CN112561159B - Hierarchical power supply and demand prediction method and system for metro level - Google Patents
Hierarchical power supply and demand prediction method and system for metro level Download PDFInfo
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Abstract
The invention discloses a method and a system for predicting the power supply and demand in a hierarchical manner facing to the city level, wherein the power supply and demand prediction is carried out in a macroscopic and microscopic combined manner, a station power supply analysis prediction result and a user power demand analysis prediction result are obtained by starting from a station and a user which have few influence factors and are simple in structure, then the hierarchical statistics of the power supply and demand prediction is carried out on a transformer area, a transformer substation, a district/county and a city in sequence on the basis of the station power supply analysis prediction result and the user power demand analysis prediction result by taking the day, the month and the year as time dimensions, and finally the city power supply analysis prediction result and the city power demand analysis prediction result are obtained. Compared with the traditional scheme, the whole power supply and demand prediction process is based on the existing power related data, and parameters are not required to be set subjectively, so that a more accurate power supply and demand prediction result can be obtained, and the rapid development and change of a power market can be adapted.
Description
Technical Field
The invention relates to the technical field of power supply and demand, in particular to a method and a system for predicting the hierarchical power supply and demand oriented to the city level.
Background
The power supply and demand forecasting index is a sensitive index reflecting national economic activities, and the result of power supply and demand forecasting can be used as a reference basis for national macro-economic policy decision, so that the development direction and speed of power enterprises are determined. Because the electric power development is highly related to the national economic development, governments and electric power enterprises at all levels pay high attention to electric power supply and demand prediction work so as to meet the demand of the public on electric power and resolve the main contradiction that the electric power development is unbalanced and insufficient.
The traditional power supply and demand forecasting method is based on power supply and demand forecasting at national and provincial levels, although the power supply and demand forecasting result can better reflect the internal relation between the power industry and national economic development and the self development law of power enterprises, and the method is widely applied to the macro decision reference level of the government and the planning development of the power enterprises. The traditional power supply and demand prediction method is mainly realized by adopting a self extrapolation method and a correlation analysis method, and various parameters need to be preset by the self extrapolation method and the correlation analysis method, and the setting of the parameters is greatly influenced by subjective factors, so that the accuracy of a power supply and demand prediction result is low, a large error exists, and the power supply and demand prediction method is difficult to adapt to the rapid development and change of a power market.
Disclosure of Invention
In view of this, the invention discloses a method and a system for predicting supply and demand of electric power in a hierarchical manner for the metro, so as to solve the problems that in the conventional scheme, the accuracy of a power supply and demand prediction result is low, a large error exists, and the rapid development and change of the power market are difficult to adapt to due to the fact that various parameters are preset by an extrapolation method and a correlation analysis method and are greatly influenced by subjective factors.
A hierarchical power supply and demand prediction method for the metro level comprises the following steps:
acquiring a station power supply analysis prediction result and a user power demand analysis prediction result, wherein the station power supply analysis prediction result comprises station supply power, station supply electric quantity, station supply rate and station output power which take days, months and years as time dimensions, and the user power demand analysis prediction result comprises user demand power, user demand electric quantity and user load rate which take days, months and years as time dimensions;
obtaining a station power supply analysis prediction result based on station level equipment modification data and all station power supply analysis prediction results in a station range, and obtaining a station power demand analysis prediction result based on all user power demand analysis prediction results in the station range, wherein the station power supply analysis prediction result comprises station supply power, station supply electric quantity, station supply rate and station output power with time dimensions of day, month and year, and the station power demand analysis prediction result comprises station demand power, station demand electric quantity and station load rate with time dimensions of day, month and year;
obtaining a substation power supply analysis prediction result based on substation-level equipment modification data and all the substation area power supply analysis prediction results in a substation area, and obtaining a substation power demand analysis prediction result based on all the substation area power demand analysis prediction results in the substation area, wherein the substation power supply analysis prediction result comprises substation supply power, substation supply electric quantity, substation supply rate and substation output power with time dimensions of day, month and year, and the substation power demand analysis prediction result comprises substation demand power, substation demand electric quantity and substation load rate with time dimensions of day, month and year;
obtaining district/county power supply analysis prediction results based on district/county-level power macroscopic information and all the substation power supply analysis prediction results within a district/county range, and obtaining district/county power demand analysis prediction results based on the district/county-level power macroscopic information and all the substation power demand analysis prediction results within the district/county range, wherein the district/county power supply analysis prediction results comprise district/county supply power, district/county supply electric quantity, district/county supply rate and district/county output power with time dimensions of day, month and year, and the district/county power demand analysis prediction results comprise district/county demand power, district/county demand electric quantity and district/county load rate with time dimensions of day, month and year;
the method comprises the steps of obtaining a prefecture power supply analysis and prediction result based on prefecture-level power macroscopic information and all district/county power supply analysis and prediction results within a prefecture range, obtaining a prefecture power supply analysis and prediction result based on the prefecture-level power macroscopic information and all district/county power demand analysis and prediction results within the prefecture range, obtaining a prefecture power demand analysis and prediction result based on the prefecture-level power macroscopic information and all district/county power demand analysis and prediction results within the prefecture range, wherein the prefecture power supply analysis and prediction result comprises prefecture supply power, prefecture supply rate and prefecture output power with time dimensions of day, month and year, and the prefecture power demand analysis and prediction result comprises prefecture demand power, prefecture demand power and prefecture load rate with time dimensions of day, month and year.
