Method and device for predicting operation data of power transmission and distribution, and electronic equipment

Abstract

The invention discloses a power transmission and distribution operation data prediction method and device and electronic equipment. The method comprises the steps that all users of power transmission and distribution in a to-be-predicted target area are acquired, the user category of each user is determined, and the user category comprises at least one of industrial users, industrial and commercial users and agricultural users; historical operation data of the target area is acquired, power transmission and distribution indexes of each user are predicted through a preset time sequence algorithm and the historical operation data, and the power transmission and distribution indexes comprise at least one of the following: power sale quantity, capacity, demand, force regulation increase amount, force regulation decrease amount and agent electricity purchase and sale quantity; and calculating sub-operation data of each user through the power transmission and distribution indexes and the power transmission and distribution prices, and calculating the sum of the sub-operation data of all the users to obtain predicted operation data of the target region. According to the invention, the problem that the prediction result of the operation data of power transmission and distribution is not accurate in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of power transmission and distribution, and more specifically, to a method, device and electronic equipment for predicting operational data of power transmission and distribution. Background Art

[0002] The power grid is the intermediate link between the power generation side and the power consumption side, and the power transmission and distribution system is a key component of the power grid. With the continuous growth of power demand, it is necessary to efficiently manage the power transmission and distribution system to ensure a stable supply of electricity. How to predict and analyze the operational data of power transmission and distribution is one of the key issues to support efficient management of power transmission and distribution.

[0003] The operational data of power transmission and distribution include: power grid operation data, such as bus power, voltage, frequency, power flow, etc.; economic operation data of power transmission and distribution, such as power generation cost, power transmission and distribution cost, line loss cost, depreciation of distribution network assets, etc.; power transmission and distribution user operation data, such as power sales volume, capacity, demand, electricity price, electricity charges, etc.

[0004] In power planning, operation and management, the operation data of power supply enterprises, including economic operation data of power transmission and distribution, operation data of power transmission and distribution users and power grid operation data, are key decision-making inputs. When conducting power planning and operation management, power supply enterprises use operation data for decision analysis, such as predicting economic benefits under different scenarios, analyzing load characteristics of different user types, analyzing the operation status of the power grid, and formulating reasonable power purchase and sales plans. Therefore, power supply enterprises need to predict the operation data of power transmission and distribution to provide support for efficient management and decision-making of the power grid.

[0005] In the related technology, power supply enterprises mainly rely on manual experience to predict operation data. This prediction method has low accuracy and low efficiency, and cannot meet the accuracy and efficiency requirements of power supply enterprises for operation data prediction.

[0006] Currently, no effective solution has been proposed to the problem of inaccurate prediction results of power transmission and distribution operation data in related technologies. Summary of the invention

[0007] The main purpose of the present application is to provide a method, device and electronic equipment for predicting the operation data of power transmission and distribution, so as to solve the problem of inaccurate prediction results of the operation data of power transmission and distribution in the related art.

[0008] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for predicting the operation data of power transmission and distribution is provided. The method comprises: obtaining all users of power transmission and distribution in the target area to be predicted, and determining the user category of each user, wherein the user category includes at least one of the following: industrial users, industrial and commercial users, and agricultural users; obtaining the historical operation data of the target area, and predicting the power transmission and distribution index of each user through a preset time series algorithm and historical operation data, wherein the power transmission and distribution index includes at least one of the following: power sales, capacity, demand, power regulation increase, power regulation decrease, and power purchase and sales by proxy; calculating the sub-operation data of each user through the power transmission and distribution index and the power transmission and distribution price, and calculating the sum of the sub-operation data of all users to obtain the predicted operation data of the target area.

[0009] Optionally, predicting the power transmission and distribution index of each user by using a preset time series algorithm and historical operating data includes: for each power transmission and distribution index, determining first historical data of the target power transmission and distribution index of the target area from the historical operating data; predicting the target data of the target area based on the preset time series algorithm and the first historical data, wherein the target data is the predicted data of the target power transmission and distribution index of the target area within the target period; determining the indicator proportion of the user in the historical operating data, wherein the indicator proportion is the ratio of the historical data of the user's target power transmission and distribution index to the first historical data; and calculating the product of the target data and the indicator proportion to obtain the predicted data of the user's target power transmission and distribution index.

