CN113837898A - New energy consumption calculation method and device - Google Patents
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Abstract
The invention discloses a new energy consumption calculation method and a new energy consumption calculation device, which are used for obtaining the traditional new energy power generation amount and power prediction data and actual data of a station, the new energy power generation amount and power prediction data and actual power generation data, analyzing the load power amount and power prediction data and actual data based on the power supply range of the station, and carrying out hierarchical statistics on new energy consumption on a transformer area, a transformer substation, a district and county and a city in sequence by taking days, months and years as time dimensions to finally obtain a city-level new energy consumption calculation result. The new energy consumption calculation process is based on the existing electric power related data, and parameters are not required to be set subjectively, so that a more accurate new energy consumption calculation result can be obtained, and the new energy consumption calculation method can adapt to rapid development and change of new energy.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a new energy consumption calculation method and device.
Background
The new energy consumption index is a sensitive index reflecting the high-efficiency operation of new energy, and the result of new energy consumption calculation can be used as a national decision reference for new energy development to determine the self development direction and speed of the new energy. With the proposition of the "carbon peak-to-peak, carbon neutralization" target, governments and power enterprises at all levels are highly concerned about new energy consumption. The traditional new energy consumption calculation method is based on the nationwide and provincial level, a corresponding mathematical model is established according to the electric quantity balance, the internal relation between the change rule of historical data and various relevant factors is found out, and the new energy consumption condition is calculated by adopting a time sequence-based production simulation method, so that the prediction result has large granularity, long time consumption and low precision.
Disclosure of Invention
Aiming at the problems, the invention provides a new energy consumption calculation method and a new energy consumption calculation device, and solves the problems that the new energy consumption calculation has larger granularity, larger error and is difficult to adapt to the rapid development and change of new energy.
In order to achieve the purpose, the invention provides the following technical scheme:
a new energy consumption calculation method comprises the following steps:
calculating to obtain a station new energy consumption calculation result based on the station traditional energy power generation data, the new energy power generation data and the load data;
acquiring a new energy consumption calculation result of the transformer area based on transformer area level line data and all the station consumption calculation results within the transformer area range;
obtaining a new energy consumption calculation result of the transformer substation based on the transformer substation level line data and all the transformer area new energy consumption calculation results in the transformer substation range;
acquiring new energy consumption calculation results of the counties based on the county-level power line data and the new energy consumption calculation results of all the transformer substations within the county range;
and calculating to obtain a new energy consumption calculation result of the city based on the city-level line data and all the counties new energy consumption calculation results within the city range.
Optionally, the calculating to obtain a station new energy consumption calculation result based on the station traditional energy power generation data, the new energy power generation data, and the load data includes:
acquiring project related data, historical meteorological data, forecast meteorological data, historical generated energy, generated power and station-level line data of stations in a transformer area;
acquiring load related data, historical meteorological data, forecast meteorological data, and historical power consumption and power consumption of stations in a transformer area;
and processing the project related data, the historical meteorological data, the forecast meteorological data, the historical power generation amount, the historical power consumption and the station-level line data to obtain the new energy consumption calculation result.
Optionally, the project-related data comprises: the system includes at-ship project related data, at-construction project related data and planning project related data.
Optionally, the obtaining a station area new energy consumption calculation result based on the station area level line data and all station consumption calculation results within the station area range includes:
acquiring loads in a transformer area, wherein the loads comprise residential user data, enterprise user data and business user data;
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 load prediction result;
and determining a new energy consumption calculation result of the transformer area based on the user load prediction result.
Optionally, wherein,
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, predicted power generation amount and historical power generation amount;
the enterprise user data includes: enterprise type, daily maximum load of an enterprise, daily minimum load of the enterprise, daily average load of the enterprise, predicted power generation amount and historical power generation amount;
the business user data includes: business type, business rated load, business day maximum load, business day minimum load and business day average load, predicted power generation, historical power generation.