Optionally, the obtaining process of the station power supply analysis prediction result includes:
acquiring project related data, historical meteorological data, forecast meteorological data and station-level equipment modification data of stations in a transformer area;
and based on a big data technology, processing the project related data, the historical meteorological data, the forecast meteorological data and the station level equipment modification data by adopting a recurrent neural network algorithm to obtain a station power supply analysis forecast result.
Optionally, the item-related data includes: an on-going project, an on-building project, and a planning project.
Optionally, the obtaining process of the user power demand analysis prediction result includes:
acquiring residential user data, enterprise user data and business user data in a transformer area;
and processing the resident user data, the enterprise user data and the business user data by adopting a recurrent neural network algorithm based on a big data technology to obtain a user power demand analysis prediction result.
Optionally, the resident user data includes: the method comprises the following steps of (1) resident type, resident rated load, resident daily maximum load, resident daily minimum load and resident daily average load;
the enterprise user data includes: enterprise type, enterprise daily maximum load, enterprise daily minimum load and enterprise daily average load;
the business user data includes: business type, business rated load, business day maximum load, business day minimum load, and business day average load.
A hierarchical power supply and demand forecasting system for the metro level comprises the following components:
the system comprises a prediction result acquisition unit, a station power supply analysis prediction unit and a user power demand analysis prediction unit, wherein the station power supply analysis prediction result comprises station supply power, station supply electric quantity, station supply rate and station output power which take days, months and years as time dimensions, and the user power demand analysis prediction result comprises user demand power, user demand electric quantity and user load rate which take days, months and years as time dimensions;
the distribution room power supply and demand prediction unit is used for obtaining a distribution room power supply analysis prediction result based on distribution room level equipment modification data and all the station power supply analysis prediction results in a distribution room range, and obtaining a distribution room power demand analysis prediction result based on all the user power demand analysis prediction results in the distribution room range, wherein the distribution room power supply analysis prediction result comprises distribution room supply power, distribution room supply capacity, distribution room supply rate and distribution room output power with time dimensions of day, month and year, and the distribution room power demand analysis prediction result comprises distribution room demand power, distribution room demand capacity and distribution room load rate with time dimensions of day, month and year;
the transformer substation power supply and demand prediction unit is used for obtaining a transformer substation power supply analysis prediction result based on transformer substation level equipment modification data and all transformer substation power supply analysis prediction results in a transformer substation range, and obtaining a transformer substation power demand analysis prediction result based on all transformer substation power demand analysis prediction results in the transformer substation range, wherein the transformer substation power supply analysis prediction result comprises transformer substation supply power, transformer substation supply electric quantity, transformer substation supply rate and transformer substation output power with time dimensions of days, months and years, and the transformer substation power demand analysis prediction result comprises transformer substation demand power, transformer substation demand electric quantity and transformer substation load rate with the time dimensions of days, months and years;
a district/county power supply and demand prediction unit configured to obtain a district/county power supply analysis prediction result based on district/county-level power macroscopic information and all of the substation power supply analysis prediction results within a district/county scope, and obtain a district/county power demand analysis prediction result based on the district/county-level power macroscopic information and all of the substation power demand analysis prediction results within the district/county scope, the district/county power supply analysis prediction result including district/county supply power, district/county supply capacity, district/county supply rate, and district/county output power in time dimensions of day, month, and year;
the local city power supply and demand prediction unit is used for obtaining a local city power supply analysis prediction result based on local city level power macroscopic information and all district/county power supply analysis prediction results in a local city range, and obtaining a local city power demand analysis prediction result based on the local city level power macroscopic information and all district/county power demand analysis prediction results in the local city range, wherein the local city power supply analysis prediction result comprises local city supply power, local city supply electric quantity, local city supply rate and local city output power with time dimensions of day, month and year, and the local city power demand analysis prediction result comprises local city demand power, local city demand electric quantity and local city load rate with time dimensions of day, month and year.
Optionally, the prediction result obtaining unit includes: a station power supply prediction subunit;
the station power supply prediction subunit is specifically configured to:
acquiring project related data, historical meteorological data, forecast meteorological data and station-level equipment modification data of stations in a transformer area;
and based on a big data technology, processing the project related data, the historical meteorological data, the forecast meteorological data and the station-level equipment modification data by adopting a recurrent neural network algorithm to obtain a station power supply analysis and prediction result.
Optionally, the item-related data includes: an on-going project, an on-building project, and a planning project.
Optionally, the prediction result obtaining unit further includes: a consumer power demand prediction subunit;
the consumer power demand forecasting subunit is specifically configured to:
acquiring residential user data, enterprise user data and business user data in a transformer area;
and processing the resident user data, the enterprise user data and the business user data by adopting a recurrent neural network algorithm based on a big data technology to obtain a user power demand analysis prediction result.