[0010] Optionally, when the target power transmission and distribution indicator is power sales volume, after obtaining the predicted data of the user's target power transmission and distribution indicator, the method further includes: determining the expected power sales volume data of the target area, calculating the ratio of the expected power sales volume data to the target data, and obtaining the adjustment coefficient; calculating the product of the user's predicted power sales volume data and the adjustment coefficient, and obtaining the user's adjusted predicted power sales volume data.

[0011] Optionally, predicting target data of a target area based on a preset time series algorithm and the first historical data includes: determining at least one time series algorithm and determining a weight for each time series algorithm to obtain a set of weights; inputting the first historical data into each time series algorithm for calculation to obtain predicted data for each time series algorithm; and performing weighted summation of the predicted data for each time series algorithm based on a set of weights to obtain target data.

[0012] Optionally, calculating the sub-operation data of each user through the transmission and distribution index and the transmission and distribution price includes: when the transmission and distribution index is the amount of electricity sold, determining the transmission and distribution unit price from the transmission and distribution price, calculating the product of the transmission and distribution unit price and the predicted amount of electricity sold, and obtaining the kilowatt-hour electricity price data; when the transmission and distribution index is capacity and demand, determining the capacity electricity price and the demand electricity price from the transmission and distribution price, calculating the product of the capacity electricity price and the predicted capacity data, obtaining the capacity electricity fee, calculating the product of the demand electricity price and the predicted demand data, obtaining the demand electricity fee, calculating the sum of the capacity electricity fee and the demand electricity fee, and obtaining the basic electricity fee; when the transmission and distribution index is the power regulation increase amount and the power regulation decrease amount, determining the first power regulation prediction data of the power regulation increase amount and the second power regulation prediction data of the power regulation decrease amount, and calculating The power regulation electricity fee is obtained by the difference between the first power regulation forecast data and the second power regulation forecast data; when the transmission and distribution indicator is the proxy purchase and sale of electricity, the peak power consumption period forecast data and the valley power consumption period forecast data of the proxy purchase and sale of electricity are determined, the peak electricity price, the valley electricity price and the flat electricity price are determined from the transmission and distribution price, the difference between the peak electricity price and the flat electricity price is calculated, and the product of the difference and the peak power consumption period forecast data is calculated to obtain the first profit and loss, the difference between the valley electricity price and the flat electricity price is calculated, and the product of the difference and the valley power consumption period forecast data is calculated to obtain the second profit and loss, and the sum of the first profit and loss and the second profit and loss is calculated to obtain the peak-valley profit and loss; the accumulated value of at least one of the following is determined as the user's sub-operation data: electricity price data, basic electricity fee, power regulation electricity fee and peak-valley profit and loss.

[0013] Optionally, after calculating the sum of the sub-operation data of all users to obtain the predicted operation data of the target area, the method also includes: determining the line loss rate and the average electricity purchase price of the target area, wherein the line loss rate is the ratio of the line transmission loss electricity in the power transmission and distribution process to the electricity sales volume; calculating the ratio of the electricity sales volume to the transmission efficiency, and calculating the product of the ratio, the line loss rate and the average electricity purchase price to obtain the line loss cost, wherein the transmission efficiency is the difference between the preset value and the line loss rate; calculating the difference between the predicted operation data and the line loss cost to obtain the target operation data of the target area excluding line loss.

[0014] Optionally, after calculating the sum of the sub-operation data of all users to obtain the predicted operation data of the target area, the method also includes: obtaining the actual operation data of the target area, calculating the difference between the predicted operation data and the actual operation data, and calculating the ratio of the difference to the actual operation data to obtain the deviation rate; when the deviation rate is greater than or equal to the deviation rate threshold, updating the preset time series algorithm, and executing the step of predicting the power transmission and distribution indicators of each user through the preset time series algorithm and historical operation data based on the updated preset time series algorithm.

