a second meteorological factor of the target area in the history time and the actual photovoltaic output load;
a load reduction value determining module 230, configured to determine a predicted load reduction value of the time to be predicted according to an actual load reduction value of the target area;
the internet load prediction module 240 is configured to calculate a difference between the predicted load reduction value and the predicted photovoltaic output load, so as to obtain a predicted internet load of the target area at the time to be predicted.
Optionally, the inputting the first meteorological factor into a photovoltaic output load function to obtain a predicted photovoltaic output load of the target area before the time to be predicted, where the apparatus further includes: the system comprises a historical data acquisition module, a variable order determination module, a constant term determination module and a function construction module.
The historical data acquisition module is used for acquiring a second meteorological factor of the historical time per hour and an actual photovoltaic output load corresponding to the second meteorological factor to obtain historical data of the historical time per hour;
the variable order determining module is used for determining the order of each meteorological factor variable in the photovoltaic output load function according to the hourly historical data;
the constant term determining module is used for determining the coefficient of each meteorological factor in the photovoltaic output load function and the constant term of the photovoltaic output load function based on a least square method according to the hourly historical data;
and the function construction module is used for constructing the photovoltaic output load function according to the order of each meteorological factor variable, the coefficient of each meteorological factor and the constant term.
Optionally, the load reduction value determining module 230 is configured to:
acquiring actual photovoltaic internet load of historical time per hour;
calculating the sum of the actual photovoltaic internet load and the actual photovoltaic output load of the corresponding time to obtain an actual load reduction value per hour;
the predicted load reduction value is determined from the actual load reduction value per hour based on wavelet analysis.
Optionally, the first and second meteorological factors include illumination intensity, rainfall, and/or average temperature.
For relevant details reference is made to the method embodiments described above.
It should be noted that: when the regional internet load prediction device in photovoltaic power generation provided in the above embodiment performs regional internet load prediction in photovoltaic power generation, only the division of the functional modules is used for illustration, in practical application, the functional allocation may be completed by different functional modules according to needs, that is, the internal structure of the regional internet load prediction device in photovoltaic power generation is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the device for predicting the regional internet load in photovoltaic power generation provided in the above embodiment and the method embodiment for predicting the regional internet load in photovoltaic power generation belong to the same concept, and detailed implementation processes of the device are referred to the method embodiment, and are not repeated here.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (6)
1. The regional internet load prediction method in the photovoltaic power generation is characterized by comprising the following steps of:
acquiring a first meteorological factor of a target area in a time to be predicted;
inputting the first meteorological factors into a photovoltaic output load function to obtain a predicted photovoltaic output load of the target area in the time to be predicted; the photovoltaic output load function is obtained by fitting according to a second meteorological factor of the target area in the history time and the actual photovoltaic output load;
determining a predicted load reduction value of the time to be predicted according to the actual load reduction value of the target area;
calculating the difference between the predicted load reduction value and the predicted photovoltaic output load to obtain the predicted internet load of the target area at the time to be predicted;
the determining the predicted load reduction value of the time to be predicted according to the actual load reduction value of the target area comprises the following steps:
acquiring actual photovoltaic internet load of historical time per hour;
calculating the sum of the actual photovoltaic internet load and the actual photovoltaic output load of the corresponding time to obtain an actual load reduction value per hour;
the predicted load reduction value is determined from the actual load reduction value per hour based on wavelet analysis.
2. The method of claim 1, wherein said inputting the first meteorological factor into a photovoltaic output load function results in the target area prior to the predicted photovoltaic output load for the time to be predicted, further comprising:
acquiring a second meteorological factor of the historical time per hour and an actual photovoltaic output load corresponding to the second meteorological factor, and obtaining historical data of the historical time per hour;
determining the order of each meteorological factor variable in the photovoltaic output load function according to the hourly historical data;
determining the coefficient of each meteorological factor in the photovoltaic output load function and a constant term of the photovoltaic output load function based on a least square method according to the hourly historical data;
and constructing the photovoltaic output load function according to the order of each meteorological factor variable, the coefficient of each meteorological factor and the constant term.
3. The method of claim 1 or 2, wherein the first and second meteorological factors comprise illumination intensity, rainfall and/or average temperature.
4. An area internet load prediction device in photovoltaic power generation, which is characterized by comprising:
the weather factor acquisition module is used for acquiring a first weather factor of the target area in the time to be predicted;
the output load prediction module is used for inputting the first meteorological factors into a photovoltaic output load function to obtain a predicted photovoltaic output load of the target area at the time to be predicted; the photovoltaic output load function is obtained by fitting according to a second meteorological factor of the target area in the history time and the actual photovoltaic output load;
the load reduction value determining module is used for determining a predicted load reduction value of the time to be predicted according to the actual load reduction value of the target area;
the internet load prediction module is used for calculating the difference between the predicted load reduction value and the predicted photovoltaic output load to obtain the predicted internet load of the target area at the time to be predicted;
the load reduction value determining module is used for:
acquiring actual photovoltaic internet load of historical time per hour;
calculating the sum of the actual photovoltaic internet load and the actual photovoltaic output load of the corresponding time to obtain an actual load reduction value per hour;
the predicted load reduction value is determined from the actual load reduction value per hour based on wavelet analysis.