Optionally, the resident user data includes: the method comprises the following steps of (1) resident type, resident rated load, resident daily maximum load, resident daily minimum load and resident daily average load;
the enterprise user data includes: enterprise type, enterprise daily maximum load, enterprise daily minimum load and enterprise daily average load;
the business user data includes: business type, business rated load, business day maximum load, business day minimum load, and business day average load.
According to the technical scheme, the power supply and demand forecasting method and the system for the hierarchical power supply and demand forecasting facing to the metro and city level are used for conducting power supply and demand forecasting in a macro and micro combined mode, the method comprises the steps of starting with a site and a user which are few in influence factors and simple in structure, obtaining a site power supply analysis forecasting result and a user power demand analysis forecasting result, conducting hierarchical statistics on power supply and demand forecasting on a transformer station, a transformer substation, a district/county and a metro based on the site power supply analysis forecasting result and the user power demand analysis forecasting result in sequence with time dimensions of day, month and year, and finally obtaining a metro power supply analysis forecasting result and a metro and city power demand analysis forecasting result. Compared with the traditional scheme, the whole power supply and demand prediction process is based on the existing power related data, and parameters are not required to be set subjectively, so that a more accurate power supply and demand prediction result can be obtained, and the rapid development and change of a power market can be adapted.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the disclosed drawings without creative efforts.
Fig. 1 is a flow chart of a method for predicting supply and demand of hierarchical electric power facing to the metro level disclosed in the embodiment of the present invention;
FIG. 2 is a schematic diagram of a hierarchical urban-level-oriented hierarchical power supply and demand prediction content based on combination of macro and micro technologies according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a hierarchical power supply and demand prediction system for the metro level according to an 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 flow chart of a hierarchical power supply and demand prediction method for the metro level disclosed in the embodiment of the present invention includes:
step S101, acquiring a station power supply analysis prediction result and a user power demand analysis prediction result;
the station power supply analysis prediction result comprises station supply power, station supply electric quantity, station supply rate and station output power, wherein the time dimensions of days, months and years are used as the station supply power, the station supply electric quantity, the station supply rate and the station output power.
The user power demand analysis prediction result comprises user demand power, user demand electric quantity and user load rate which take days, months and years as time dimensions.
It should be noted that, the users in this embodiment include: residential users, enterprise users, and business users.
Step S102, obtaining a power supply analysis prediction result of the transformer area based on transformation data of the transformer area level equipment and all power supply analysis prediction results of the stations in the transformer area range, and obtaining a power demand analysis prediction result of the transformer area based on all power demand analysis prediction results of the users in the transformer area range;
the station area power supply analysis prediction result comprises station area supply power, station area supply electric quantity, station area supply rate and station area output power which take days, months and years as time dimensions, and the station area power demand analysis prediction result comprises station area demand power, station area demand electric quantity and station area load rate which take days, months and years as time dimensions.
Specifically, statistical summation can be performed on analysis and prediction results of power supply of all stations in the station area range, analysis and prediction with time dimensions of day, month and year are performed on power supply of the station area through a regression analysis algorithm by combining modification data of the station area equipment, and analysis and prediction results of power supply of the station area are obtained.
It should be noted that, when the power supply analysis prediction result of the transformer area is obtained, not only the transformation data of the transformer area level equipment, but also the current technical development status of the transformer area level and the optimization data of the transformer area level, etc. may be combined.
Specifically, statistics and summation are carried out on analysis and prediction results of all the power demands of the users in the distribution room range, analysis and prediction are carried out on the power loads of the distribution room by taking the day, the month and the year as time dimensions, and analysis and prediction results of the power demands of the distribution room are obtained.
Step S103, obtaining a transformer substation power supply analysis prediction result based on transformer substation level equipment modification data and all transformer substation area power supply analysis prediction results in a transformer substation range, and obtaining a transformer substation power demand analysis prediction result based on all transformer substation area power demand analysis prediction results in the transformer substation range;
the transformer substation power supply analysis and prediction result comprises transformer substation supply power, transformer substation supply electric quantity, transformer substation supply rate and transformer substation output power which take days, months and years as time dimensions, and the transformer substation power demand analysis and prediction result comprises transformer substation demand power, transformer substation demand electric quantity and transformer substation load rate which take days, months and years as time dimensions.
Specifically, the analysis and prediction results of the power supply of all transformer areas in the transformer substation range can be counted and summed, and by combining with the transformation data of the transformer substation level equipment, the analysis and prediction of the time dimensions of day, month and year are performed on the power supply of the transformer substation through a regression analysis algorithm, so that the analysis and prediction results of the power supply of the transformer substation are obtained.
It should be noted that, when the substation power supply analysis prediction result is obtained, the substation level equipment modification data may be combined, and the platform area level technical development current situation and the substation level optimization data may also be combined.
Specifically, statistics and summation can be performed on analysis and prediction results of power demand of all transformer areas in the transformer substation range, analysis and prediction are performed on load demand of the transformer substation by taking day, month and year as time dimensions, and analysis and prediction results of the power demand of the transformer substation are obtained.
Step S104, obtaining district/county power supply analysis prediction results based on district/county power macroscopic information and all the transformer substation power supply analysis prediction results within the district/county range, and obtaining district/county power demand analysis prediction results based on the district/county power macroscopic information and all the transformer substation power demand analysis prediction results within the district/county range;
wherein the district/county power supply analysis prediction result comprises district/county supply power, district/county supply rate and district/county output power with the time dimensions of day, month and year, and the district/county power demand analysis prediction result comprises district/county demand power, district/county demand power and district/county load rate with the time dimensions of day, month and year.