[0015] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a prediction device for the operation data of power transmission and distribution is provided. The device includes: an acquisition unit, which is used to acquire all users of power transmission and distribution in the target area to be predicted, and determine the user category of each user, wherein the user category includes at least one of the following: industrial users, industrial and commercial users, and agricultural users; a prediction unit, which is used to acquire the historical operation data of the target area, and predict the power transmission and distribution index of each user through a preset time series algorithm and historical operation data, wherein the power transmission and distribution index includes at least one of the following: power sales, capacity, demand, power regulation increase, power regulation decrease, and power purchase and sales by proxy; a calculation unit, which is used to calculate the sub-operation data of each user through the power transmission and distribution index and the power transmission and distribution price, and calculate the sum of the sub-operation data of all users to obtain the predicted operation data of the target area.

[0016] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a computer program product is provided, including a computer program, which, when executed by a processor, implements the steps of the method for predicting the operation data of power transmission and distribution described in each embodiment of the present application.

[0017] Through this application, the following steps are adopted: obtain all users of power transmission and distribution in the target area to be predicted, and determine the user category of each user, wherein the user category includes at least one of the following: industrial users, industrial and commercial users, and agricultural users; obtain the historical operation data of the target area, and predict the power transmission and distribution indicators of each user through a preset time series algorithm and historical operation data, wherein the power transmission and distribution indicators include at least one of the following: power sales, capacity, demand, power regulation increase, power regulation decrease, and agent power purchase and sales; calculate the sub-operation data of each user through the power transmission and distribution indicators and the power transmission and distribution price, calculate the sum of the sub-operation data of all users, and obtain the predicted operation data of the target area, which solves the problem of inaccurate prediction results of the power transmission and distribution operation data in the related technology. By obtaining and predicting the power transmission and distribution indicators of all power transmission and distribution users in the target area, the comprehensiveness and accuracy of the prediction results are ensured, the revenue prediction needs of power grid companies under different user categories are met, and the preset time series algorithm is used to analyze and predict the historical operation data, accurately predict the future trend of the user's power transmission and distribution indicators, and improve the prediction accuracy, thereby achieving the effect of improving the accuracy of the predicted power transmission and distribution operation data. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 is a flow chart of a method for predicting operation data of power transmission and distribution provided in an embodiment of the present application;

[0020] Figure 2 is a schematic diagram of a device for predicting operation data of power transmission and distribution provided in an embodiment of the present application;

[0021] Figure 3 is a schematic diagram of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0023] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties.

[0026] It should be noted that the information collected is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data comply with the relevant laws, regulations and standards of the relevant regions, take necessary confidentiality measures, do not violate public order and good customs, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0027] For the convenience of description, some nouns or terms involved in the embodiments of the present application are explained below:

[0028] Basic electricity fee: The electricity fee is calculated based on the maximum demand, maximum capacity, and the actual maximum demand of the user and is not related to the amount of electricity consumed;

[0029] Electricity price data: electricity price per kilowatt-hour determined according to electricity consumption category and electricity fee calculated based on electricity consumption;

[0030] Power adjustment electricity fee: the electricity fee charged according to the change of the user's power factor;

[0031] Peak-valley profit and loss: refers to the peak-valley profit and loss generated by the power purchasing agent of large industries and general industry and commerce in the process of power transmission and distribution transactions;

[0032] Peak and valley profits and losses from agency electricity purchases: refers to the peak and valley profits and losses generated by the agency electricity purchase part of large industries and general industry and commerce during the agency electricity purchase transaction process.

[0033] The present invention is described below in conjunction with preferred implementation steps. Figure 1 is a flow chart of a method for predicting power transmission and distribution operation data according to an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps:

[0034] Step S101, obtaining all users of power transmission and distribution in the target area to be predicted, and determining the user category of each user, wherein the user category includes at least one of the following: industrial users, industrial and commercial users, and agricultural users.