A new energy consumption computing device, comprising:
the first calculation unit is used for calculating to obtain a station new energy consumption calculation result based on the station traditional energy power generation data, the new energy power generation data and the load data;
the first acquisition unit is used for acquiring a new energy consumption calculation result of the transformer area based on transformer area level line data and all station consumption calculation results in a transformer area range;
the second acquisition unit is used for acquiring a new energy consumption calculation result of the transformer substation based on the transformer substation level line data and all the transformer area new energy consumption calculation results within the transformer substation range;
the third obtaining unit is used for obtaining new energy consumption calculation results of the counties based on the county-level power line data and the new energy consumption calculation results of all the substations within the county range;
and the second calculating unit is used for calculating to obtain a new energy consumption calculating result of the city based on the city-level line data and all the counties and counties new energy consumption calculating results within the city range.
Optionally, the first computing unit is specifically configured to:
acquiring project related data, historical meteorological data, forecast meteorological data, historical generated energy, generated power and station-level line data of stations in a transformer area;
acquiring load related data, historical meteorological data, forecast meteorological data, and historical power consumption and power consumption of stations in a transformer area;
and processing the project related data, the historical meteorological data, the forecast meteorological data, the historical power generation amount, the historical power consumption and the station-level line data to obtain the new energy consumption calculation result.
Optionally, the project-related data comprises: the system includes at-ship project related data, at-construction project related data and planning project related data.
Optionally, the first obtaining unit is specifically configured to:
acquiring loads in a transformer area, wherein the loads comprise residential user data, enterprise user data and business user data;
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 load prediction result;
and determining a new energy consumption calculation result of the transformer area based on the user load prediction result.
Optionally, wherein,
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, predicted power generation amount and historical power generation amount;
the enterprise user data includes: enterprise type, daily maximum load of an enterprise, daily minimum load of the enterprise, daily average load of the enterprise, predicted power generation amount and historical power generation amount;
the business user data includes: business type, business rated load, business day maximum load, business day minimum load and business day average load, predicted power generation, historical power generation.
Compared with the prior art, the invention provides a new energy consumption calculation method and a new energy consumption calculation device, wherein the traditional new energy power generation capacity and power prediction data and actual data of a station, the new energy power generation capacity and power prediction data and actual power generation data of the station are obtained, then the load power capacity and power prediction data and actual data are analyzed based on the power supply range of the station, and new energy consumption is hierarchically counted for a transformer area, a transformer substation, a district/county and a city in sequence by taking days, months and years as time dimensions, so that a new energy consumption calculation result of the city level is finally obtained. The new energy consumption calculation process is based on the existing electric power related data, and parameters are not required to be set subjectively, so that a more accurate new energy consumption calculation result can be obtained, and the new energy consumption calculation method can adapt to rapid development and change of new energy.
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 description of the embodiments or the prior art 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 provided drawings without creative efforts.
Fig. 1 is a schematic flow chart of a new energy consumption calculation method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of new energy consumption calculation according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a new energy consumption computing device 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.
The terms "first" and "second," and the like in the description and claims of the present invention and the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "comprising" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not set forth for a listed step or element but may include steps or elements not listed.
Referring to fig. 1, a schematic flow chart of a new energy consumption calculation method provided in an embodiment of the present invention is shown, where the method includes:
and S101, calculating to obtain a new energy consumption calculation result of the station based on the traditional energy power generation data, the new energy power generation data and the load data of the station.
The station traditional energy power generation data comprise station predicted power generation amount, actual power generation amount, predicted power and actual power with the time dimensions of day, month and year, the station new energy power generation data comprise station predicted power generation amount, actual power generation amount, predicted power and actual power with the time dimensions of day, month and year, and the load data comprise load predicted power amount, actual load power amount, predicted load power and actual load power with the time dimensions of day, month and year;
the station new energy consumption result comprises station new energy utilization amount, new energy utilization rate, new energy abandoned amount and new energy abandoned rate with the time dimensions of day, month and year.
The load in the present embodiment includes: residential users, enterprise users, and business users.
And S102, obtaining a new energy consumption calculation result of the transformer area based on transformer area level line data and all the station consumption calculation results in the transformer area range.
