CN117273789A - Electric power marketing data acquisition and analysis method - Google Patents
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Abstract
The invention discloses a power marketing data acquisition and analysis method, and relates to the technical field of power marketing data analysis. The electric power marketing data acquisition and analysis method comprises the steps of obtaining electric power marketing data, classifying and storing the electric power marketing data in a preset storage structure according to set attributes, obtaining electric power marketing data stored in the storage structure, analyzing electric power marketing data architecture information in a rich period, a flat period and a dry period, wherein the electric power marketing data architecture information comprises a total amount of achievements, a new energy occupation ratio and a traditional electric power occupation ratio, calculating electric power price fluctuation rates in the rich period, the flat period and the dry period, providing profound market insight for electric power companies, helping to know price fluctuation degree and operation cost risk, helping companies to predict market fluctuation better and reduce potential loss, supporting optimizing operation strategies, improving risk management, improving market competitiveness, enabling the electric power marketing data architecture information to solve technical problems more pertinently, and improving service performance.
Description
Technical Field
The invention relates to the technical field of electric power marketing data analysis, in particular to an electric power marketing data acquisition and analysis method.
Background
Currently, the trading mode is gradually updated, and various flexible and autonomous trading modes such as power grid, internet, credit, futures, retail and wholesale are generated, so that the pressure of the power enterprise in the aspects of developing the electricity selling market, preventing management risks and the like is increased, and meanwhile, the serious challenges of high-quality customer reduction, market share reduction and high-quality talent loss are faced, and particularly, higher requirements and new challenges are provided for marketing service business. Therefore, it becomes particularly important how to reasonably and efficiently utilize the power marketing data.
At present, the electric power marketing data increasingly presents the characteristics of large quantity, high updating speed and diversified forms. The characteristics of the big electric power data are mainly as follows: the large data Volume (Volume), which is an important characteristic of large power data, is greatly beyond the expectations of power enterprises along with the continuous advancement of power informatization construction; multiple types (Variety) means that the power big data contains a wide Variety of data types, such as structured, semi-structured, and unstructured data; fast (speed) refers to the speed of the collection process and process analysis of the power big data. Value (Value) means that the power data contains a lot of valuable information.
The Chinese patent publication No. CN115905364A discloses a system and a method for analyzing electric marketing data, comprising: the information management module is used for managing and storing the power utilization information data of the power users; the data acquisition module is used for acquiring electric power marketing business data; the data filtering module is used for filtering the electric power marketing business data; the database module is used for associating the filtered power marketing business data with the power utilization information data of the power user and storing the power marketing business data; the data screening module is used for screening the data stored in the database module; the data analysis module is used for analyzing the data screened by the data screening module and obtaining an analysis result; the visualization module is used for visually displaying the analysis result; the data acquisition module, the data filtering module, the database module, the data screening module, the data analysis module and the visualization module are sequentially connected, and the information management module is connected with the database module, so that the accuracy and the effectiveness of data analysis can be improved, and the utilization efficiency of analysis results can be improved.
However, the existing method for collecting and analyzing the electric power marketing data has the problem that the electric power price fluctuation rate in the water-rich period, the water-flat period and the water-free period is inconvenient to calculate, so that an electric power company can know the price fluctuation degree.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a power marketing data acquisition and analysis method, which solves the problem that the price fluctuation rate of the power in the high water period, the flat water period and the dead water period is inconvenient to calculate, so that the power company knows the price fluctuation degree.
In order to achieve the above purpose, the invention is realized by the following technical scheme: the electric power marketing data acquisition and analysis method comprises the following steps of: acquiring electric power marketing data, and storing the electric power marketing data in a preset storage structure in a classified manner according to set attributes, wherein the set attributes comprise a high water period, a flat water period and a dead water period; traversing the electric power marketing data stored in the storage structure and filling the electric power marketing data into a table; acquiring electric power marketing data stored in a storage structure, and analyzing electric power marketing data architecture information of a rich period, a flat period and a dead period, wherein the electric power marketing data architecture information comprises a total amount of a transaction, a new energy duty and a traditional electric power duty; respectively predicting the power generation increase rate in the water-rich period, the power generation increase rate in the water-flat period and the power generation increase rate in the water-free period based on the power marketing data architecture information; calculating the price fluctuation rate of the price in the high water period, the price fluctuation rate of the price in the low water period and the price fluctuation rate of the price in the low water period based on the price in the electric power sales in the high water period, the low water period and the price in the low water period respectively; cost losses are estimated based on the power marketing data architecture information and the power sales price.