[0035] In step S101, industrial users refer to large industrial users, i.e. industrial production enterprises with large electricity consumption, special and stable electricity consumption. Industrial and commercial users refer to the group of electricity users engaged in industrial production and commercial operations, except for large industrial users and specific residential users. Agricultural users can be ordinary residents, agriculture, paddy field irrigation, wholesale sales and other users.

[0036] Among them, the operating data of industrial users include kilowatt-hour electricity price data, basic electricity charges, power regulation electricity charges and peak-valley profit and loss; the operating data of industrial and commercial users include kilowatt-hour electricity price data, power regulation electricity charges and peak-valley profit and loss. The peak-valley profit and loss only calculates the peak-valley profit and loss of power transmission and distribution of the power purchased by the agent. The operating data of agricultural users only includes kilowatt-hour electricity price data.

[0037] Step S102, obtaining historical operation data of the target area, and predicting the power transmission and distribution indicators of each user through a preset time series algorithm and historical operation data, wherein the power transmission and distribution indicators include at least one of the following: power sales, capacity, demand, power regulation increase, power regulation decrease, and agency power purchase and sales.

[0038] In step S102, the transmission and distribution indicators may be indicators predicted based on historical operation data, including power sales, capacity, demand, power sales revenue for non-industrial and commercial users (residential, agricultural, paddy field irrigation and drainage, bulk sales), power regulation increase, power regulation decrease, power sales of the power purchase agent, etc. The preset time series algorithm may be Holt-Winters (exponential smoothing method), spectral decomposition, PROPHET (a tool library for time series prediction), GARCH (Generalized Autoregressive Conditional Heteroskedasticity), LSTM (Long Short-Term Memory), CHAOS (Chaos Theory), etc. The historical operation data includes past transmission and distribution indicators and operation data.

[0039] For example, for the prediction of power transmission and distribution indicators, the target region can be the target province. First, the historical change trend of the annual data of each power transmission and distribution indicator of the target province is obtained, and the annual results of the target region are predicted using a time series algorithm or based on the year-on-year growth rate; then, the historical city proportion, county proportion, user category proportion, voltage level proportion, peak and valley proportion, and monthly proportion under the indicator are calculated in combination with the dimensional conditions of each power transmission and distribution indicator. According to the proportion results, the annual prediction results of the target region of each power transmission and distribution indicator are allocated layer by layer to obtain the detailed prediction data of each city and county under the jurisdiction of the target region. Then, the prediction results of the power transmission and distribution indicators of each user are obtained according to the proportion of each user in the county or city to which they belong.

[0040] Step S103, calculating the sub-operation data of each user through the power transmission and distribution index and the power transmission and distribution price, calculating the sum of the sub-operation data of all users, and obtaining the predicted operation data of the target area.

[0041] In step S103, after obtaining the prediction results of the power transmission and distribution indicators of each user, the sub-operation data corresponding to the user category is calculated according to the prediction data of each power transmission and distribution indicator and the predetermined power transmission and distribution price. For example, if the user is an industrial user, the sub-operation data of the user that needs to be calculated includes electricity price data, basic electricity fee, power regulation electricity fee and peak-valley profit and loss. The above operation data are summed to obtain the sub-operation data of the user. After determining the sub-operation data of all users in the target area, the sub-operation data of all users are summed to obtain the predicted operation data of the target area.