The method comprises the steps of calculating to obtain traditional energy power generation data, new energy power generation data and load data of a transformer area based on circuit data of the transformer area and all traditional energy power generation data, new energy power generation data and load data in the transformer area range to obtain a new energy consumption calculation result of the transformer area, wherein the traditional energy power generation data of the transformer area comprise predicted power generation amount, actual power generation amount, predicted power and actual power of traditional energy of the transformer area with time dimensions of day, month and year, the new energy power generation data of the transformer area comprise predicted power generation amount, actual power generation amount, predicted power and actual power of traditional energy with time dimensions of day, month and year, and the load data of the transformer area comprise predicted power amount, actual load power amount, predicted load power and actual load power of loads with time dimensions of day, month and year.
And the platform area new energy consumption calculation result comprises platform area new energy utilization quantity, new energy utilization rate, new energy electricity abandonment quantity and new energy electricity abandonment rate which take days, months and years as time dimensions.
Specifically, the new energy utilization amount, the new energy utilization rate, the new energy electricity abandonment amount and the new energy electricity abandonment rate of all stations in the station area range can be counted and summed, and by combining station area level line data, the new energy consumption condition of the station area is analyzed and calculated by a regression analysis algorithm with the dimensions of day, month and year as time dimensions, so that a new energy consumption calculation result of the station area is obtained.
S103, obtaining a new energy consumption calculation result of the transformer substation based on the transformer substation level line data and all the transformer area new energy consumption calculation results in the transformer substation range.
The method comprises the steps of calculating to obtain traditional energy power generation data, new energy power generation data and load data of a transformer substation based on line data of the transformer substation and traditional energy power generation data, new energy power generation data and load data of all transformer substations in the transformer substation range, and obtaining a new energy consumption calculation result of the transformer substation, wherein the traditional energy power generation data of the transformer substation comprise predicted power generation capacity, actual power generation capacity, predicted power and actual power of the transformer substation with time dimensions of day, month and year, the new energy power generation data of the transformer substation comprise predicted power generation capacity, actual power generation capacity, predicted power and actual power of the transformer substation with time dimensions of day, month and year, and the load data of the transformer substation comprise predicted power, actual load power, predicted load power and actual load power of the transformer substation with time dimensions of day, month and year.
The calculation result of the new energy consumption of the transformer substation comprises the new energy utilization rate, the new energy abandoned electricity amount and the new energy abandoned electricity rate of the transformer substation with the time dimensions of day, month and year.
Specifically, statistics and summation can be performed on all the station area new energy consumption calculation results in the range of the transformer substation, and analysis and calculation with the time dimensions of day, month and year are performed on the new energy consumption condition of the transformer substation through a regression analysis algorithm by combining with transformer substation-level line data, so that the transformer substation new energy consumption calculation result is obtained.
And S104, obtaining new energy consumption calculation results of the counties based on the county-level power line data and the new energy consumption calculation results of all the substations in the county range.
Calculating to obtain the traditional energy power generation data, the new energy power generation data and the load data of the district/county based on the district/county-level line data and the traditional energy power generation data, the new energy power generation data and the load data of all the substations in the district/county range to obtain the new energy consumption calculation result of the substation, the district/county traditional energy power generation data comprises predicted power generation amount, actual power generation amount, predicted power and actual power of a substation station with time dimensions of day, month and year, the district/county new energy power generation data comprise station predicted power generation amount, actual power generation amount, predicted power and actual power with the time dimensions of day, month and year, the district/county load data comprises load prediction electric quantity, actual load electric quantity, prediction load power and actual load power with the time dimension of day, month and year.
And calculating the new energy consumption of the district/county according to the new energy consumption calculation result, wherein the new energy consumption calculation result comprises the utilization amount of the new energy of the district/county, the utilization rate of the new energy, the abandoned amount of the new energy and the abandoned rate of the new energy, and the time dimension is day, month and year.
Specifically, statistical summation can be performed on all new energy consumption calculation results of the transformer substations in the district/county range, and analysis and calculation are performed on the new energy consumption conditions of the district/county by taking days, months and years as time dimensions through a regression analysis algorithm by combining with district/county-level line information, such as district/county-level power planning, power industry development and the like, so that the new energy consumption calculation results of the district/county are obtained.
