CN111754264B - Data analysis method based on clean energy subsidy data - Google Patents
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- 238000007405 data analysis Methods 0.000 title claims abstract description 25
- 238000000034 method Methods 0.000 title claims abstract description 12
- 238000004458 analytical method Methods 0.000 claims abstract description 81
- 238000010248 power generation Methods 0.000 claims abstract description 55
- 238000012502 risk assessment Methods 0.000 claims abstract description 14
- 238000012549 training Methods 0.000 claims description 5
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S50/00—Market activities related to the operation of systems integrating technologies related to power network operation or related to communication or information technologies
- Y04S50/14—Marketing, i.e. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards
Abstract
The embodiment of the invention discloses a data analysis method based on clean energy patch data, which comprises the following steps: acquiring clean energy basic data; inputting the clean energy basic data into a clean energy patch prediction model to generate a clean energy patch prediction analysis result; inputting the clean energy subsidy prediction analysis result into the clean energy subsidy risk pricing model to generate a clean energy subsidy risk analysis result; acquiring a preset service requirement, and determining a data analysis result of clean energy subsidy data corresponding to the service requirement according to the service requirement, the clean energy subsidy prediction analysis result and the clean energy subsidy risk analysis result. According to the scheme, the data analysis result of the clean energy subsidy data can be rapidly determined, the power generation enterprises are helped to make more reasonable production and operation plans, decision support is provided for the financial institutions to develop financial product design and financing service based on the clean energy subsidy, and the financing cost of the power generation enterprises and the investment risk of the financial institutions are reduced.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to a data analysis method based on clean energy patch data.
Background
The existing clean energy financing patch has the problems of late patch scale and plan issuing, late money arrival time, long money arrival period and the like, and influences the normal operation of cash flow of power generation enterprises. In order to improve turnover rate, power generation enterprises need to financing based on clean energy subsidies, but the traditional data analysis method for the clean energy subsidies lacks enough basis, so that the innovation degree is insufficient, and the pertinence to the characteristics of the clean energy subsidies is not strong.
Disclosure of Invention
The embodiment of the invention provides a data analysis method based on clean energy patch data, which can rapidly determine the data analysis result of the clean energy patch data.
The embodiment of the invention provides a clean energy subsidy financing analysis method, which comprises the following steps:
acquiring clean energy basic data, wherein the clean energy basic data comprises clean energy subsidy data and power generation data;
inputting the clean energy basic data into a clean energy patch prediction model to generate a clean energy patch prediction analysis result;
inputting the clean energy subsidy prediction analysis result into a clean energy subsidy risk pricing model to generate a clean energy subsidy risk analysis result;
and acquiring a preset service requirement, and determining a data analysis result of clean energy subsidy data corresponding to the service requirement according to the service requirement, the clean energy subsidy prediction analysis result and the clean energy subsidy risk analysis result.
Further, the clean energy patch prediction model includes a patch information analysis model, a patch scale prediction model, a patch plan prediction model, and a clean energy power generation amount prediction model, and the step of inputting the clean energy basic data into the clean energy patch prediction model includes:
inputting the clean energy basic data into the patch information analysis model, the patch scale prediction model, the patch plan prediction model and the clean energy power generation amount prediction model respectively;
the subsidy information analysis model is used for determining characteristic information of the clean energy basic data;
the patch scale prediction model is used for predicting the clean energy patch scale;
the patch plan prediction model is used for predicting a clean energy patch plan;
the clean energy power generation amount prediction model is used for predicting the power generation amount of the clean energy power generation enterprise.
Further, the clean energy subsidy risk pricing model includes a risk pricing model, a yield analysis model and an investment period analysis model, and inputting the clean energy subsidy prediction analysis result into the clean energy subsidy risk pricing model includes:
inputting the clean energy subsidy prediction analysis result into the risk pricing model, the yield analysis model and the investment deadline analysis model respectively;
the risk pricing model is used for determining credit limits of clean energy enterprises;
the profitability analysis model is used for determining the profitability of the investment clean energy;
the investment deadline analysis model is used for determining the investment deadline of clean energy.
Further, when the clean energy base data is clean energy base data of a non-grid business system, the acquiring the clean energy base data includes:
carrying out structuring treatment on clean energy source original data of the non-power grid business system to generate structured clean energy source original data;
and preprocessing the structured clean energy original data. Generating the clean energy base data.
By adopting the technical means, the data analysis result of the clean energy subsidy data can be rapidly determined, the power generation enterprises are helped to make more reasonable production and operation plans, decision support is provided for the financial institutions to develop financial product design and financing service based on the clean energy subsidy, and the financing cost of the power generation enterprises and the investment risk of the financial institutions are reduced.
