CN110222879B - System-bus load prediction coordination method and device considering confidence interval - Google Patents
System-bus load prediction coordination method and device considering confidence interval Download PDFInfo
- Publication number
- CN110222879B CN110222879B CN201910416505.9A CN201910416505A CN110222879B CN 110222879 B CN110222879 B CN 110222879B CN 201910416505 A CN201910416505 A CN 201910416505A CN 110222879 B CN110222879 B CN 110222879B
- Authority
- CN
- China
- Prior art keywords
- coordination
- value
- load
- data
- sharing
- 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
- 238000000034 method Methods 0.000 title claims abstract description 44
- 238000004364 calculation method Methods 0.000 claims description 23
- 238000005315 distribution function Methods 0.000 claims description 14
- 238000007781 pre-processing Methods 0.000 claims description 9
- 238000010276 construction Methods 0.000 claims description 5
- 230000006870 function Effects 0.000 claims description 3
- 238000013480 data collection Methods 0.000 claims description 2
- 230000009286 beneficial effect Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 230000015572 biosynthetic process Effects 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 238000010248 power generation Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical 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
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (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)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Development Economics (AREA)
- Power Engineering (AREA)
- Entrepreneurship & Innovation (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a system-bus load forecasting and coordinating method considering confidence intervals, which comprises the following steps: data acquisition, including actual data and predicted data of system and each bus load; constructing a time-sharing credibility model according to the acquired data; calculating a coordination value corresponding to the system load and each bus load through the time-sharing credibility model; and judging whether the coordination value meets the requirement or not according to the confidence interval, and outputting a coordination result when the coordination value meets the requirement. The invention coordinates the prediction results of the system load and the bus load to achieve the purpose of equal sum of the system load and the bus load.
Description
Technical Field
The invention relates to the technical field of power load prediction, in particular to a system-bus load prediction coordination method and device considering a confidence interval.
Background
The bus load prediction can provide a power prediction value of each bus, which can be generally understood as the load of off-grid; the system total load prediction curve can be used for making a power generation plan and generally represents the load of a power generation caliber or a power supply caliber. Therefore, in a physical sense, the two prediction results should essentially satisfy the "direct addition" characteristic, that is, the system load should be equal to the cumulative sum of the loads of the buses, excluding the plant power and the line loss.
Although the traditional bus load prediction method based on the distribution factors theoretically meets the characteristic of direct addition, when load prediction is actually carried out, in the process of predicting system load and bus load respectively, due to the difference of the considered prediction model, prediction method and characteristics and inevitable errors of prediction, the prediction results of the two methods often hardly meet the characteristic of direct addition. If the difference between the two predicted results is large, a large deviation can occur in the system power balance characterized by the power flow.
Disclosure of Invention
In order to solve the technical problems, the invention provides a system-bus load prediction coordination method and device considering a confidence interval, and the purpose of equalizing the sum of the system load and the bus load is achieved by coordinating the prediction results of the system load and the bus load. The technical scheme is as follows:
in a first aspect, an embodiment of the present invention provides a system-bus load prediction coordination method considering a confidence interval, including:
data acquisition, including actual data and predicted data of the system and each bus load;
constructing a time-sharing credibility model according to the acquired data;
calculating a coordination value corresponding to the system load and each bus load through the time-sharing credibility model;
and judging whether the coordination value meets the requirement or not according to the confidence interval, and outputting a coordination result when the coordination value meets the requirement.
In a first possible implementation manner of the first aspect of the present invention, the constructing a time-sharing reliability model according to the collected data includes:
obtaining a bus load sum ratio coefficient by counting historical load actual data;
calculating a time-sharing credibility value based on the historical load prediction data and the historical load actual data;
and constructing a time-sharing credibility model by using the bus load sum ratio coefficient and the time-sharing credibility value.
In a second possible implementation manner of the first aspect of the present invention, the time-sharing reliability value is calculated based on the historical load predicted data and the historical load actual data, and specifically includes:
calculating to obtain a relative prediction error value by using the historical load predicted value and the historical load actual value;
and calculating a corresponding time-sharing reliability value based on the relative prediction error value and the original prediction value.
In a third possible implementation manner of the first aspect of the present invention, the determining, according to the confidence interval, whether the coordination value meets the requirement, and outputting a coordination result when the coordination value meets the requirement includes:
respectively solving probability density distribution functions of the system load coordination values and the bus load coordination values according to historical prediction precision;
obtaining a corresponding confidence interval according to the probability density distribution function;
judging according to the confidence interval, and recalculating the coordination value when any coordination value is smaller than the lower confidence limit or larger than the upper confidence limit; and when all the coordination values are within the corresponding confidence interval ranges, outputting a coordination result.