Further, the process of classifying and storing the power marketing data in a preset storage structure according to the set attribute is as follows: acquiring power marketing data, and preprocessing the power marketing data, wherein the preprocessing comprises deleting blank values and abnormal values; classifying according to the time stamp in the electric power marketing data, and storing the classified electric power marketing data into the corresponding area in the storage structure.
Further, after storing the classified power marketing data in the corresponding area in the storage structure, performing data verification, wherein the verification process is as follows: the connection storage structure is used for inquiring electric power marketing data; comparing the query result with the preprocessed power marketing data, including data quantity comparison and data value comparison; recording the verification result.
Further, the process of traversing the power marketing data stored in the storage structure and filling the power marketing data into the table is as follows: creating an empty table, adding columns of the table, and naming according to the attribute; traversing the data in the water-filling period in the storage structure, and filling each piece of data into the water-filling period column of the table; traversing the horizontal period data in the storage structure, and filling each piece of data into the horizontal period column of the table; traversing the dead water period data in the storage structure, and filling each piece of data into the dead water period column of the table; the table is saved.
Further, the total amount of the achievement includes a total annual achievement amount and a total monthly achievement amount, the new energy duty includes a new energy annual duty and a new energy monthly duty, and the conventional power duty includes a conventional power annual duty and a conventional power monthly duty.
Further, the process of analyzing the electric power marketing data structure information of the high water period, the flat water period and the dead water period is as follows: acquiring annual and monthly total volume of the deals; acquiring single-day traditional power trading volume and single-day new energy power trading volume in single-day trading volume, and respectively calculating annual traditional power trading volume and monthly traditional power trading volume based on the single-day traditional power trading volume; calculating new annual energy power trading volume and new monthly energy power trading volume based on the new daily energy power trading volume respectively; calculating a traditional power annual duty and a traditional power monthly duty respectively based on the annual traditional power trading volume and the monthly traditional power trading volume; and calculating new energy annual duty and new energy monthly duty respectively based on the annual new energy power transaction amount and the monthly new energy power transaction amount.
Further, the process of respectively predicting the power generation increase rate in the water-rich period, the power generation increase rate in the flat period and the power generation increase rate in the dead period based on the power marketing data architecture information is as follows: acquiring the daily yield of electric power marketing data in a high water period and the daily yield of electric power marketing data in a low water period; calculating the high-water-period power generation quantity, the flat-water-period power generation quantity and the dead-water-period power generation quantity of each year respectively based on the high-water-period single-day power generation quantity, the flat-water-period single-day power generation quantity and the dead-water-period single-day power generation quantity; the high-water-period power generation increase rate, the flat-water-period power generation increase rate and the dead-water-period power generation increase rate are calculated based on the high-water-period power generation amount, the flat-water-period power generation amount and the dead-water-period power generation amount of each year respectively, and the calculation formula is as follows:wherein P is (f,p,k) For the power generation increase rate in the water-rich period or the power generation increase rate in the water-flat period or the power generation increase rate in the dead period, +.>For the i-th year's high-water-period or flat-water-period or dead-water-period traffic, the term +.>The i+1th year's high-water-period or flat-water-period or dead-water-period traffic is calculated as the growth rate calculation factor, and e is a natural constant.