5. The apparatus of claim 4, wherein said inputting said first meteorological factor into a photovoltaic output load function results in a predicted photovoltaic output load of said target area prior to said time to be predicted, said apparatus further comprising:
the historical data acquisition module is used for acquiring a second meteorological factor of the historical time per hour and an actual photovoltaic output load corresponding to the second meteorological factor to obtain historical data of the historical time per hour;
the variable order determining module is used for determining the order of each meteorological factor variable in the photovoltaic output load function according to the hourly historical data;
the constant term determining module is used for determining the coefficient of each meteorological factor in the photovoltaic output load function and the constant term of the photovoltaic output load function based on a least square method according to the hourly historical data;
and the function construction module is used for constructing the photovoltaic output load function according to the order of each meteorological factor variable, the coefficient of each meteorological factor and the constant term.
6. The apparatus of claim 4 or 5, wherein the first and second weather factors comprise light intensity, rainfall, and/or average temperature.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910279806.1A CN109978277B (en) | 2019-04-09 | 2019-04-09 | Regional internet load prediction method and device in photovoltaic power generation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910279806.1A CN109978277B (en) | 2019-04-09 | 2019-04-09 | Regional internet load prediction method and device in photovoltaic power generation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109978277A CN109978277A (en) | 2019-07-05 |
| CN109978277B true CN109978277B (en) | 2023-05-30 |
Family
ID=67083562
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910279806.1A Active CN109978277B (en) | 2019-04-09 | 2019-04-09 | Regional internet load prediction method and device in photovoltaic power generation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109978277B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111262873B (en) * | 2020-01-20 | 2022-06-03 | 安徽大学 | User login feature prediction method and device based on wavelet decomposition |
| CN112330032B (en) * | 2020-11-09 | 2023-01-24 | 中国联合网络通信集团有限公司 | Active power output prediction method and device of photovoltaic power plant |
| CN112615374B (en) * | 2020-12-25 | 2022-07-19 | 国网北京市电力公司 | Coal-to-electricity load reduction calculation method, system, device and storage medium |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP6118975B2 (en) * | 2015-02-27 | 2017-05-10 | 株式会社Ｅ．Ｉ．エンジニアリング | Energy prediction system, method of operating energy prediction system, computer program for executing the same, computer-readable recording medium storing this program, and driving support system |
| CN105337415A (en) * | 2015-11-18 | 2016-02-17 | 深圳合纵能源技术有限公司 | Regional power grid dispatching system and method based on prediction control |
| CN108053149A (en) * | 2018-01-05 | 2018-05-18 | 东南大学 | A kind of photovoltaic electric station grid connection methods of risk assessment for considering meteorologic factor |
| CN108429288B (en) * | 2018-04-12 | 2021-10-01 | 国网湖北省电力有限公司荆州供电公司 | Off-grid type microgrid energy storage optimization configuration method considering demand response |
| CN108808730B (en) * | 2018-06-12 | 2020-08-18 | 国网山东省电力公司聊城供电公司 | Photovoltaic space-time distribution power distribution network system load reserve capacity calculation method and system |
-
2019
- 2019-04-09 CN CN201910279806.1A patent/CN109978277B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN109978277A (en) | 2019-07-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109494723B (en) | Micro-grid system and control and power generation amount prediction method thereof | |
| CN109978277B (en) | Regional internet load prediction method and device in photovoltaic power generation | |
| CN104268800B (en) | Wind-electricity integration peak regulation balance decision method based on scene library | |
| CN109376951B (en) | Photovoltaic probability prediction method | |
| Li et al. | Data-based statistical property analyzing and storage sizing for hybrid renewable energy systems | |
| CN113315171B (en) | New energy consumption capacity evaluation method and device, computer equipment and storage medium | |
| Long et al. | Configuration optimization and analysis of a large scale PV/wind system | |
| CN107453396B (en) | Multi-objective optimization scheduling method for output of distributed photovoltaic power supply | |
| CN112396220A (en) | Optimal scheduling method containing wind power and demand side resources based on scene reduction | |
| Lv et al. | A probability model of PV for the middle-term to long-term power system analysis and its application | |
| KR101635450B1 (en) | PV (Photo-Voltaic) generation forecasting system for the city energy management system based on weather information | |
| Zhang et al. | A simulation-based policy improvement method for joint-operation of building microgrids with distributed solar power and battery | |
| CN111200293A (en) | Battery loss and distributed power grid battery energy storage day-ahead random scheduling method | |
| CN116826714A (en) | Power distribution method, system, terminal and storage medium based on photovoltaic power generation | |
| CN110598898A (en) | Open-acquisition-based weather data photovoltaic prediction method and system and storage medium | |
| Jimenez-Castillo et al. | Smart meters for the evaluation of self-consumption in zero energy buildings | |
| CN111105096A (en) | New energy grid-connected layout planning method and system | |
| CN116417996A (en) | Method, device, equipment and medium for controlling generating capacity of photovoltaic power generation | |
| CN114006392B (en) | Energy storage node capacity demand estimation system of distributed power system | |
| Trashchenkov et al. | Kernel density estimation for stochastic modeling of PV power output | |
| Koike et al. | Planning of optimal daily power generation tolerating prediction uncertainty of demand and photovoltaics | |
| Takeda et al. | Distributed stochastic control of microgrids based on PV power predictions | |
| CN118137500B (en) | Photovoltaic power generation prediction method and device | |
| Sachs et al. | Combined probabilistic and set-based uncertainties for a stochastic model predictive control of island energy systems | |
| CN113689045B (en) | Photovoltaic area grid-connected electric quantity prediction method, device and medium |
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 |