In a fourth possible implementation manner of the first aspect of the present invention, after the data acquisition includes actual data and predicted data of a system and loads of each bus, the data acquisition further includes:
and preprocessing the acquired historical data by adopting a Lagrange interpolation method to obtain preprocessed historical data.
In a second aspect, an embodiment of the present invention provides a system-bus load prediction coordination apparatus considering a confidence interval, including:
the data acquisition module is used for data acquisition and comprises actual data and predicted data of the system and each bus load;
the model construction module is used for constructing a time-sharing credibility model according to the acquired data;
the coordination value calculation module is used for calculating a coordination value corresponding to the system load and each bus load through the time-sharing credibility model;
and the result output module is used for judging whether the coordination value meets the requirement or not according to the confidence interval, and outputting a coordination result when the coordination value meets the requirement.
In a first possible implementation manner of the second aspect of the present invention, the model building module includes:
the coefficient calculation module is used for obtaining a bus load sum ratio coefficient by counting historical load actual data;
the credibility calculation module is used for calculating a time-sharing credibility value based on the historical load prediction data and the historical load actual data;
and the credibility model building module is used for building a time-sharing credibility model by utilizing the bus load sum ratio coefficient and the time-sharing credibility value.
In a second possible implementation manner of the second aspect of the present invention, the credibility calculation module includes:
the error value calculation module is used for calculating a relative prediction error value by utilizing the historical load predicted value and the historical load actual value;
and the time-sharing reliability value calculation module is used for calculating a corresponding time-sharing reliability value based on the relative prediction error value and the original prediction value.
In a third possible implementation manner of the second aspect of the present invention, the result output module includes:
the function construction module is used for respectively solving probability density distribution functions of the system load coordination values and the bus load coordination values according to historical prediction precision;
the confidence interval setting module is used for obtaining a corresponding confidence interval according to the probability density distribution function;
the coordination value judging module is used for judging according to the confidence interval and recalculating the coordination value when any coordination value is smaller than the lower confidence limit or larger than the upper confidence limit; and when all the coordination values are within the corresponding confidence interval ranges, outputting a coordination result.
In a fourth possible implementation manner of the second aspect of the present invention, the system-bus load prediction and coordination apparatus considering the confidence interval further includes a preprocessing module, configured to preprocess the obtained historical data by using a lagrange interpolation method, so as to obtain preprocessed historical data.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
the system-bus load forecasting and coordinating method considering the confidence interval obtains the rule of error formation from historical data analysis by obtaining historical data comprising actually obtained historical load data and forecasted historical load data, and constructs a time-sharing credibility model according to the rule, wherein the time-sharing credibility model is used for adjusting the current system load and the original forecasting value of each bus load, the finally obtained coordination value of the system load and the coordination value of each bus load are more consistent with the direct addition characteristic in the actual application, compared with the original forecasting value, the deviation between the coordination value of the system load and the coordination value of each bus load is eliminated, the time-sharing credibility model is favorable for rapidly processing the original forecasting data, the data processing efficiency is improved, and meanwhile, the model is optimized according to the historical data continuously, so that the flexibility and the practicability are higher. In addition, a confidence interval judgment method is adopted, so that the coordination value is iterated continuously, the obtained coordination result is more in accordance with the rule of probability, and the rationality of the coordination result is ensured.
Drawings
FIG. 1 is a flow chart of steps of a system-bus load forecasting coordination method with confidence intervals taken into account in an embodiment of the present invention;
FIG. 2 is a schematic diagram of a time-sharing confidence model of a system-bus load prediction coordination method considering confidence intervals in an embodiment of the present invention;
FIG. 3 is a schematic diagram of a system-bus load forecasting coordination method with confidence intervals taken into account in an embodiment of the present invention;
fig. 4 is a block diagram of a system-bus load prediction coordination apparatus considering a confidence interval in the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a method for coordinating system-bus load prediction considering confidence intervals according to an exemplary embodiment of the present invention is shown, including:
s101, data acquisition, including actual data and prediction data of system and each bus load; wherein the historical data comprises the saved actual load data and the load prediction data in the prediction record.