Drawings
Fig. 1 is a flow chart of a data analysis method based on clean energy patch data according to an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Fig. 1 is a flow chart of a data analysis method based on clean energy patch data according to an embodiment of the present invention, as shown in fig. 1, the method includes:
step 101, acquiring clean energy basic data, wherein the clean energy basic data comprises clean energy patch data and power generation data.
Illustratively, clean energy base data for a preset period of time, or clean energy base data for a preset length of time, is obtained. The clean energy basic data comprise subsidy and power generation data of clean energy power generation enterprises. The clean energy subsidy data may be understood as data for performing financial subsidy based on the amount of power generation of the clean energy, for example, clean energy financial subsidy data may be acquired from a financial management system. The clean energy patch data can comprise related data such as patch amount, patch plan-to-patch delivery and the like, such as patch plan time, patch delivery time, actual patch delivery amount, patch electricity price and the like. The clean energy power generation amount data may include data related to power generation, equipment operation, and the like, such as power generation plans, power generation amount, online power amount, equipment planned operation hours, and the like, wherein the clean energy power generation data may be acquired from the dispatch system.
And 102, inputting the clean energy basic data into a clean energy patch prediction model to generate a clean energy patch prediction analysis result.
The clean energy patch prediction model can be understood as being capable of rapidly predicting the clean energy patch related information within a preset time period in the future after the clean energy basic data is input. The clean energy subsidy prediction model can be constructed in a mode that the clean energy power generation enterprise subsidy data and the generated energy data are used as training samples to train a preset time sequence and a neural network algorithm.
In the embodiment of the invention, the clean energy patch data and the clean energy power generation amount data obtained in the step 101 are input into the clean energy patch prediction model, and the clean energy patch prediction model analyzes the clean energy patch data and the clean energy power generation amount data to generate a clean energy patch prediction analysis result. The clean energy patch prediction analysis result can include information such as clean energy patch scale, patch-to-account period, and power generation amount of the clean energy in a preset time period in the future.
And step 103, inputting the clean energy subsidy prediction analysis result into a clean energy subsidy risk pricing model to generate a clean energy subsidy risk analysis result.
The clean energy subsidy risk pricing model can be understood as being capable of rapidly predicting relevant information such as clean energy subsidy risk after the clean energy subsidy prediction analysis result is input. The clean energy patch risk pricing model can be constructed by taking the clean energy patch data, the generated energy data and the historical output result of the clean energy patch prediction model as training samples and combining an improved exponential algorithm.
In the embodiment of the invention, the clean energy patch prediction analysis result obtained in the step 102 is input into the clean energy patch risk pricing model, and the clean energy patch risk pricing model analyzes the clean energy patch prediction analysis result to generate the clean energy patch risk analysis result. The clean energy subsidy risk analysis result can include related information such as clean energy pricing, yield, investment deadline, investment risk and the like.
Optionally, the online electric quantity data, the historical clean energy subsidy data and the clean energy subsidy predictive analysis result of the power generation enterprise can be input into the clean energy subsidy risk pricing model at the same time, and the clean energy subsidy risk analysis result is obtained.
Step 104, acquiring a preset service requirement, and determining a data analysis result of clean energy subsidy data corresponding to the service requirement according to the service requirement, the clean energy subsidy prediction analysis result and the clean energy subsidy risk analysis result.
The method comprises the steps of obtaining service requirements input by service participants according to different service requirements of different service participants, comprehensively analyzing the service requirements, the clean energy subsidy prediction analysis results and the clean energy subsidy risk analysis results, and obtaining data analysis results of clean energy subsidy data matched with the service requirements. The data analysis result of the clean energy patch data can include relevant information such as financing years, financing amount and financing risk. The data analysis result of the obtained clean energy subsidy data can help the power generation enterprises to make more reasonable production and operation plans, and can also provide decision support for the financial institutions to develop financial product design and financing services based on the clean energy subsidy. The advantage of this arrangement is that the data analysis result of the clean energy patch data matched with the business requirements can be output according to different business scenarios.
According to the data analysis scheme based on the clean energy subsidy data, the clean energy basic data are obtained, wherein the clean energy basic data comprise clean energy subsidy and power generation data; inputting the clean energy basic data into a clean energy patch prediction model to generate a clean energy patch prediction analysis result; inputting the clean energy subsidy prediction analysis result into the clean energy subsidy risk pricing model to generate a clean energy subsidy risk analysis result; acquiring a preset service requirement, and determining a data analysis result of clean energy subsidy data corresponding to the service requirement according to the service requirement, the clean energy subsidy prediction analysis result and the clean energy subsidy risk analysis result. By adopting the technical means, the large data analysis processing technology is utilized, various data such as electricity consumption, power generation, subsidy planning, subsidy distribution conditions and the like of the clean energy subsidy analysis business participants are integrated, the clean energy subsidy analysis results are output in combination with different business scenes, the power generation enterprises are helped to make more reasonable production and operation plans, decision support is provided for the financial institutions to develop financial product design and financing service based on clean energy subsidy, and the financing cost of the power generation enterprises and the investment risk of the financial institutions are reduced.