Specifically, statistical summation can be performed on analysis and prediction results of power supply of all substations in a district/county range, and analysis and prediction with day, month and year as time dimensions are performed on district/county power supply through a regression analysis algorithm by combining with macroscopic district/county power information, such as district/county power policies, power planning, power industry development and the like, so as to obtain analysis and prediction results of district/county power supply.
When district/county level power macroscopic information is combined, correlation decoupling needs to be carried out on the city and prefecture level power macroscopic information so as to improve accuracy of a prediction result.
Specifically, statistical summation can be performed on the analysis and prediction results of the power demand of all substations in the district/county range, and the analysis and prediction results of the district/county power supply with the time dimensions of day, month and year are obtained through a regression analysis algorithm by combining with the macroscopic district/county power information, such as district/county power policy, power planning, power industry development and the like.
When district/county level power macroscopic information is combined, correlation decoupling needs to be carried out on the local and city level power macroscopic information so as to improve accuracy of a prediction result.
In order to improve the accuracy of the district/county power demand analysis prediction result, in addition to the district/county power demand analysis prediction result, the district/county power demand analysis prediction result is combined with the district/county power macroscopic information, the district/county population, population distribution, dominant income of urban residents, dominant income of rural residents, electricity price, GDP (Gross Domestic Product) and related index changes.
Step S105, obtaining a city power supply analysis prediction result based on the city-level power macroscopic information and all district/county power supply analysis prediction results in the city range, and obtaining a city power demand analysis prediction result based on the city-level power macroscopic information and all district/county power demand analysis prediction results in the city range.
The analysis and prediction result of the power supply of the city comprises the supply power of the city, the supply electric quantity of the city, the supply rate of the city and the output power of the city with the time dimensions of day, month and year, and the analysis and prediction result of the power demand of the city comprises the demand power of the city, the demand electric quantity of the city and the load rate of the city with the time dimensions of day, month and year.
Specifically, statistical summation can be performed on all district/county power supply analysis prediction results within the city-to-ground range, and analysis and prediction with time dimensions of day, month and year are performed on the city-to-ground power supply through a regression analysis algorithm by combining with city-to-ground power macroscopic information, such as city-to-ground power policy, power planning, power industry development and the like, so as to obtain the city-to-ground power supply analysis prediction results.
The analysis and prediction results of the power demand of the prefecture in the district/county within the scope of the prefecture can be counted and summed, and the analysis and prediction results of the power demand of the prefecture are obtained by combining with the macroscopic information of the power in the prefecture level, such as the power policy in the prefecture level, power planning, power industry development and the like and analyzing and predicting the power demand of the prefecture by taking days, months and years as time dimensions through a regression analysis algorithm.
It should be noted that, when the local-city-level power macro information is combined, correlation decoupling needs to be performed on the regional/county-level power macro information to improve the accuracy of the prediction result.
In order to facilitate understanding of the power supply and demand prediction content in the hierarchical power supply and demand prediction method to be protected by the invention, the invention also discloses a schematic diagram of the hierarchical power supply and demand prediction content oriented to the metro level based on the combination of the macro level and the micro level, and the detailed diagram is shown in fig. 2.
In summary, the method for predicting the power supply and demand in the hierarchical manner facing to the prefecture level disclosed by the invention performs power supply and demand prediction in a manner of combining macroscopical and microscopic manners, starts with a station and a user which have few influence factors and are simple in structure, obtains a station power supply analysis prediction result and a user power demand analysis prediction result, performs hierarchical statistics of power supply and demand prediction on a transformer area, a transformer substation, a district/county and a prefecture in sequence by taking days, months and years as time dimensions on the basis of the station power supply analysis prediction result and the user power demand analysis prediction result, and finally obtains a prefecture power supply analysis prediction result and a prefecture power demand analysis prediction result. Compared with the traditional scheme, the whole power supply and demand prediction process is based on the existing power related data and does not need to set parameters subjectively, so that a more accurate power supply and demand prediction result can be obtained, and the method can adapt to the rapid development and change of a power market.
In order to further optimize the above embodiments, the present invention discloses a process for obtaining the analysis and prediction result of the station power supply and the analysis and prediction result of the user power demand, which specifically includes the following steps:
(1) The acquisition process of the station power supply analysis prediction result comprises the following steps:
acquiring project related data, historical meteorological data, forecast meteorological data and station-level equipment modification data of stations in a transformer area;
and based on a big data technology, processing the project related data, the historical meteorological data, the forecast meteorological data and the station-level equipment modification data by adopting a recurrent neural network algorithm to obtain a station power supply analysis and prediction result.
Wherein the project-related data comprises: an on-going project, an on-building project, and a planning project.
The transport item comprises: energy type, installed capacity, power generation capacity, power curve (maximum generated power, minimum generated power, average generated power), operation state, and operation instruction history data.
The project under construction comprises: energy type, installed capacity and commissioning time.
The planning project comprises the following steps: energy type, installed capacity, and commissioning time.