S102, constructing a time-sharing credibility model according to the acquired data;
s103, calculating a coordination value of the system load and a coordination value of each bus load through the time-sharing credibility model according to the predicted value of the system load and the predicted value of each bus load;
and S104, judging whether the coordination value meets the requirement or not according to the confidence interval, and outputting a coordination result when the coordination value meets the requirement.
In this embodiment, the time-sharing reliability model is constructed by a weighted least square method, and a mathematical expression of the time-sharing reliability model is as follows:
wherein, ω is b,t The reliability of the predicted value at the moment t of the b-th bus is obtained;
P b,t respectively obtaining an original predicted value and a coordination value of the b-th bus at the time t; especially when b =0 represents the system load,
P 0,t respectively an original predicted value and a coordination value of the system load; lambda [ alpha ] t And the ratio coefficient is the sum of the bus loads at the time t.
It can be understood that the time-sharing reliability model belongs to a standard quadratic programming model, and since the reliability is a positive number, the problem of solving the time-sharing reliability model is convex quadratic programming, for which, the corresponding analysis can be solved by using a lagrange multiplier method as follows:
in this embodiment, the system-bus load prediction coordination method considering the confidence interval obtains the rule of error formation from historical data analysis by obtaining historical data including actually obtained historical load data and predicted historical load data, and constructs a time-sharing reliability model based on the rule, the time-sharing reliability model is used for adjusting the current system load and the original predicted value of each bus load, the finally obtained coordination value of the system load and the coordination value of each bus load better conform to the "direct addition" characteristic in actual application, compared with the original predicted value, the deviation between the coordination value of the system load and the coordination value of each bus load is eliminated, and the time-sharing reliability model is beneficial to rapidly process the original predicted data, so that the data processing efficiency is improved, and meanwhile, the model is continuously optimized according to the historical data, so that the method has higher flexibility and practicability. In addition, a confidence interval judgment method is adopted, so that the coordination value is iterated continuously, the obtained coordination result is more consistent with the rule of probability, and the rationality of the coordination result is ensured.
Referring to FIG. 2, the inputs are the original predicted values of the system load and the bus load
The output is a corresponding load coordination value P, and meanwhile, model parameters omega and lambda are covered in the time-sharing credibility model, wherein omega is a credibility value, and the value of omega is related to the load level and the historical prediction precision; and lambda is a bus load sum ratio coefficient, and the value of the lambda can be obtained according to historical actual load data statistics.
Preferably, a bus load sum ratio coefficient is obtained by counting historical load actual data;
calculating a time-sharing reliability value based on the historical load prediction data and the historical load actual data;
and constructing a time-sharing credibility model by using the bus load sum ratio coefficient and the time-sharing credibility value.
In the embodiment, a ratio coefficient lambda of the total load sum of the bus at the time t is calculated t And the load data can be generally obtained through historical load actual data statistics. For a certain historical day, the following method can be adopted for counting:
and (4) after the bus load total ratio coefficient of each historical day is obtained through statistics in the formula (3.1), obtaining a corresponding average value as the bus load total ratio coefficient at the final time t.
Preferably, the time-sharing reliability value is calculated based on the historical load predicted value and the historical load actual value, and specifically includes:
calculating a relative prediction error value by using the historical load predicted value and the historical load actual value;
calculating a corresponding time-sharing reliability value based on the relative prediction error value and the original prediction value; and the time-sharing credibility value is the ratio of the original prediction value to the relative prediction error value.
In this embodiment, the calculation formula of the time-sharing reliability value is:
in the formula (4.1), k is any positive number;
the original predicted value at the moment t of the b-th bus is obtained; sigma b,t The prediction precision of the moment t of the b-th bus is the accuracy of judging the prediction effect, and a relative error value is adopted in the method, and the numerical value can be calculated from a historical load predicted value and a historical load actual value. For any bus and system load, assume its historical predicted value is known to be P y Actual value of the historical load is P s Then the relative prediction error is:
in the embodiment, the feasibility of calculation is provided for the time-sharing reliability model to calculate the coordination value by calculating the bus load sum ratio coefficient and the time-sharing reliability value, and the time-sharing reliability model formed by using the bus load sum ratio coefficient and the time-sharing reliability value as parameters has fewer parameters, so that the calculation steps can be simplified as much as possible, the effect of optimizing the calculation model is achieved, and the calculation efficiency is improved.
Preferably, according to historical prediction precision, probability density distribution functions of the system load coordination values and the bus load coordination values are respectively obtained; wherein, the prediction precision is the accuracy of judging the prediction effect.