In another embodiment of the present invention, the clean energy patch prediction model includes a patch information analysis model, a patch scale prediction model, a patch plan prediction model, and a clean energy power generation amount prediction model; the step of inputting the clean energy basic data into a clean energy patch prediction model comprises the following steps: inputting the clean energy basic data into the patch information analysis model, the patch scale prediction model, the patch plan prediction model and the clean energy power generation amount prediction model respectively; the subsidy information analysis model is used for determining characteristic information of the clean energy basic data; the patch scale prediction model is used for predicting the clean energy patch scale; the patch plan prediction model is used for predicting a clean energy patch plan; the clean energy power generation amount prediction model is used for predicting the power generation amount of the newly-added clean energy.
The clean energy patch prediction model comprises a patch information analysis model, a patch scale prediction model, a patch plan prediction model and a clean energy power generation amount prediction model, and clean energy base data comprising clean energy patch data and clean energy power generation amount data are respectively input into the four models of the patch information analysis model, the patch scale prediction model, the patch plan prediction model and the clean energy power generation amount prediction model. And the patch information analysis model analyzes the clean energy patch data and the clean energy generating capacity data to determine the characteristic information of the clean energy. For example, trend fitting on patch scale can be realized based on lambda algorithm, variation trend is displayed, and patch scale and distribution characteristics can be displayed through a scatter diagram. The patch scale prediction model analyzes the clean energy patch data and the clean energy power generation amount data to predict the clean energy patch scale. The patch plan prediction model analyzes the clean energy patch data and the clean energy power generation amount data, and predicts the clean energy patch plan within a preset time period (such as 6 months or 12 months in the future). The clean energy generating capacity prediction model analyzes the clean energy patch data and the clean energy generating capacity data, and predicts the generating capacity of the newly added clean energy in a preset time period in the future. Optionally, when the prediction model (patch scale prediction model, patch plan prediction model and clean energy power generation amount prediction model) analyzes the clean energy patch data and the clean energy power generation amount data, the characteristic information of the clean energy basic data determined by the patch information analysis model may be referred to.
In another embodiment of the present invention, the clean energy subsidy risk pricing model includes a risk pricing model, a yield analysis model, and an investment deadline analysis model, and inputting the clean energy subsidy prediction analysis result into the pre-trained clean energy subsidy risk pricing model includes: inputting the clean energy subsidy prediction analysis result into the risk pricing model, the yield analysis model and the investment deadline analysis model respectively; the risk pricing model is used for determining credit limits of clean energy enterprises; the profitability analysis model is used for determining the profitability of the investment clean energy; the investment deadline analysis model is used for determining the investment deadline of clean energy.
The clean energy subsidy risk pricing model comprises a risk pricing model, a yield analysis model and an investment period analysis model, and the clean energy subsidy prediction analysis results are respectively input into the risk pricing model, the yield analysis model and the investment period analysis model. Optionally, the online electric quantity data, the clean energy subsidy data and the clean energy subsidy prediction analysis result of the power generation enterprise can be input into the risk pricing model, the yield analysis model and the investment period analysis model simultaneously. The risk pricing model comprehensively analyzes the online electric quantity data, the clean energy subsidy data and the clean energy subsidy prediction analysis result of the power generation enterprise, and determines the price of the clean energy, namely, the risk asset of the clean energy is priced, so that the advice service for priced clean energy is provided for the financial institutions. The yield analysis model comprehensively analyzes the online electric quantity data, the clean energy subsidy data and the clean energy subsidy prediction analysis result of the power generation enterprise, and determines the yield of investment clean energy so as to provide the financial institution with the advice service of the financial product yield of clean energy. The investment period analysis model comprehensively analyzes the online electric quantity data, the clean energy subsidy data and the clean energy subsidy prediction analysis result of the power generation enterprise to determine the investment period of the clean energy so as to provide the financial institution with the advice service of the clean energy, namely the financial product investment period.