[0042] The prediction method of the operation data of power transmission and distribution provided in the embodiment of the present application obtains all users of power transmission and distribution in the target area to be predicted, and determines the user category of each user, wherein the user category includes at least one of the following: industrial users, industrial and commercial users, and agricultural users; obtains the historical operation data of the target area, and predicts the power transmission and distribution index of each user through a preset time series algorithm and historical operation data, wherein the power transmission and distribution index includes at least one of the following: power sales, capacity, demand, power regulation increase, power regulation decrease, and power purchase and sales by proxy; calculates the sub-operation data of each user through the power transmission and distribution index and the power transmission and distribution price, calculates the sum of the sub-operation data of all users, and obtains the predicted operation data of the target area, which solves the problem of inaccurate prediction results of the operation data of power transmission and distribution in the related technology. By obtaining and predicting the power transmission and distribution index of all power transmission and distribution users in the target area, the comprehensiveness and accuracy of the prediction results are ensured, the income prediction needs of power grid enterprises under different user categories are met, and the preset time series algorithm is used to analyze and predict the historical operation data, accurately predict the future trend of the user's power transmission and distribution index, and improve the prediction accuracy, thereby achieving the effect of improving the accuracy of the predicted power transmission and distribution operation data.

[0043] The user's power transmission and distribution index can be calculated based on the user's index proportion in historical operating data. Optionally, in the method for predicting power transmission and distribution operating data provided in an embodiment of the present application, predicting each user's power transmission and distribution index through a preset time series algorithm and historical operating data includes: for each power transmission and distribution index, determining first historical data of the target power transmission and distribution index of the target area from the historical operating data; predicting the target data of the target area based on the preset time series algorithm and the first historical data, wherein the target data is the predicted data of the target power transmission and distribution index of the target area within the target period; determining the user's index proportion in the historical operating data, wherein the index proportion is the ratio of the historical data of the user's target power transmission and distribution index to the first historical data; calculating the product of the target data and the index proportion to obtain the predicted data of the user's target power transmission and distribution index.

[0044] In some examples, the transmission and distribution indicators that need to be predicted for different user categories may be different. For example, for industrial users, since the operating data of industrial users include kilowatt-hour electricity price data, basic electricity charges, power regulation electricity charges and peak-valley profit and loss, the transmission and distribution indicators that industrial users need to predict need to include electricity sales, capacity, demand, power regulation increase, power regulation decrease and agency power purchase and sales. For agricultural users, since the operating data of agricultural users only includes kilowatt-hour electricity price data, the transmission and distribution indicators that agricultural users need to predict only include electricity sales. The historical operating data can be the data of the previous audit cycle, and the target period can be the next audit cycle. The audit cycle refers to the period in which relevant agencies supervise and review the transmission and distribution prices of power grid companies. For example, the audit cycle can be three years, that is, the transmission and distribution prices of power grid companies will be re-determined every three years.

[0045] For each power transmission and distribution indicator, the target data of the target area is first predicted based on the preset time series algorithm and the first historical data, and then the predicted data of the user's target power transmission and distribution indicator is determined according to the proportion of the user's indicators in the historical operation data. For example, multiple preset time series algorithms such as exponential smoothing, spectral decomposition, PROPHET, GARCH, LSTM, CHAOS, etc. are used to predict based on the first historical data to obtain multiple prediction results, and then the multiple prediction results are weighted and summed according to the preset weights of different prediction time series algorithms to obtain the target data.

[0046] Then, according to the historical city proportion, county proportion, user category proportion, voltage level proportion, peak and valley proportion, monthly proportion and other indicators in the first historical data of the target power transmission and distribution indicators, the product of the target data and the indicator proportion is calculated to obtain the predicted data of the user's target power transmission and distribution indicators.

[0047] This embodiment analyzes and predicts historical operation data through a preset time series algorithm, effectively grasps the future trend of user power transmission and distribution indicators, and improves prediction accuracy.

[0048] In order to improve the prediction accuracy, the predicted electricity sales data is adjusted based on the adjustment coefficient. Optionally, in the prediction method for the operation data of power transmission and distribution provided in the embodiment of the present application, when the target power transmission and distribution indicator is the electricity sales volume, after obtaining the predicted data of the user's target power transmission and distribution indicator, the method further includes: determining the expected electricity sales data of the target area, calculating the ratio of the expected electricity sales data to the target data, and obtaining the adjustment coefficient; calculating the product of the user's predicted electricity sales data and the adjustment coefficient, and obtaining the user's adjusted predicted electricity sales data.