In the invention, the system load and each bus load are assumed to meet a normal distribution function, and for any bus and system load, the normal distribution function is assumed to be N (u, delta ^ 2), wherein u is an expected value, delta is a standard deviation, the numerical value can be obtained according to the corresponding historical prediction precision, and the prediction precision is taken as a relative error value.
Obtaining a corresponding confidence interval according to the probability density distribution function;
wherein for a given α (0 < α < 1), the following equation is satisfied for any load value P, according to the definition of the confidence interval:
in the formula (5.1), random interval
Is the confidence interval for P with a confidence level of 1-alpha,
the lower and upper confidence limits of the two-sided confidence interval with a confidence level of 1-alpha, respectively.
Judging according to the confidence interval, and recalculating the coordination value when any coordination value is smaller than the lower confidence limit or larger than the upper confidence limit; and when all the coordination values are within the corresponding confidence interval ranges, outputting a coordination result.
Wherein, let P b,t In order to be the value of the coordination,
to solve for the lower confidence limit of the resulting confidence interval,
is a confidence upper limit, if
Then
If it is
Then
And meanwhile, returning to the step of calculating the coordination value, and continuing to perform loop iteration until all coordination results are in the corresponding confidence interval ranges.
In this embodiment, the system-bus load prediction coordination method considering the confidence interval judges the credibility of the calculated coordination value based on the confidence intervals of the system and each bus, and ensures that the coordination result is reasonable, and the coordination values of the system load and each bus load meet the goal that the sum is equal and the total adjustment amount is minimum.
Referring to fig. 3, a method for forecasting and coordinating bus loads of a system in consideration of confidence intervals according to an exemplary embodiment of the present invention is shown, where the data collection, including actual data and forecast data of loads of the system and each bus, further includes:
and preprocessing the acquired original historical data by adopting a Lagrange interpolation method to obtain preprocessed data.
It can be understood that due to problems in measurement or transmission, abnormal phenomena such as vacancy, zero value and the like often occur in the acquired load data, particularly in the historical load data of the bus, and therefore, the method adopts the lagrangian interpolation method for preprocessing, the lagrangian interpolation method is a mature interpolation method at present, and preprocessing of the data is beneficial to improvement of integrity and usability of the data.
Referring to fig. 4, a system-bus load prediction coordination apparatus considering a confidence interval according to an exemplary embodiment of the present invention is shown, including:
the data acquisition module 201 is used for data acquisition, and comprises actual data and predicted data of the system and each bus load;
the model construction module 202 is used for constructing a time-sharing credibility model according to the acquired data;
the coordination value calculation module 203 is used for calculating the predicted value of the system load and the predicted value of each bus load through the time-sharing credibility model to obtain a coordination value of the system load and a coordination value of each bus load;
and the result output module 204 is configured to judge whether the coordination value meets the requirement according to the confidence interval, and output a coordination result when the coordination value meets the requirement.
Preferably, the model building module comprises:
the coefficient calculation module is used for obtaining a bus load sum ratio coefficient by counting historical load actual data;
the credibility calculation module is used for calculating a time-sharing credibility value based on the historical load prediction data and the historical load actual data;
and the credibility model building module is used for building a time-sharing credibility model by utilizing the bus load sum ratio coefficient and the time-sharing credibility value.
Preferably, the credibility calculation module comprises:
the error value calculation module is used for calculating a relative prediction error value by utilizing the historical load predicted value and the historical load actual value;
and the time-sharing reliability value calculation module is used for calculating a corresponding time-sharing reliability value based on the relative prediction error value and the original prediction value.
Preferably, the result output module includes:
the function building module is used for respectively solving probability density distribution functions of the system load coordination values and the bus load coordination values according to historical prediction precision;
the confidence interval setting module is used for obtaining a corresponding confidence interval according to the probability density distribution function;
the coordination value judging module is used for judging according to the confidence interval and recalculating the coordination value when any coordination value is smaller than the lower confidence limit or larger than the upper confidence limit; and when all the coordination values are within the corresponding confidence interval ranges, outputting a coordination result.