And S105, calculating to obtain a new energy consumption calculation result of the city based on the city-level line data and all the counties and counties within the city range.
On the basis of city-level line data and traditional energy power generation data, new energy power generation data and load data of all the districts/counties within a city range, traditional energy power generation data, new energy power generation data and load data of the city are obtained through calculation, a new energy consumption calculation result of a transformer substation is obtained, the traditional energy power generation data of the city comprise predicted power generation, actual power generation, predicted power and actual power of transformer substation sites with time dimensions of day, month and year, the new energy power generation data of the city comprise predicted power generation, actual power generation, predicted power and actual power of the transformer substation with time dimensions of day, month and year, and the load data of the city comprise predicted power, actual load power, predicted load power and actual load power of the load with time dimensions of day, month and year.
And the new energy consumption calculation result of the city comprises the new energy utilization amount of the city, the new energy utilization rate, the new energy abandoned amount and the new energy abandoned rate of the city with the time dimensions of day, month and year.
Specifically, statistical summation can be performed on all district/county new energy consumption calculation results within the city range, and analysis and calculation with time dimensions of day, month and year are performed on the new energy consumption condition of the city through a regression analysis algorithm by combining city-level line data, such as city-level power planning, power industry development and the like, so as to obtain the new energy consumption calculation result of the city.
In order to facilitate the description of the new energy consumption calculation method provided by the embodiment of the invention, the invention also discloses a hierarchical new energy consumption calculation content schematic diagram based on the combination of macro and micro, and the content schematic diagram is shown in fig. 2.
In summary, the new energy consumption calculation method disclosed by the invention performs new energy consumption calculation in a manner of combining macroscopicity and microcosmic, starts with stations and loads with few influence factors and simple structures, obtains station new energy consumption calculation results, and then performs hierarchical statistics of new energy consumption on transformer bays, transformer substations, districts/counties and cities in sequence by taking days, months and years as time dimensions based on the station new energy consumption calculation results to finally obtain city new energy consumption calculation results. Compared with the traditional scheme, the whole consumption calculation process is based on the existing related data, and parameters do not need to be set subjectively, so that a more accurate new energy consumption calculation result can be obtained, and the rapid development and change of new energy can be adapted.
In order to further optimize the above embodiments, the invention discloses a station new energy consumption calculation method, which specifically comprises the following steps:
the acquisition process of the station new energy consumption calculation result comprises the following steps:
acquiring project related data, historical meteorological data, forecast meteorological data, historical generated energy, generated power and station-level line data of stations in a transformer area;
acquiring load related data, historical meteorological data, forecast meteorological data, and historical power consumption and power consumption of stations in a transformer area;
and based on a big data technology, processing the project related data, the historical meteorological data, the predicted meteorological data, the historical generated energy, the historical power consumption and the station-level line data by adopting a recurrent neural network algorithm to obtain a new energy consumption calculation 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 load data acquisition process comprises the following steps:
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 the load data.
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 new energy consumption calculation method disclosed by the invention performs new energy consumption calculation in a manner of combining macroscopicity and microcosmic, starts with stations and loads with few influence factors and simple structures, performs new energy consumption calculation by combining respective historical data and operating characteristics of the stations and the loads to ensure the accuracy of the consumption calculation, and then performs hierarchical statistics on new energy consumption conditions of transformer substations, districts/counties and cities in sequence by taking days, months and years as time dimensions based on station consumption calculation results and load data results to finally obtain new energy consumption calculation results of the cities. Compared with the traditional scheme, the whole new energy consumption calculation is based on the existing electric power related data, and parameters are not required to be set subjectively, so that a more accurate consumption calculation result can be obtained, and the method can adapt to the rapid development and change of new energy.
It should be noted that, in the embodiment of the present invention, the absorption calculation is calculated from the station, the cell, and from bottom to top based on the big data technology. The new energy consumption is the actual power generation of the new energy, and is also obtained by calculating and summing the actual power generation of the new energy on one layer of the station, the transformer area and the transformer substation; the new energy consumption (prediction) is obtained by analyzing factors such as historical actual power generation of new energy, power generation of traditional energy, historical power consumption and the like through a big data technology.