[0049] In some examples, the expected electricity sales data may be data input by a power grid company, which refers to a forecast of the total amount of electricity expected to be sold to users, obtained by the power grid company or power company based on a comprehensive analysis of market trends, economic development, changes in user demand, policy adjustments, seasonal factors, and historical data.

[0050] For example, according to the transmission and distribution indicators predicted in the above embodiment, the electricity sales of industrial and commercial users by user category and voltage level (and the peak and valley of the proxy power purchase part) in each city and county in the target area are predicted respectively, as well as the electricity sales of non-industrial and commercial users by user category. Then, the two parts of electricity sales of each city and county are summarized to obtain the annual total electricity sales of the target area and the annual total electricity sales of each city, and then calculated according to the input expected electricity sales data:

[0051] Adjustment coefficient of electricity sales in target area = expected electricity sales data of input target area / target data of target area;

[0052] The adjustment coefficient of electricity sales in each city in the target area = the expected electricity sales data of each city / the target data of each city;

[0053] If the adjustment coefficient is 0, the forecast result will be retained. If the calculated adjustment coefficient is not 0, the electricity sales volume for each city and county will be multiplied by the corresponding adjustment coefficient by electricity consumption category, voltage level, peak and valley levels, and the forecast electricity sales data after adjustment for each city and county will be obtained. The adjusted forecast electricity sales data will be used to support subsequent measurement work.

[0054] Based on the historical data of electricity sales of all user categories, the forecast data of electricity sales of industrial and commercial users by voltage level (as well as the peak and valley of the proxy power purchase part) and the forecast data of electricity sales of non-industrial and commercial users by user category are predicted. According to the expected electricity sales data of the input target area and the expected electricity sales data of various cities, the forecast results of electricity sales of various cities and counties are adjusted respectively, and the forecast electricity sales data of various cities and counties after adjustment by electricity category and voltage level are obtained.

[0055] This embodiment adjusts the forecast data of electricity sales by adjusting the coefficient, which can effectively calibrate the deviation of the forecast result and improve the accuracy of the forecast, thereby ensuring that the forecast result matches the actual demand more closely and avoiding waste or shortage of resources.

[0056] The target data can be obtained by weighted summation of multiple time series algorithms. Optionally, in the prediction method of power transmission and distribution operation data provided in the embodiment of the present application, predicting the target data of the target area based on the preset time series algorithm and the first historical data includes: determining at least one time series algorithm, and determining the weight of each time series algorithm to obtain a set of weights; inputting the first historical data into each time series algorithm for calculation to obtain the predicted data of each time series algorithm; and performing weighted summation of the predicted data of each time series algorithm based on a set of weights to obtain the target data.

[0057] In some examples, time series algorithms may include exponential smoothing (Holt-Winters), spectral decomposition, PROPHET, GARCH, LSTM, CHAOS, and the predicted data of each time series algorithm is weighted summed according to the predetermined weight of each time series algorithm to obtain the target data.

[0058] In some optional embodiments, the target data can also be obtained by predicting historical data based on the year-on-year growth rate. The target data is obtained by observing the historical change trend of the annual data of the target area of ​​the power transmission and distribution indicators and predicting its annual results using the year-on-year growth rate.

[0059] This embodiment predicts target data by using the precision of different time series algorithms as weights for weighted combination, and analyzes the frequency domain and frequency domain characteristics of various types of time series data such as trend, periodicity, seasonality and randomness, thereby ensuring the accuracy of the prediction results.