Preferably, the system-bus load prediction coordination apparatus considering the confidence interval further includes a preprocessing module, configured to preprocess the acquired historical data by using a lagrangian interpolation method, so as to obtain preprocessed historical data.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by a computer program, which may be stored in a computer readable storage medium and executed by a computer to implement the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
Claims (6)
1. A method for coordinating system-bus load forecasting considering confidence intervals, comprising:
data acquisition, including actual data and predicted data of system and each bus load;
constructing a time-sharing credibility model according to the acquired data;
calculating a coordination value corresponding to the system load and each bus load through the time-sharing credibility model;
judging whether the coordination value meets the requirement or not according to a confidence interval, and outputting a coordination result when the coordination value meets the requirement;
the method for constructing the time-sharing credibility model according to the acquired data comprises the following specific steps:
obtaining a bus load sum ratio coefficient by counting historical load actual data;
calculating a time-sharing reliability value based on the historical load prediction data and the historical load actual data;
constructing a time-sharing credibility model by using the bus load sum ratio coefficient and the time-sharing credibility value;
the time-sharing credibility value is calculated based on the historical load prediction data and the historical load actual data, and specifically comprises the following steps:
calculating a relative prediction error value by using the historical load predicted value and the historical load actual value;
and calculating a corresponding time-sharing credibility value based on the relative prediction error value and the original prediction value.
2. The system-bus load prediction coordination method considering the confidence interval as claimed in claim 1, wherein said determining whether said coordination value meets the requirement according to the confidence interval, and outputting the coordination result when said coordination value meets the requirement, comprises the following steps:
respectively solving probability density distribution functions of the system load coordination values and the bus load coordination values according to historical prediction precision;
obtaining a corresponding confidence interval according to the probability density distribution function;
judging according to the confidence interval, and recalculating the coordination value when any coordination value is smaller than the lower confidence limit or larger than the upper confidence limit; and when all the coordination values are within the corresponding confidence interval ranges, outputting a coordination result.
3. The method for coordinating system-bus load forecasting according to claim 1, wherein the data collection, after including the actual data and the forecast data of the system and each bus load, further comprises:
and preprocessing the acquired historical data by adopting a Lagrange interpolation method to obtain preprocessed historical data.
4. A system-bus load forecast coordination device considering confidence interval, comprising:
the data acquisition module is used for acquiring data, including actual data and predicted data of the system and each bus load;
the model construction module is used for constructing a time-sharing credibility model according to the acquired data;
the coordination value calculation module is used for calculating a coordination value corresponding to the system load and each bus load through the time-sharing credibility model;
the result output module is used for judging whether the coordination value meets the requirement or not according to the confidence interval, and outputting a coordination result when the coordination value meets the requirement;
the model building module comprises:
the coefficient calculation module is used for obtaining a bus load sum ratio coefficient by counting historical load actual data;
the credibility calculation module is used for calculating a time-sharing credibility value based on the historical load prediction data and the historical load actual data;
the credibility model building module is used for building a time-sharing credibility model by utilizing the bus load sum ratio coefficient and the time-sharing credibility value;
the credibility calculation module comprises:
the error value calculation module is used for calculating a relative prediction error value by utilizing the historical load predicted value and the historical load actual value;
and the time-sharing reliability value calculation module is used for calculating a corresponding time-sharing reliability value based on the relative prediction error value and the original prediction value.
5. The system-bus load forecasting coordination device according to claim 4, wherein said result output module includes:
the function construction module is used for respectively solving probability density distribution functions of the system load coordination values and the bus load coordination values according to historical prediction precision;
the confidence interval setting module is used for obtaining a corresponding confidence interval according to the probability density distribution function;
the coordination value judging module is used for judging according to the confidence interval and recalculating the coordination value when any coordination value is smaller than the lower confidence limit or larger than the upper confidence limit; and when all the coordination values are within the corresponding confidence interval ranges, outputting a coordination result.