The new energy power abandon amount is the new energy prediction power generation amount-the new energy actual power generation amount;
and (4) predicting the electricity abandoning amount of the new energy, namely predicting the electricity generating amount, namely predicting the electricity consumption of the new energy.
Referring to fig. 3, in an embodiment of the present invention, there is further provided a new energy consumption computing apparatus, including:
the first calculating unit 10 is used for calculating to obtain a station new energy consumption calculation result based on the station traditional energy power generation data, the new energy power generation data and the load data;
a first obtaining unit 20, configured to obtain a station area new energy consumption calculation result based on station area line data and all station consumption calculation results within a station area range;
a second obtaining unit 30, configured to obtain a new energy consumption calculation result of the substation based on the substation-level line data and all new energy consumption calculation results of the transformer area within the range of the substation;
a third obtaining unit 40, configured to obtain new energy consumption calculation results of the county based on the county-level power line data and the new energy consumption calculation results of all the substations within the county range;
and the second calculating unit 50 is used for calculating and obtaining a new energy consumption calculating result of the local city based on the local city level line data and all the counties new energy consumption calculating results within the local city range.
Further, the first computing unit is specifically configured to:
acquiring project related data, historical meteorological data, forecast meteorological data, historical generated energy, generated power and station-level line data of stations in a transformer area;
acquiring load related data, historical meteorological data, forecast meteorological data, and historical power consumption and power consumption of stations in a transformer area;
and processing the project related data, the historical meteorological data, the forecast meteorological data, the historical power generation amount, the historical power consumption and the station-level line data to obtain the new energy consumption calculation result.
Further, the project-related data includes: the system includes at-ship project related data, at-construction project related data and planning project related data.
Further, the first obtaining unit is specifically configured to:
acquiring loads in a transformer area, wherein the loads comprise residential user data, enterprise user data and business user data;
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 load prediction result;
and determining a new energy consumption calculation result of the transformer area based on the user load prediction result.
Further, the method comprises, among others,
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, predicted power generation amount and historical power generation amount;
the enterprise user data includes: enterprise type, daily maximum load of an enterprise, daily minimum load of the enterprise, daily average load of the enterprise, predicted power generation amount and historical power generation amount;
the business user data includes: business type, business rated load, business day maximum load, business day minimum load and business day average load, predicted power generation, historical power generation.
Based on the foregoing embodiments, embodiments of the present application provide a computer-readable storage medium storing one or more programs, which are executable by one or more processors to implement the steps of the new energy consumption calculation method as any one of the above.
An embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the program to implement the steps of the new energy consumption calculation method.
The Processor or the CPU may be at least one of an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a Central Processing Unit (CPU), a controller, a microcontroller, and a microprocessor. It is understood that the electronic device implementing the above-mentioned processor function may be other electronic devices, and the embodiments of the present application are not particularly limited.
The computer storage medium/Memory may be a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read Only Memory (EPROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a magnetic Random Access Memory (FRAM), a Flash Memory (Flash Memory), a magnetic surface Memory, an optical Disc, or a Compact Disc Read-Only Memory (CD-ROM); but may also be various terminals such as mobile phones, computers, tablet devices, personal digital assistants, etc., that include one or any combination of the above-mentioned memories.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all functional units in the embodiments of the present application may be integrated into one processing module, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit. Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: various media capable of storing program codes, such as a removable Memory device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, and an optical disk.
The methods disclosed in the several method embodiments provided in the present application may be combined arbitrarily without conflict to obtain new method embodiments.
Features disclosed in several of the product embodiments provided in the present application may be combined in any combination to yield new product embodiments without conflict.