[0060] The sub-operation data can be obtained by calculating the predicted data of the transmission and distribution index and the transmission and distribution price. Optionally, in the prediction method of the transmission and distribution operation data provided in the embodiment of the present application, the sub-operation data of each user is calculated by the transmission and distribution index and the transmission and distribution price, including: when the transmission and distribution index is the amount of electricity sold, the transmission and distribution unit price is determined from the transmission and distribution price, and the product of the transmission and distribution unit price and the predicted amount of electricity sold is calculated to obtain the kilowatt-hour electricity price data; when the transmission and distribution index is capacity and demand, the capacity electricity price and the demand electricity price are determined from the transmission and distribution price, the product of the capacity electricity price and the predicted capacity data is calculated to obtain the capacity electricity fee, the product of the demand electricity price and the predicted demand data is calculated to obtain the demand electricity fee, and the sum of the capacity electricity fee and the demand electricity fee is calculated to obtain the basic electricity fee; when the transmission and distribution index is the power regulation increase amount and the power regulation decrease amount, the power regulation increase amount is determined The first power regulation forecast data and the second power regulation forecast data of the power regulation reduction amount are used to calculate the difference between the first power regulation forecast data and the second power regulation forecast data to obtain the power regulation electricity fee; when the transmission and distribution indicator is the agent purchase and sale of electricity, the peak power consumption period forecast data and the valley power consumption period forecast data of the agent purchase and sale of electricity are determined, the peak electricity price, the valley electricity price and the flat electricity price are determined from the transmission and distribution price, the difference between the peak electricity price and the flat electricity price is calculated, and the product of the difference and the peak power consumption period forecast data is calculated to obtain the first profit and loss, the difference between the valley electricity price and the flat electricity price is calculated, and the product of the difference and the valley power consumption period forecast data is calculated to obtain the second profit and loss, and the sum of the first profit and loss and the second profit and loss is calculated to obtain the peak-valley profit and loss; the accumulated value of at least one of the following is determined as the user's sub-operation data: electricity price data, basic electricity fee, power regulation electricity fee and peak-valley profit and loss.

[0061] In some embodiments, the kilowatt-hour electricity price data needs to be calculated based on user categories. For example, for industrial users, the transmission and distribution unit price of industrial users is determined according to the corresponding transmission and distribution price catalog in the price list (including line loss) of the predicted audit period, and the product of the adjusted predicted electricity sales data and the transmission and distribution unit price is calculated to obtain the kilowatt-hour electricity price data of industrial users. For industrial and commercial users, the transmission and distribution unit price of industrial and commercial users is determined according to the corresponding transmission and distribution price catalog in the price list (including line loss) of the predicted audit period, and the product of the adjusted predicted electricity sales data and the transmission and distribution unit price is calculated to obtain the kilowatt-hour electricity price data of industrial and commercial users. For agricultural users, the sales unit price is calculated based on the adjusted predicted electricity sales data and electricity sales revenue, and then the sales unit price is deducted from the average purchase price of electricity in the price list (including line loss) to obtain the transmission and distribution unit price, and finally the adjusted predicted electricity sales data is multiplied by the transmission and distribution unit price to obtain the kilowatt-hour electricity price data of agricultural users.

[0062] For industrial users, the operating data of basic electricity charges is also involved. The basic electricity charges for industrial users include capacity charges calculated based on the maximum capacity and demand charges calculated based on the maximum demand. The capacity charge is calculated based on the predicted capacity data for each voltage level, combined with the capacity electricity price corresponding to the capacity of this voltage level in the price list (including line loss) of the audit period; the demand charge is calculated based on the predicted demand data for each voltage level, combined with the demand electricity price corresponding to the demand of this voltage level in the price list (including line loss) of the audit period. Calculate the sum of the capacity charge and the demand charge to get the basic charge.

[0063] For industrial users and commercial and industrial users, the operational data of power regulation electricity charges is also involved. The power regulation electricity charges are the power regulation increase and power regulation decrease predicted based on historical data. The power regulation electricity charges are obtained by calculating the difference between the first power regulation prediction data of the power regulation increase and the second power regulation prediction data of the power regulation decrease.

[0064] For industrial users and commercial and industrial users, the operational data of peak and valley profit and loss is also involved. It is calculated by subtracting the flat-section transmission and distribution price (also known as the flat-section electricity price) of the voltage level from the peak and valley power transmission and distribution price of the voltage level, and then multiplying it by the predicted value of the adjusted peak and valley power sales volume of the agent power purchase and sales volume by voltage level, and finally summing them up to obtain the peak and valley profit and loss.