6. The system-bus load forecast coordination device considering confidence intervals of claim 4, further comprising:
and the preprocessing module is used for preprocessing the acquired historical data by adopting a Lagrange interpolation method to obtain preprocessed historical data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910416505.9A CN110222879B (en) | 2019-05-17 | 2019-05-17 | System-bus load prediction coordination method and device considering confidence interval |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910416505.9A CN110222879B (en) | 2019-05-17 | 2019-05-17 | System-bus load prediction coordination method and device considering confidence interval |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110222879A CN110222879A (en) | 2019-09-10 |
| CN110222879B true CN110222879B (en) | 2023-02-10 |
Family
ID=67821428
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910416505.9A Active CN110222879B (en) | 2019-05-17 | 2019-05-17 | System-bus load prediction coordination method and device considering confidence interval |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110222879B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110648021B (en) * | 2019-09-12 | 2021-08-03 | 广东电网有限责任公司电力调度控制中心 | Two-stage power load prediction result coordination method, device and equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101556464A (en) * | 2009-05-22 | 2009-10-14 | 天津大学 | Auto recommending method of urban power load forecasting module based on associative rules |
| CN102930344A (en) * | 2012-10-09 | 2013-02-13 | 中国电力科学研究院 | Method for forecasting ultra-short term bus load based on load trend changes |
| CN106408105A (en) * | 2015-07-27 | 2017-02-15 | 国网浙江省电力公司金华供电公司 | Province-city-county integration-based bus load prediction management system and method |
| CN107609716A (en) * | 2017-10-12 | 2018-01-19 | 华中科技大学 | A kind of power station load setting Forecasting Methodology |
| CN107689627A (en) * | 2017-07-31 | 2018-02-13 | 国网山东省电力公司电力科学研究院 | A kind of short term coordinate forecast method of meter and branch road metric data |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2837414C (en) * | 2012-12-14 | 2022-12-13 | Battelle Memorial Institute | Transactive control and coordination framework and associated toolkit functions |
-
2019
- 2019-05-17 CN CN201910416505.9A patent/CN110222879B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101556464A (en) * | 2009-05-22 | 2009-10-14 | 天津大学 | Auto recommending method of urban power load forecasting module based on associative rules |
| CN102930344A (en) * | 2012-10-09 | 2013-02-13 | 中国电力科学研究院 | Method for forecasting ultra-short term bus load based on load trend changes |
| CN106408105A (en) * | 2015-07-27 | 2017-02-15 | 国网浙江省电力公司金华供电公司 | Province-city-county integration-based bus load prediction management system and method |
| CN107689627A (en) * | 2017-07-31 | 2018-02-13 | 国网山东省电力公司电力科学研究院 | A kind of short term coordinate forecast method of meter and branch road metric data |
| CN107609716A (en) * | 2017-10-12 | 2018-01-19 | 华中科技大学 | A kind of power station load setting Forecasting Methodology |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110222879A (en) | 2019-09-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109902801B (en) | Flood collective forecasting method based on variational reasoning Bayesian neural network | |
| CN112365029A (en) | Missing value processing method for air conditioner load prediction and air conditioner load prediction system | |
| CN114004102A (en) | Reservoir real-time flood control multi-objective robust optimization regulation and control method for resisting flood forecast error disturbance | |
| CN111680841A (en) | Short-term load prediction method and system based on principal component analysis and terminal equipment | |
| CN109118120B (en) | Multi-objective decision method considering sustainable utilization of reservoir scheduling scheme | |
| CN116595394A (en) | Training method of wind speed correction model, wind speed prediction method, wind speed prediction equipment and medium | |
| CN114819374A (en) | Regional new energy ultra-short term power prediction method and system | |
| CN113705929A (en) | Spring festival holiday load prediction method based on load characteristic curve and typical characteristic value fusion | |
| CN110222879B (en) | System-bus load prediction coordination method and device considering confidence interval | |
| CN115186923A (en) | Photovoltaic power generation power prediction method and device and electronic equipment | |
| CN111507507A (en) | Big data-based monthly water consumption prediction method | |
| CN114357670A (en) | Power distribution network power consumption data abnormity early warning method based on BLS and self-encoder | |
| CN112465266A (en) | Bus load prediction accuracy analysis method and device and computer equipment | |
| CN113659631A (en) | Wind-solar power station group output description method considering time-varying characteristics | |
| CN116562398B (en) | Power load prediction model training method, prediction method, electronic device and medium | |
| CN111476411B (en) | Photovoltaic power station generating capacity calculation method and server | |
| CN110298490A (en) | Time series Combination power load forecasting method and computer readable storage medium based on multiple regression | |
| CN116760006A (en) | Wind power prediction method, device, equipment and storage medium | |
| KR20210063017A (en) | Medium-Range heatwave forecasting system and method | |
| CN103544362B (en) | A kind of harmonic wave medium-and long-term forecasting method based on two-dimensional curve prediction | |
| CN113468811B (en) | Power grid reserve capacity probabilistic dynamic assessment method and system containing new energy unit | |
| CN114331072A (en) | Reservoir two-stage real-time flood control risk calculation method and system | |
| CN115563848A (en) | Distributed photovoltaic total radiation prediction method and system based on deep learning | |
| CN110942195A (en) | Power load prediction method and device | |
| CN111368257A (en) | Method and device for analyzing and predicting coal-to-electricity load characteristics |
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 |