Optionally, related information such as the subsidy information analysis result, the predicted clean energy subsidy scale, the predicted clean energy subsidy plan, the predicted generated energy of the newly added clean energy, the price of the investment clean energy, the yield of the investment clean energy, the investment period of the clean energy and the like can be obtained in an interface mode, comprehensive analysis is performed on the related information according to the service requirement, and the clean energy subsidy financing analysis result matched with the service requirement is prepared.
In another embodiment of the present invention, when the clean energy base data is grid external clean energy base data, the acquiring clean energy base data includes: carrying out structuring treatment on clean energy source original data of the non-power grid business system to generate structured clean energy source original data; and preprocessing the structured clean energy original data to generate clean energy basic data.
Illustratively, the clean energy base data includes clean energy base data for an internal grid business system and external clean energy base data for a non-grid business system. The clean energy basic data in the power grid can be understood as clean energy subsidy data and clean energy generating capacity data acquired from a national network internal service system, and the clean energy basic data in the power grid can be understood as clean energy basic data acquired from a non-national network service system. The clean energy basic data outside the power grid can be obtained by data integration with a service system inside the national network, the clean energy basic data outside the power grid can be obtained, the clean energy raw data is structured, unstructured data (such as pictures) are transferred into structured data, and then the structured data are further cleaned and subjected to comparable pretreatment. The comparison operation is to compare the structured data with the data requirement, judge whether the structured data is the required data, and filter out the non-required data.
Optionally, the clean energy subsidy financing analysis scene can be verified and perfected, for example, test point units are selected for application, and the accuracy of service analysis results is further verified. And carrying out iterative perfection on the clean energy subsidy financing analysis service according to the actual feedback of the user.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (2)
1. A data analysis method based on clean energy subsidy data, comprising:
step 1, acquiring clean energy basic data;
the clean energy basic data comprise clean energy subsidy data and clean energy power generation data, the clean energy power generation data are obtained from a dispatching system and comprise a power generation plan, power generation capacity, on-line power and equipment plan operation hours, and the clean energy subsidy data are data for performing financial subsidy based on the power generation capacity of the clean energy and comprise subsidy amount, subsidy planning time, subsidy release time, actual subsidy release amount and subsidy electricity price;
step 2, inputting the clean energy basic data into a clean energy patch prediction model to generate a clean energy patch prediction analysis result;
the clean energy patch prediction model is constructed in a mode of training a preset time sequence and a neural network algorithm by taking the clean energy patch data and the clean energy power generation data as training samples, and after the clean energy basic data is input, relevant information of the clean energy patch in a future preset time period can be rapidly predicted; the clean energy patch prediction model comprises a patch information analysis model, a patch scale prediction model, a patch plan prediction model and a clean energy power generation amount prediction model;
the step of inputting the clean energy basic data into a clean energy patch prediction model comprises the following steps:
inputting the clean energy basic data into the patch information analysis model, the patch scale prediction model, the patch plan prediction model and the clean energy power generation amount prediction model respectively;
the subsidy information analysis model is used for determining characteristic information of the clean energy basic data;
the patch scale prediction model is used for predicting the patch scale of clean energy;
the patch plan prediction model is used for predicting a clean energy patch plan;
the clean energy generating capacity prediction model is used for predicting the generating capacity of the newly-added clean energy;
step 3, inputting the clean energy subsidy prediction analysis result into a clean energy subsidy risk pricing model to generate a clean energy subsidy risk analysis result;
the clean energy subsidy risk pricing model takes the historical output results of the clean energy subsidy data, the clean energy power generation data and the clean energy subsidy prediction model as training samples; the clean energy subsidy risk pricing model comprises a risk pricing model, a yield analysis model and an investment deadline analysis model;
the step of inputting the clean energy patch prediction analysis result into the clean energy patch risk pricing model comprises the following steps:
inputting the clean energy subsidy prediction analysis result into the risk pricing model, the yield analysis model and the investment deadline analysis model respectively;
the risk pricing model is used for determining the price of investment clean energy, namely pricing the risk asset of the clean energy;
the profitability analysis model is used for determining the profitability of the investment clean energy;
the investment deadline analysis model is used for determining the investment deadline of clean energy;
and 4, acquiring a preset service requirement, and determining a data analysis result of clean energy subsidy data corresponding to the service requirement according to the service requirement, the clean energy subsidy prediction analysis result and the clean energy subsidy risk analysis result, wherein the data analysis result of the clean energy subsidy data comprises a financing period, a financing amount and a financing risk.
2. The method of claim 1, wherein when the clean energy base data is clean energy base data of a non-grid business system, the acquiring clean energy base data is preceded by:
carrying out structuring treatment on clean energy source original data of the non-power grid business system to generate structured clean energy source original data;
preprocessing the structured clean energy raw data to generate the clean energy basic data.
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