(2) The process for acquiring the user power demand analysis prediction result comprises the following steps:
acquiring residential user data, enterprise user data and business user data in a transformer area;
and based on a big data technology, processing the resident user data, the enterprise user data and the business user data by adopting a recurrent neural network algorithm to obtain a user power demand analysis prediction result.
Wherein the resident user data includes: the resident type (town, rural area), the resident rated load, the resident daily maximum load, the resident daily minimum load and the resident daily average load;
the enterprise user data includes: enterprise type, enterprise daily maximum load, enterprise daily minimum load and enterprise daily average load;
the business user data includes: business type, business rated load, business day maximum load, business day minimum load, and business day average load.
In summary, the invention discloses a method for predicting the supply and demand of power in a hierarchical manner facing to the prefecture level, which predicts the supply and demand of power in a manner of combining macroscopical and microscopic manners, starts with stations and users with few influence factors and simple structures, and combines the respective historical data, the operating characteristics and the supply and demand characteristics of power markets of the stations and the users to predict the supply and demand of the power so as to ensure the accuracy of prediction, and then carries out hierarchical statistics of the supply and demand prediction of the power on a station, a transformer substation, a district/county and a prefecture in sequence by taking days, months and years as time dimensions on the basis of the analysis and prediction results of the supply of the stations and the analysis and prediction results of the demand of the power on the prefecture, so as to finally obtain the analysis and prediction results of the supply of the power on the prefecture and the analysis and prediction results of the demand of the power on the prefecture. Compared with the traditional scheme, the whole power supply and demand prediction process is based on the existing power related data, and parameters are not required to be set subjectively, so that a more accurate power supply and demand prediction result can be obtained, and the rapid development and change of a power market can be adapted.
Corresponding to the embodiment of the method, the invention also discloses a system for predicting the supply and demand of the hierarchical power facing to the city level.
Referring to fig. 3, a schematic structural diagram of a hierarchical power supply and demand prediction system for the metro level disclosed in the embodiment of the present invention includes:
a prediction result acquisition unit 201 configured to acquire a station power supply analysis prediction result and a user power demand analysis prediction result;
the station power supply analysis and prediction result comprises station supply power, station supply electric quantity, station supply rate and station output power which take days, months and years as time dimensions.
The user power demand analysis prediction result comprises user demand power, user demand electric quantity and user load rate which take days, months and years as time dimensions.
It should be noted that, the users in this embodiment include: residential users, enterprise users, and business users.
The station area power supply and demand prediction unit 202 is configured to obtain a station area power supply analysis prediction result based on the station area-level device modification data and all the station power supply analysis prediction results within the station area range, and obtain a station area power demand analysis prediction result based on all the user power demand analysis prediction results within the station area range;
the station area power supply analysis prediction result comprises station area supply power, station area supply electric quantity, station area supply rate and station area output power which take days, months and years as time dimensions, and the station area power demand analysis prediction result comprises station area demand power, station area demand electric quantity and station area load rate which take days, months and years as time dimensions.
Specifically, statistical summation can be performed on analysis and prediction results of power supply of all stations in the station area range, analysis and prediction with time dimensions of day, month and year are performed on power supply of the station area through a regression analysis algorithm by combining modification data of the station area equipment, and analysis and prediction results of power supply of the station area are obtained.
It should be noted that, when the power supply analysis prediction result of the transformer area is obtained, not only the transformation data of the transformer area level equipment, but also the current technical development status of the transformer area level and the optimization data of the transformer area level, etc. may be combined.
Specifically, statistics and summation are carried out on analysis and prediction results of all the power demands of the users in the distribution room range, analysis and prediction are carried out on the power loads of the distribution room by taking the day, the month and the year as time dimensions, and analysis and prediction results of the power demands of the distribution room are obtained.
The substation power supply and demand prediction unit 203 is used for obtaining a substation power supply analysis prediction result based on substation level equipment modification data and all the substation area power supply analysis prediction results in a substation range, and obtaining a substation power demand analysis prediction result based on all the substation area power demand analysis prediction results in the substation range;
the transformer substation power supply analysis and prediction result comprises transformer substation supply power, transformer substation supply electric quantity, transformer substation supply rate and transformer substation output power which take days, months and years as time dimensions, and the transformer substation power demand analysis and prediction result comprises transformer substation demand power, transformer substation demand electric quantity and transformer substation load rate which take days, months and years as time dimensions.
Specifically, the analysis and prediction results of the power supply of all transformer areas in the transformer substation range can be counted and summed, and by combining with the transformation data of the transformer substation level equipment, the analysis and prediction of the time dimensions of day, month and year are performed on the power supply of the transformer substation through a regression analysis algorithm, so that the analysis and prediction results of the power supply of the transformer substation are obtained.
It should be noted that, when the substation power supply analysis prediction result is obtained, the substation level equipment modification data may be combined, and the platform area level technical development current situation and the substation level optimization data may also be combined.
Specifically, statistics and summation can be performed on analysis and prediction results of power demand of all transformer areas in the transformer substation range, analysis and prediction are performed on load demand of the transformer substation by taking day, month and year as time dimensions, and analysis and prediction results of the power demand of the transformer substation are obtained.