Further, the processes of calculating the high-water-period price fluctuation rate, the low-water-period price fluctuation rate and the low-water-period price fluctuation rate based on the high-water-period, the low-water-period and the low-water-period electric power sales price respectively are as follows: acquiring a high-water-period single-day price in the electric power marketing data, and acquiring a flat-water-period single-day price in the electric power marketing data; calculating the average price of the power selling in the high water period, the average price of the power selling in the low water period and the average price of the power selling in the low water period respectively based on the single-day price of the high water period, the single-day price of the single-day period and the price of the single-day period; the method comprises the following steps of respectively calculating the fluctuation rate of the high-water-period single-day price, the fluctuation rate of the low-water-period single-day price and the fluctuation rate of the low-water-period single-day price based on the high-water-period single-day price, the low-water-period single-day price, the average price of the high-water-period power sales price and the average price of the low-water-period power sales price, wherein the calculation formulas are as follows:wherein bo (f,p,k) I=1, 2,3 for the high-water-period price fluctuation rate or the flat-water-period price fluctuation rate or the dead-water-period price fluctuation rate,..m is the number of the high-water-period price for electric power selling or the flat-water-period price for electric power selling or the dead-water-period price for electric power selling, and @, respectively>For the single-day price in the high water period or the single-day price in the flat water period or the single-day price in the dead water period, gamma is a fluctuation modulation factor, and delta JC is provided (f,p,k) Is the maximum price difference in the water-rich period or the maximum price difference in the water-flat period or the maximum price difference in the water-free period.
Further, the calculation process of the maximum price difference in the water-rich period or the maximum price difference in the water-flat period or the maximum price difference in the water-free period is as follows: after the high-water-period single-day price, the flat-water-period single-day price and the dead-water-period single-day price of the electric power marketing data are obtained, the high-water-period highest single-day price, the high-water-period lowest single-day price, the flat-water-period highest single-day price, the dead-water-period highest single-day price and the dead-water-period lowest single-day price are respectively obtained, and the high-water-period maximum price difference, the flat-water-period maximum price difference and the dead-water-period maximum price difference are calculated.
Further, the calculation formula for estimating the cost loss based on the power marketing data architecture information and the power sales price is as follows:wherein CS is (f,p,k) I=1, 2,3, for the loss of water-full period cost or water-flat period cost or water-dead period cost, b is the total amount of monthly completions in water-full period or water-flat period or water-dead period, YD i For the total amount of the i-th monthly transaction in the full-water period or the flat-water period or the dead-water period, i=1, 2,3,.. D is the number of the single-day transaction in the full-water period or the single-day transaction in the flat-water period or the single-day transaction in the dead-water period, CT i For the ith month of traditional electric power transaction amount in the full or flat or dead water period, delta ct For the maintenance cost of traditional power per degree, XN i The new energy power transaction amount delta is the ith month in the full-water period, the flat-water period or the dead-water period xn And maintaining the cost for each degree of new energy power.
The invention has the following beneficial effects:
(1) According to the electric power marketing data acquisition and analysis method, the electric power price fluctuation rate in the high water period, the flat water period and the dead water period is calculated, so that deep market insight is provided for electric power companies, the price fluctuation degree and the operation cost risk are helped to be known, the companies are helped to predict market fluctuation better and reduce potential loss, the operation strategy is supported to be optimized, the risk management is improved, the market competitiveness is improved, the technical problem can be solved more specifically, and the service performance is improved.
(2) According to the electric power marketing data acquisition and analysis method, through data-driven decision based on historical data, an electric power company can evaluate market conditions more accurately, adopts an adaptive strategy, reduces uncertainty, meets customer requirements better, is beneficial to reducing economic risks, and supports the company to make intelligent decisions, so that the electric power marketing data acquisition and analysis method can be operated better and succeeded in market fluctuation environments in different water periods.
Of course, it is not necessary for any one product to practice the invention to achieve all of the advantages set forth above at the same time.
Drawings
Fig. 1 is a flowchart of a method for collecting and analyzing electric power marketing data according to the present invention.
Fig. 2 is a flow chart of the method for collecting and analyzing electric power marketing data according to the present invention.
Fig. 3 is a flowchart of the method for collecting and analyzing electric power marketing data according to the present invention for analyzing electric power marketing data structure information in the high water period, the flat water period and the dead water period.
Fig. 4 is a flowchart of the method for collecting and analyzing electric power marketing data, which predicts the growth rate of electric power generation in the water-rich period, the growth rate of electric power generation in the water-flat period and the growth rate of electric power generation in the water-free period.