The features disclosed in the several method or apparatus embodiments provided in the present application may be combined arbitrarily, without conflict, to arrive at new method embodiments or apparatus embodiments.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
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 device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
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 new energy consumption calculation method is characterized by comprising the following steps:
calculating to obtain a station new energy consumption calculation result based on the station traditional energy power generation data, the new energy power generation data and the load data;
acquiring a new energy consumption calculation result of the transformer area based on transformer area level line data and all the station consumption calculation results within the transformer area range;
obtaining a new energy consumption calculation result of the transformer substation based on the transformer substation level line data and all the transformer area new energy consumption calculation results in the transformer substation range;
acquiring new energy consumption calculation results of the counties based on the county-level power line data and the new energy consumption calculation results of all the transformer substations within the county range;
and calculating to obtain a new energy consumption calculation result of the city based on the city-level line data and all the counties new energy consumption calculation results within the city range.
2. The method according to claim 1, wherein the calculating a station new energy consumption calculation result based on the station conventional energy generation data, the new energy generation data and the load data comprises:
acquiring project related data, historical meteorological data, forecast meteorological data, historical generated energy, generated power and station-level line data of stations in a transformer area;
acquiring load related data, historical meteorological data, forecast meteorological data, and historical power consumption and power consumption of stations in a transformer area;
and processing the project related data, the historical meteorological data, the forecast meteorological data, the historical power generation amount, the historical power consumption and the station-level line data to obtain the new energy consumption calculation result.
3. The method of claim 2, wherein the item-related data comprises: the system includes at-ship project related data, at-construction project related data and planning project related data.
4. The method of claim 1, wherein obtaining a station area new energy consumption calculation based on the station area level line data and all station consumption calculations within a station area comprises:
acquiring loads in a transformer area, wherein the loads comprise residential user data, enterprise user data and business user data;
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 load prediction result;
and determining a new energy consumption calculation result of the transformer area based on the user load prediction result.
5. The method of claim 4, wherein,
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, predicted power generation amount and historical power generation amount;
the enterprise user data includes: enterprise type, daily maximum load of an enterprise, daily minimum load of the enterprise, daily average load of the enterprise, predicted power generation amount and historical power generation amount;
the business user data includes: business type, business rated load, business day maximum load, business day minimum load and business day average load, predicted power generation, historical power generation.
6. A new energy consumption computing device, comprising:
the first calculation unit is used for calculating to obtain a station new energy consumption calculation result based on the station traditional energy power generation data, the new energy power generation data and the load data;
the first acquisition unit is used for acquiring a new energy consumption calculation result of the transformer area based on transformer area level line data and all station consumption calculation results in a transformer area range;
the second acquisition unit is used for acquiring a new energy consumption calculation result of the transformer substation based on the transformer substation level line data and all the transformer area new energy consumption calculation results within the transformer substation range;
the third obtaining unit is used for obtaining new energy consumption calculation results of the counties based on the county-level power line data and the new energy consumption calculation results of all the substations within the county range;
and the second calculating unit is used for calculating to obtain a new energy consumption calculating result of the city based on the city-level line data and all the counties and counties new energy consumption calculating results within the city range.
7. The apparatus according to claim 6, wherein the first computing unit is specifically configured to:
acquiring project related data, historical meteorological data, forecast meteorological data, historical generated energy, generated power and station-level line data of stations in a transformer area;
acquiring load related data, historical meteorological data, forecast meteorological data, and historical power consumption and power consumption of stations in a transformer area;
and processing the project related data, the historical meteorological data, the forecast meteorological data, the historical power generation amount, the historical power consumption and the station-level line data to obtain the new energy consumption calculation result.
8. The apparatus of claim 7, wherein the item-related data comprises: the system includes at-ship project related data, at-construction project related data and planning project related data.
9. The apparatus according to claim 6, wherein the first obtaining unit is specifically configured to:
acquiring loads in a transformer area, wherein the loads comprise residential user data, enterprise user data and business user data;
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 load prediction result;
and determining a new energy consumption calculation result of the transformer area based on the user load prediction result.
10. The apparatus of claim 9, wherein,
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, predicted power generation amount and historical power generation amount;
the enterprise user data includes: enterprise type, daily maximum load of an enterprise, daily minimum load of the enterprise, daily average load of the enterprise, predicted power generation amount and historical power generation amount;
the business user data includes: business type, business rated load, business day maximum load, business day minimum load and business day average load, predicted power generation, historical power generation.
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