A district/county power supply and demand prediction unit 204, configured to obtain a district/county power supply analysis prediction result based on the district/county-level power macroscopic information and all the substation power supply analysis prediction results within the district/county scope, and obtain a district/county power demand analysis prediction result based on the district/county-level power macroscopic information and all the substation power demand analysis prediction results within the district/county scope;
wherein the district/county power supply analysis prediction result comprises district/county supply power, district/county supply rate and district/county output power with the time dimensions of day, month and year, and the district/county power demand analysis prediction result comprises district/county demand power, district/county demand power and district/county load rate with the time dimensions of day, month and year.
Specifically, statistical summation can be performed on analysis and prediction results of power supply of all substations in a district/county range, and analysis and prediction with day, month and year as time dimensions are performed on district/county power supply through a regression analysis algorithm by combining with macroscopic district/county power information, such as district/county power policies, power planning, power industry development and the like, so as to obtain analysis and prediction results of district/county power supply.
When district/county level power macroscopic information is combined, correlation decoupling needs to be carried out on the local and city level power macroscopic information so as to improve accuracy of a prediction result.
Specifically, statistical summation can be performed on the analysis and prediction results of the power demand of all substations in the district/county range, and the analysis and prediction results of the district/county power supply with the time dimensions of day, month and year are obtained through a regression analysis algorithm by combining with the macroscopic district/county power information, such as district/county power policy, power planning, power industry development and the like.
When district/county level power macroscopic information is combined, correlation decoupling needs to be carried out on the city and prefecture level power macroscopic information so as to improve accuracy of a prediction result.
In order to improve the accuracy of the district/county power demand analysis prediction result, in addition to the district/county power demand analysis prediction result, the district/county power demand analysis prediction result is combined with the district/county power macroscopic information, the district/county population, population distribution, dominant income of urban residents, dominant income of rural residents, electricity price, GDP (Gross Domestic Product) and related index changes.
A local electric power supply and demand prediction unit 205, configured to obtain a local electric power supply analysis prediction result based on the local electric power macro information and all the district/county electric power supply analysis prediction results within the local scope, and obtain a local electric power demand analysis prediction result based on the local electric power macro information and all the district/county electric power demand analysis prediction results within the local scope.
The analysis and prediction result of the power supply of the city comprises the supply power of the city, the supply electric quantity of the city, the supply rate of the city and the output power of the city with the time dimensions of day, month and year, and the analysis and prediction result of the power demand of the city comprises the demand power of the city, the demand electric quantity of the city and the load rate of the city with the time dimensions of day, month and year.
Specifically, statistical summation can be performed on all district/county power supply analysis prediction results within the city-to-ground range, and analysis and prediction with time dimensions of day, month and year are performed on the city-to-ground power supply through a regression analysis algorithm by combining with city-to-ground power macroscopic information, such as city-to-ground power policy, power planning, power industry development and the like, so as to obtain the city-to-ground power supply analysis prediction results.
The analysis and prediction results of the power demand of the prefecture in the district/county within the scope of the prefecture can be counted and summed, and the analysis and prediction results of the power demand of the prefecture are obtained by combining with the macroscopic information of the power in the prefecture level, such as the power policy in the prefecture level, power planning, power industry development and the like and analyzing and predicting the power demand of the prefecture by taking days, months and years as time dimensions through a regression analysis algorithm.
It should be noted that, when the prefecture-level power macroscopic information is combined, the district/county-level power macroscopic information needs to be subjected to correlation decoupling, so as to improve the accuracy of the prediction result.
In order to facilitate understanding of the content of the layered power supply and demand prediction in the layered power supply and demand prediction system to be protected by the invention, the invention also discloses a schematic diagram of the content of the layered power supply and demand prediction oriented to the metro level based on the combination of the macro level and the micro level, and the detailed diagram is shown in fig. 2.
In summary, the hierarchical power supply and demand forecasting system for the prefecture level disclosed by the invention carries out power supply and demand forecasting in a manner of combining macroscopical and microscopic modes, starts with a station and a user which have few influence factors and simple structure, obtains a station power supply analysis forecasting result and a user power demand analysis forecasting result, carries out hierarchical statistics of power supply and demand forecasting on a transformer area, a transformer substation, a district/county and a prefecture in sequence by taking days, months and years as time dimensions on the basis of the station power supply analysis forecasting result and the user power demand analysis forecasting result, and finally obtains a prefecture power supply analysis forecasting result and a prefecture power demand analysis forecasting result. Compared with the traditional scheme, the whole power supply and demand prediction process is based on the existing power related data and does not need to set parameters subjectively, so that a more accurate power supply and demand prediction result can be obtained, and the method can adapt to the rapid development and change of a power market.
In order to further optimize the above embodiments, the present invention discloses a process for obtaining the analysis and prediction result of the station power supply and the analysis and prediction result of the user power demand, which specifically includes the following steps:
the prediction result acquisition unit 201 includes: a station power supply prediction subunit;
the station power supply prediction subunit is specifically configured to:
acquiring project related data, historical meteorological data, forecast meteorological data and station-level equipment modification data of stations in a transformer area;
and based on a big data technology, processing the project related data, the historical meteorological data, the forecast meteorological data and the station-level equipment modification data by adopting a recurrent neural network algorithm to obtain a station power supply analysis and prediction result.