Fig. 5 is a flowchart of the method for collecting and analyzing electric power marketing data, which is used for calculating the high-water-period price-making fluctuation rate, the low-water-period price-making fluctuation rate and the low-water-period price-making fluctuation rate.
Detailed Description
According to the embodiment of the application, through the electric power marketing data acquisition and analysis method, the degree that the electric power price exchange fluctuation rate in the water-rich period, the water-flat period and the water-free period is calculated to be beneficial to the electric power company to know price fluctuation is achieved, the electric power company is helped to better know operation cost, and accordingly cost is reduced.
The problems in the embodiments of the present application are as follows:
first, the power marketing data is collected, including the price of the transaction, the amount of the transaction and other relevant information, the collected power marketing data is classified according to preset attributes, including the water-rich period, the water-flat period and the water-free period, and the data is stored in a preset data structure for subsequent analysis and processing.
The power marketing data within the storage structure is traversed and the data is populated into the table.
The analysis of the price fluctuation rate is carried out on the electric power sales data in the high water period, the flat water period and the dead water period, and the price fluctuation rate can be realized by calculating the price standard deviation or other statistical indexes of each water period, and reflects the fluctuation degree of the price in different water periods, so that the electric power company is helped to know the instability of the market price.
Based on the power marketing data architecture information and the power marketing price fluctuation rate, the operation cost loss in different water periods is estimated.
Referring to fig. 1, the embodiment of the invention provides a technical scheme: the electric power marketing data acquisition and analysis method comprises the following steps of: acquiring electric power marketing data, and storing the electric power marketing data in a preset storage structure according to set attributes, wherein the set attributes comprise a high water period, a flat water period and a dead water period; traversing the electric power marketing data stored in the storage structure and filling the electric power marketing data into a table; acquiring electric power marketing data stored in a storage structure, and analyzing electric power marketing data architecture information of a water-rich period, a water-flat period and a water-free period, wherein the electric power marketing data architecture information comprises a total amount of the complete traffic, a new energy duty cycle and a traditional electric power duty cycle, the total amount of the complete traffic comprises a annual total amount of the complete traffic and a monthly total amount of the complete traffic, the new energy duty cycle comprises a new energy annual duty cycle and a new energy monthly duty cycle, and the traditional electric power duty cycle comprises a traditional electric power annual duty cycle and a traditional electric power monthly duty cycle; respectively predicting the power generation increase rate in the water-rich period, the power generation increase rate in the water-flat period and the power generation increase rate in the water-free period based on the power marketing data architecture information; calculating the price fluctuation rate of the price in the high water period, the price fluctuation rate of the price in the low water period and the price fluctuation rate of the price in the low water period based on the price in the electric power sales in the high water period, the low water period and the price in the low water period respectively; cost losses are estimated based on the power marketing data architecture information and the power sales price.
Specifically, the process of classifying and storing the power marketing data in a preset storage structure according to the set attribute is as follows: acquiring power marketing data, and preprocessing the power marketing data, wherein the preprocessing comprises deleting blank values and abnormal values; classifying according to the time stamp in the electric power marketing data, and storing the classified electric power marketing data into the corresponding area in the storage structure.
After the classified power marketing data are stored in the corresponding areas in the storage structure, data verification is carried out, and the verification process is as follows: the connection storage structure is used for inquiring electric power marketing data; comparing the query result with the preprocessed power marketing data, including data quantity comparison and data value comparison; recording the verification result.
In this embodiment, the preprocessing step includes the deletion of blank and outliers, which helps ensure the quality of the stored data, and the cleansing and sorting of the data is an important step to ensure that subsequent analysis and decision making is based on accurate data, helping to prevent errors or deviations from occurring.
The data can be divided into different time periods or water periods according to the time stamps in the electric power marketing data, so that the data is easier to manage and analyze, the preset storage structure is favorable for organizing and storing the data, the data can be searched and used as required, the data verification step is favorable for ensuring that the data stored in the storage structure is consistent with the original data, whether the data is lost or not can be detected by comparing the data quantity and the data value, and inaccurate decisions caused by data damage or errors can be avoided.
Specifically, as shown in fig. 2, the process of traversing the power marketing data stored in the storage structure and filling the power marketing data into the table is as follows: creating an empty table, adding columns of the table, and naming according to the attribute; traversing the data in the water-filling period in the storage structure, and filling each piece of data into the water-filling period column of the table; traversing the horizontal period data in the storage structure, and filling each piece of data into the horizontal period column of the table; traversing the dead water period data in the storage structure, and filling each piece of data into the dead water period column of the table; the table is saved.
In this embodiment, creating a table and populating the corresponding columns with data of different water ages facilitates the integration of the scattered data into a structured data table, making the data easier to view, analyze and understand, and after populating the table with data, various data analysis tools and visualization techniques can be used to better present and display the data.
Specifically, as shown in fig. 3, the process of analyzing the electric power marketing data structure information of the high water period, the flat water period and the dead water period is as follows: acquiring annual and monthly total volume of the deals; acquiring single-day traditional power trading volume and single-day new energy power trading volume in single-day trading volume, and respectively calculating annual traditional power trading volume and monthly traditional power trading volume based on the single-day traditional power trading volume; calculating new annual energy power trading volume and new monthly energy power trading volume based on the new daily energy power trading volume respectively; calculating a traditional power annual duty and a traditional power monthly duty respectively based on the annual traditional power trading volume and the monthly traditional power trading volume; and calculating new energy annual duty and new energy monthly duty respectively based on the annual new energy power transaction amount and the monthly new energy power transaction amount.
In this embodiment, the total annual and monthly traffic and the traffic of different types of electricity (traditional electricity and new energy electricity) are obtained, an overall overview of the electricity sales is provided, the electric company is helped to better understand the market scale of different time periods, the proportion of traditional electricity and new energy electricity in the total electricity sales can be understood by calculating their annual and monthly proportion, and the electric company is helped to understand the use condition of clean energy and whether the renewable energy goal is met.
Analyzing the annual and monthly total amount of exchanges can identify the difference between the power sales in different seasons and months, and is important for developing seasonal market strategies and supply plans.
Specifically, as shown in fig. 4, the processes of predicting the power generation increase rate in the rich period, the power generation increase rate in the flat period, and the power generation increase rate in the dry period based on the power marketing data structure information are as follows: obtaining electricityThe daily amount of traffic in the high water period and the daily amount of traffic in the flat water period of the power marketing data; calculating the high-water-period power generation quantity, the flat-water-period power generation quantity and the dead-water-period power generation quantity of each year respectively based on the high-water-period single-day power generation quantity, the flat-water-period single-day power generation quantity and the dead-water-period single-day power generation quantity; the high-water-period power generation increase rate, the flat-water-period power generation increase rate and the dead-water-period power generation increase rate are calculated based on the high-water-period power generation amount, the flat-water-period power generation amount and the dead-water-period power generation amount of each year respectively, and the calculation formula is as follows:wherein P is (f,p,k) For the power generation increase rate in the water-rich period or the power generation increase rate in the water-flat period or the power generation increase rate in the dead period, +.>For the i-th year's high-water-period or flat-water-period or dead-water-period traffic, the term +.>The i+1th year's high-water-period or flat-water-period or dead-water-period traffic is calculated as the growth rate calculation factor, and e is a natural constant.
In this embodiment, by calculating the power generation increase rates of different water periods, the future market trend can be predicted, and the calculation of the power generation increase rate can quantify the increase of the power market, so that it is easier to understand and compare, and helps the power company to identify which water period has more remarkable generation increase, so that corresponding action is taken.
By calculating the growth rate based on historical data, a decision basis for data support can be provided, and the power generation growth rate is allowed to be calculated according to different water periods (a full water period, a flat water period and a dead water period) respectively, so that analysis is more flexible and targeted.