Wherein the project-related data comprises: an on-going project, an on-building project, and a planning project.
The transport item comprises: energy type, installed capacity, power generation capacity, power curve (maximum generated power, minimum generated power, average generated power), operation state, and operation instruction history data.
The project under construction comprises: energy type, installed capacity and commissioning time.
The planning project comprises the following steps: energy type, installed capacity and commissioning time.
The prediction result acquisition unit 201 may further include: a consumer power demand prediction subunit;
the consumer power demand forecasting subunit is specifically configured to:
acquiring residential user data, enterprise user data and business user data in a transformer area;
and processing the resident user data, the enterprise user data and the business user data by adopting a recurrent neural network algorithm based on a big data technology to obtain a user power demand analysis prediction result.
Wherein the resident user data includes: the resident type (town, rural area), the resident rated load, the resident daily maximum load, the resident daily minimum load and the resident daily average load;
the enterprise user data includes: enterprise type, enterprise daily maximum load, enterprise daily minimum load and enterprise daily average load;
the business user data includes: business type, business rated load, business day maximum load, business day minimum load, and business day average load.
In summary, the hierarchical power supply and demand prediction system for the metro level disclosed by the invention performs power supply and demand prediction in a manner of combining macroscopicity and microcosmic, starts with stations and users with few influence factors and simple structures, performs power supply and demand prediction by combining respective historical data, operating characteristics and power market supply and demand characteristics of the stations and the users to ensure the accuracy of the prediction, and then performs hierarchical statistics of power supply and demand prediction on a transformer station, a transformer substation, a district/county and a metro in sequence by taking days, months and years as time dimensions on the basis of the analysis and prediction results of the power supply of the stations and the analysis and prediction results of the power demand of the users to finally obtain the analysis and prediction results of the metro power supply and the analysis and prediction results of the metro power demand. Compared with the traditional scheme, the whole power supply and demand prediction process is based on the existing power related data and does not need to set parameters subjectively, so that a more accurate power supply and demand prediction result can be obtained, and the method can adapt to the rapid development and change of a power market.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for predicting supply and demand of hierarchical electric power facing to the metro level is characterized by comprising the following steps:
acquiring a station power supply analysis prediction result and a user power demand analysis prediction result, wherein the station power supply analysis prediction result comprises station supply power, station supply electric quantity, station supply rate and station output power which take days, months and years as time dimensions, and the user power demand analysis prediction result comprises user demand power, user demand electric quantity and user load rate which take days, months and years as time dimensions;
obtaining a station power supply analysis prediction result based on station level equipment modification data and all station power supply analysis prediction results in a station range, and obtaining a station power demand analysis prediction result based on all user power demand analysis prediction results in the station range, wherein the station power supply analysis prediction result comprises station supply power, station supply electric quantity, station supply rate and station output power with time dimensions of day, month and year, and the station power demand analysis prediction result comprises station demand power, station demand electric quantity and station load rate with time dimensions of day, month and year;
obtaining a substation power supply analysis prediction result based on substation-level equipment modification data and all the substation area power supply analysis prediction results in a substation area, and obtaining a substation power demand analysis prediction result based on all the substation area power demand analysis prediction results in the substation area, wherein the substation power supply analysis prediction result comprises substation supply power, substation supply electric quantity, substation supply rate and substation output power with time dimensions of day, month and year, and the substation power demand analysis prediction result comprises substation demand power, substation demand electric quantity and substation load rate with time dimensions of day, month and year;
obtaining district/county power supply analysis prediction results based on district/county-level power macroscopic information and all the substation power supply analysis prediction results within a district/county range, and obtaining district/county power demand analysis prediction results based on the district/county-level power macroscopic information and all the substation power demand analysis prediction results within the district/county range, wherein the district/county power supply analysis prediction results comprise district/county supply power, district/county supply electric quantity, district/county supply rate and district/county output power with time dimensions of day, month and year, and the district/county power demand analysis prediction results comprise district/county demand power, district/county demand electric quantity and district/county load rate with time dimensions of day, month and year;
the method comprises the steps of obtaining a prefecture power supply analysis and prediction result based on prefecture-level power macroscopic information and all district/county power supply analysis and prediction results within a prefecture range, obtaining a prefecture power supply analysis and prediction result based on the prefecture-level power macroscopic information and all district/county power demand analysis and prediction results within the prefecture range, obtaining a prefecture power demand analysis and prediction result based on the prefecture-level power macroscopic information and all district/county power demand analysis and prediction results within the prefecture range, wherein the prefecture power supply analysis and prediction result comprises prefecture supply power, prefecture supply rate and prefecture output power with time dimensions of day, month and year, and the prefecture power demand analysis and prediction result comprises prefecture demand power, prefecture demand power and prefecture load rate with time dimensions of day, month and year.
2. The method for forecasting the hierarchical power supply and demand according to claim 1, wherein the obtaining process of the station power supply analysis forecasting result comprises:
acquiring project related data, historical meteorological data, forecast meteorological data and station-level equipment modification data of stations in a transformer area;
and based on a big data technology, processing the project related data, the historical meteorological data, the forecast meteorological data and the station-level equipment modification data by adopting a recurrent neural network algorithm to obtain a station power supply analysis and prediction result.