Specifically, as shown in fig. 5, the price waves of the high-water period are calculated based on the price of the electric power sales in the high-water period, the flat-water period and the dead-water period, respectivelyThe process of the fluctuation rate, the flat water period price-making fluctuation rate and the dead water period price-making fluctuation rate is as follows: acquiring a high-water-period single-day price in the electric power marketing data, and acquiring a flat-water-period single-day price in the electric power marketing data; calculating the average price of the power selling in the high water period, the average price of the power selling in the low water period and the average price of the power selling in the low water period respectively based on the single-day price of the high water period, the single-day price of the single-day period and the price of the single-day period; the method comprises the following steps of respectively calculating the fluctuation rate of the high-water-period single-day price, the fluctuation rate of the low-water-period single-day price and the fluctuation rate of the low-water-period single-day price based on the high-water-period single-day price, the low-water-period single-day price, the average price of the high-water-period power sales price and the average price of the low-water-period power sales price, wherein the calculation formulas are as follows:wherein bo (f,p,k) I=1, 2,3 for the high-water-period price fluctuation rate or the flat-water-period price fluctuation rate or the dead-water-period price fluctuation rate,..m is the number of the high-water-period price for electric power selling or the flat-water-period price for electric power selling or the dead-water-period price for electric power selling, and @, respectively>For the single-day price in the high water period or the single-day price in the flat water period or the single-day price in the dead water period, gamma is a fluctuation modulation factor, and delta JC is provided (f,p,k) Is the maximum price difference in the water-rich period or the maximum price difference in the water-flat period or the maximum price difference in the water-free period.
The calculation process of the maximum price difference in the high water period or the maximum price difference in the flat water period or the maximum price difference in the dead water period comprises the following steps: after the high-water-period single-day price, the flat-water-period single-day price and the dead-water-period single-day price of the electric power marketing data are obtained, the high-water-period highest single-day price, the high-water-period lowest single-day price, the flat-water-period highest single-day price, the dead-water-period highest single-day price and the dead-water-period lowest single-day price are respectively obtained, and the high-water-period maximum price difference, the flat-water-period maximum price difference and the dead-water-period maximum price difference are calculated.
In this embodiment, the fluctuation degree of the electric power selling price in different water periods can be quantified by calculating the fluctuation rate of the price, knowing the fluctuation rate of the price can help the electric power company to make a risk management strategy, reduce the influence of instability on the business, calculate the fluctuation rate based on the historical price data, so the method is a data-driven method, is helpful for providing objective and quantifiable analysis results, supports decision making, can have different market characteristics in different water periods, and can better understand the market behaviors in different water periods and make corresponding strategies by respectively calculating the price fluctuation rate in the high water period, the flat water period and the dead water period.
Specifically, the calculation formula for estimating the cost loss based on the power marketing data architecture information and the power sales price is as follows:wherein CS is (f,p,k) I=1, 2,3, for the loss of water-full period cost or water-flat period cost or water-dead period cost, b is the total amount of monthly completions in water-full period or water-flat period or water-dead period, YD i For the total amount of the i-th monthly transaction in the full-water period or the flat-water period or the dead-water period, i=1, 2,3,.. D is the number of the single-day transaction in the full-water period or the single-day transaction in the flat-water period or the single-day transaction in the dead-water period, CT i For the ith month of traditional electric power transaction amount in the full or flat or dead water period, delta ct For the maintenance cost of traditional power per degree, XN i The new energy power transaction amount delta is the ith month in the full-water period, the flat-water period or the dead-water period xn And maintaining the cost for each degree of new energy power.
In this embodiment, the cost loss of different water periods is estimated by considering the monthly total amount of the power, the price of the power, the traditional power transaction amount, the new energy power transaction amount and the like, and the formula is calculated based on the historical sales data and the cost data, so that a data-driven decision basis is provided, the cost structure of different water periods can be more comprehensively known by considering the cost of the traditional power and the new energy power, and a plurality of data sources including the total amount of the power, the price of the power, the traditional power transaction amount and the new energy power transaction amount are utilized, so that a more comprehensive cost estimation is provided.
In summary, the present application has at least the following effects:
calculating the power price fluctuation rate in the rich period, the flat period and the dead period provides profound market insight for electric power companies, helps to know price fluctuation degree and operation cost risks, helps companies to predict market fluctuation better and reduce potential losses, supports optimizing operation strategies, improves risk management, improves market competitiveness, enables the electric power price fluctuation rate to solve technical problems more specifically, and improves business performance.