3. The hierarchical power supply and demand prediction method according to claim 2, wherein the project-related data includes: an on-going project, an on-building project, and a planning project.
4. The method according to claim 1, wherein the obtaining of the prediction result of the consumer power demand analysis comprises:
acquiring residential user data, enterprise user data and business user data in a transformer area;
and processing the resident user data, the enterprise user data and the business user data by adopting a recurrent neural network algorithm based on a big data technology to obtain a user power demand analysis prediction result.
5. The hierarchical power supply and demand prediction method according to claim 4,
the resident user data includes: the method comprises the following steps of (1) resident type, resident rated load, resident daily maximum load, resident daily minimum load and resident daily average load;
the enterprise user data includes: enterprise type, enterprise daily maximum load, enterprise daily minimum load and enterprise daily average load;
the business user data includes: business type, business rated load, business day maximum load, business day minimum load, and business day average load.
6. A hierarchical power supply and demand forecasting system oriented to the metro level is characterized by comprising the following components:
the system comprises a prediction result acquisition unit, a station power supply analysis prediction unit and a user power demand analysis prediction unit, wherein the station power supply analysis prediction result comprises station supply power, station supply electric quantity, station supply rate and station output power which take days, months and years as time dimensions, and the user power demand analysis prediction result comprises user demand power, user demand electric quantity and user load rate which take days, months and years as time dimensions;
the station area power supply and demand prediction unit is used for obtaining a station area power supply analysis prediction result based on station area level equipment modification data and all station power supply analysis prediction results in a station area range, and obtaining a station area power demand analysis prediction result based on all user power demand analysis prediction results in the station area range, wherein the station area power supply analysis prediction result comprises station area supply power, station area supply electric quantity, station area supply rate and station area output power with time dimensions of day, month and year, and the station area power demand analysis prediction result comprises station area demand power, station area demand electric quantity and station area load rate with time dimensions of day, month and year;
the transformer substation power supply and demand prediction unit is used for obtaining a transformer substation power supply analysis prediction result based on transformer substation level equipment modification data and all transformer substation power supply analysis prediction results in a transformer substation range, and obtaining a transformer substation power demand analysis prediction result based on all transformer substation power demand analysis prediction results in the transformer substation range, wherein the transformer substation power supply analysis prediction result comprises transformer substation supply power, transformer substation supply electric quantity, transformer substation supply rate and transformer substation output power with time dimensions of days, months and years, and the transformer substation power demand analysis prediction result comprises transformer substation demand power, transformer substation demand electric quantity and transformer substation load rate with the time dimensions of days, months and years;
a district/county power supply and demand prediction unit configured to obtain a district/county power supply analysis prediction result based on district/county-level power macroscopic information and all of the substation power supply analysis prediction results within a district/county scope, and obtain a district/county power demand analysis prediction result based on the district/county-level power macroscopic information and all of the substation power demand analysis prediction results within the district/county scope, the district/county power supply analysis prediction result including district/county supply power, district/county supply capacity, district/county supply rate, and district/county output power in time dimensions of day, month, and year;
the local city power supply and demand prediction unit is used for obtaining a local city power supply analysis prediction result based on local city level power macroscopic information and all district/county power supply analysis prediction results in a local city range, and obtaining a local city power demand analysis prediction result based on the local city level power macroscopic information and all district/county power demand analysis prediction results in the local city range, wherein the local city power supply analysis prediction result comprises local city supply power, local city supply electric quantity, local city supply rate and local city output power with time dimensions of day, month and year, and the local city power demand analysis prediction result comprises local city demand power, local city demand electric quantity and local city load rate with time dimensions of day, month and year.
7. The hierarchical electric power supply and demand prediction system according to claim 6, wherein the prediction result acquisition unit includes: a station power supply prediction subunit;
the station power supply prediction subunit is specifically configured to:
acquiring project related data, historical meteorological data, forecast meteorological data and station-level equipment modification data of stations in a transformer area;
and based on a big data technology, processing the project related data, the historical meteorological data, the forecast meteorological data and the station level equipment modification data by adopting a recurrent neural network algorithm to obtain a station power supply analysis forecast result.
8. The tiered power supply and demand prediction system of claim 7 wherein the project related data includes: an on-going project, an on-building project, and a planning project.
9. The hierarchical power supply and demand prediction system according to claim 6, wherein the prediction result acquisition unit further includes: a consumer power demand prediction subunit;
the consumer power demand forecasting subunit is specifically configured to:
acquiring residential user data, enterprise user data and business user data in a transformer area;
and processing the resident user data, the enterprise user data and the business user data by adopting a recurrent neural network algorithm based on a big data technology to obtain a user power demand analysis prediction result.
10. The hierarchical power supply and demand prediction system according to claim 9,
the resident user data includes: the method comprises the following steps of (1) resident type, resident rated load, resident daily maximum load, resident daily minimum load and resident daily average load;
the enterprise user data includes: enterprise type, enterprise daily maximum load, enterprise daily minimum load and enterprise daily average load;
the business user data includes: business type, business rated load, business day maximum load, business day minimum load, and business day average load.
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