Through the data-driven decision based on historical data, the electric company can evaluate market conditions more accurately, adopts an adaptive strategy, reduces uncertainty, better meets customer requirements, is beneficial to reducing economic risks, and also supports the company to make intelligent decisions, so that the electric company can operate and succeed in market fluctuation environments in different water periods better.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of systems, apparatuses (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. The electric power marketing data acquisition and analysis method is characterized by comprising the following steps of:
acquiring electric power marketing data, and storing the electric power marketing data in a preset storage structure in a classified manner according to set attributes, wherein the set attributes comprise a high water period, a flat water period and a dead water period;
traversing the electric power marketing data stored in the storage structure and filling the electric power marketing data into a table;
acquiring electric power marketing data stored in a storage structure, and analyzing electric power marketing data architecture information of a rich period, a flat period and a dead period, wherein the electric power marketing data architecture information comprises a total amount of a transaction, a new energy duty and a traditional electric power duty;
respectively predicting the power generation increase rate in the water-rich period, the power generation increase rate in the water-flat period and the power generation increase rate in the water-free period based on the power marketing data architecture information;
calculating the price fluctuation rate of the price in the high water period, the price fluctuation rate of the price in the low water period and the price fluctuation rate of the price in the low water period based on the price in the electric power sales in the high water period, the low water period and the price in the low water period respectively;
cost losses are estimated based on the power marketing data architecture information and the power sales price.
2. The method for collecting and analyzing electric power marketing data according to claim 1, wherein the process of classifying and storing the electric power marketing data in a preset storage structure according to the set attribute is as follows:
acquiring power marketing data, and preprocessing the power marketing data, wherein the preprocessing comprises deleting blank values and abnormal values;
classifying according to the time stamp in the electric power marketing data, and storing the classified electric power marketing data into the corresponding area in the storage structure.
3. The method for collecting and analyzing electric power marketing data according to claim 2, wherein the data verification is performed after the classified electric power marketing data is stored in a corresponding area in the storage structure, and the verification process is as follows: the connection storage structure is used for inquiring electric power marketing data; comparing the query result with the preprocessed power marketing data, including data quantity comparison and data value comparison; recording the verification result.
4. The method for collecting and analyzing the electric power marketing data according to claim 2, wherein the process of traversing the electric power marketing data stored in the storage structure and filling the electric power marketing data into the table is as follows:
creating an empty table, adding columns of the table, and naming according to the attribute;
traversing the data in the water-filling period in the storage structure, and filling each piece of data into the water-filling period column of the table;
traversing the horizontal period data in the storage structure, and filling each piece of data into the horizontal period column of the table;
traversing the dead water period data in the storage structure, and filling each piece of data into the dead water period column of the table;
the table is saved.
5. The method for collecting and analyzing power marketing data according to claim 2, wherein the total amount of the annual and monthly exchanges are comprised, the new energy duty comprises new energy annual and new energy monthly duty, and the traditional power duty comprises traditional power annual and traditional power monthly duty.
6. The method for collecting and analyzing electric power marketing data according to claim 5, wherein the process of analyzing electric power marketing data structure information of the water-rich period, the water-flat period and the water-free period is as follows:
acquiring annual and monthly total volume of the deals;
acquiring single-day traditional power trading volume and single-day new energy power trading volume in single-day trading volume, and respectively calculating annual traditional power trading volume and monthly traditional power trading volume based on the single-day traditional power trading volume; calculating new annual energy power trading volume and new monthly energy power trading volume based on the new daily energy power trading volume respectively;
calculating a traditional power annual duty and a traditional power monthly duty respectively based on the annual traditional power trading volume and the monthly traditional power trading volume;
and calculating new energy annual duty and new energy monthly duty respectively based on the annual new energy power transaction amount and the monthly new energy power transaction amount.
7. The method for collecting and analyzing the power marketing data according to claim 6, wherein the process of respectively predicting the growth rate of the power generation in the rich period, the growth rate of the power generation in the flat period and the growth rate of the power generation in the dry period based on the power marketing data structure information is as follows:
acquiring the daily yield of electric power marketing data in a high water period and the daily yield of electric power marketing data in a low water period;
calculating the high-water-period power generation quantity, the flat-water-period power generation quantity and the dead-water-period power generation quantity of each year respectively based on the high-water-period single-day power generation quantity, the flat-water-period single-day power generation quantity and the dead-water-period single-day power generation quantity;
the high-water-period power generation increase rate, the flat-water-period power generation increase rate and the dead-water-period power generation increase rate are calculated based on the high-water-period power generation amount, the flat-water-period power generation amount and the dead-water-period power generation amount of each year respectively, and the calculation formula is as follows:
wherein P is% f,p,k ) For the power generation increase rate in the rich period or the power generation increase rate in the flat period or the power generation increase rate in the dry period,for the i-th year's high-water-period or flat-water-period or dead-water-period traffic, the term +.>The i+1th year's high-water-period or flat-water-period or dead-water-period traffic is calculated as the growth rate calculation factor, and e is a natural constant.
8. The method for collecting and analyzing the electric power marketing data according to claim 7, wherein the process of calculating the price fluctuation rate of the electric power sales based on the high water period, the flat water period and the dead water period is as follows:
acquiring a high-water-period single-day price in the electric power marketing data, and acquiring a flat-water-period single-day price in the electric power marketing data;
calculating the average price of the power selling in the high water period, the average price of the power selling in the low water period and the average price of the power selling in the low water period respectively based on the single-day price of the high water period, the single-day price of the single-day period and the price of the single-day period;
the method comprises the following steps of respectively calculating the fluctuation rate of the high-water-period single-day price, the fluctuation rate of the low-water-period single-day price and the fluctuation rate of the low-water-period single-day price based on the high-water-period single-day price, the low-water-period single-day price, the average price of the high-water-period power sales price and the average price of the low-water-period power sales price, wherein the calculation formulas are as follows:
wherein bo (f,p,k ) For the high-water-period price-change fluctuation rate or the flat-water-period price-change fluctuation rate or the dead-water-period price-change fluctuation rate, i=1, 2,3,..m is the number of the high-water-period price-change or the flat-water-period price-change or the dead-water-period price-change,for the single-day price of high water period, single-day price of flat water period or single-day price of dead water period, gamma is fluctuation modulation factor, delta JC # f,p,k ) Is the maximum price difference in the water-rich period or the maximum price difference in the water-flat period or the maximum price difference in the water-free period.
9. The method for collecting and analyzing the electric power marketing data according to claim 8, wherein the calculating process of the maximum price difference in the water-rich period or the maximum price difference in the water-flat period or the maximum price difference in the water-free period is as follows: after the high-water-period single-day price, the flat-water-period single-day price and the dead-water-period single-day price of the electric power marketing data are obtained, the high-water-period highest single-day price, the high-water-period lowest single-day price, the flat-water-period highest single-day price, the dead-water-period highest single-day price and the dead-water-period lowest single-day price are respectively obtained, and the high-water-period maximum price difference, the flat-water-period maximum price difference and the dead-water-period maximum price difference are calculated.
10. The method for collecting and analyzing power marketing data according to claim 8, wherein the calculation formula of the cost loss estimated based on the power marketing data structure information and the power marketing price is as follows:
wherein CS is (f,p,k) I=1, 2,3, for the loss of water-full period cost or water-flat period cost or water-dead period cost, b is the total amount of monthly completions in water-full period or water-flat period or water-dead period, YD i For the total amount of the i-th monthly transaction in the full-water period or the flat-water period or the dead-water period, i=1, 2,3,.. D is the number of the single-day transaction in the full-water period or the single-day transaction in the flat-water period or the single-day transaction in the dead-water period, CT i For the ith month of traditional electric power transaction amount in the full or flat or dead water period, delta ct For the maintenance cost of traditional power per degree, XN i The new energy power transaction amount delta is the ith month in the full-water period, the flat-water period or the dead-water period xn And maintaining the cost for each degree of new energy power.
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