[0065] This embodiment makes segmented forecasts of transmission and distribution indicators such as power sales, capacity, demand, power regulation increase, power regulation decrease, and power purchase and sales by proxy, and calculates specific costs in combination with transmission and distribution prices, such as kilowatt-hour electricity price data, capacity electricity charges, demand electricity charges, power regulation electricity charges, and peak and valley profit and loss, so that power grid companies can achieve more refined cost management and operational data forecasts. This enables companies to more accurately evaluate the revenue and costs of different business segments, thereby optimizing financial planning and resource allocation.

[0066] Since the above-mentioned embodiment calculates the predicted operating data including line loss, it is also necessary to remove the line loss cost to obtain the target operating data excluding line loss. Optionally, in the method for predicting the operating data of power transmission and distribution provided in the embodiment of the present application, after calculating the sum of the sub-operating data of all users to obtain the predicted operating data of the target area, the method further includes: determining the line loss rate and the average purchase price of electricity in the target area, wherein the line loss rate is the ratio of the line transmission loss electricity in the power transmission and distribution process to the electricity sales; calculating the ratio of the electricity sales to the transmission efficiency, and calculating the product of the ratio with the line loss rate and the average purchase price of electricity to obtain the line loss cost, wherein the transmission efficiency is the difference between the preset value and the line loss rate; calculating the difference between the predicted operating data and the line loss cost to obtain the target operating data excluding line loss in the target area.

[0067] In some examples, the line loss rate may be a value determined based on historical operation data. The line loss rates of various urban areas and counties under the jurisdiction of the target area may be different. Therefore, when calculating the line loss cost, the line loss cost is calculated separately for different regions, and finally integrated to obtain the line loss cost of the target area. The transmission efficiency may be 1-line loss rate.

[0068] For example, the forecasted operating data including line losses of each county is determined. The forecasted operating data including line losses of each county = the sub-operating data of industrial users in each county and the sub-operating data including line losses of industrial and commercial users + the sub-operating data of agricultural users in each county. The line loss cost of each county = the adjusted total electricity sales of all users in each county / (1-line loss rate of the city to which it belongs)*line loss rate of the city to which it belongs*average electricity purchase price. The operating data of each county excluding line losses = the forecasted operating data including line losses of each county-the line loss cost of each county.

[0069] Predicted operation data including line loss in each city = sub-operation data of industrial users in each city + sub-operation data including line loss of industrial and commercial users in each city + sub-operation data of agricultural users in each city. Line loss cost in each city = total electricity sales after adjustment for all users in each city / (1-line loss rate in each city)*line loss rate in each city*average purchase price of electricity. Operation data excluding line loss in each city = predicted operation data including line loss in each city - line loss cost in each city.

[0070] The target area's forecasted operating data including line losses = the sum of the forecasted operating data including line losses in various cities. The target area's line loss cost = the target area's adjusted electricity sales / (1-the target area's line loss rate) * the target area's line loss rate * the average purchase price of electricity. The target area's target operating data excluding line losses = the target area's forecasted operating data including line losses - the target area's line loss cost.

[0071] This embodiment helps power grid companies to more accurately calculate their operating costs and actual benefits by calculating the target operating data excluding line losses in the target area. By excluding the line loss factor, the revenue directly generated by the transmission and distribution services can be clearly seen, thereby better understanding the profitability of the business. In the process of transmission and distribution price reform and electricity marketization, transmission and distribution operation data excluding line losses is an important basis for formulating transmission and distribution prices and adjusting electricity price structures. Based on these data, relevant institutions can more reasonably determine the price of transmission and distribution services to ensure that power grid companies can cover their operating costs and obtain reasonable profits while protecting the interests of consumers.

[0072] The predicted operating data predicted by this embodiment may deviate from the actual operating data. If the deviation is too large, the preset time series algorithm needs to be updated in time. Optionally, in the method for predicting the operating data of power transmission and distribution provided in the embodiment of the present application, after calculating the sum of the sub-operating data of all users to obtain the predicted operating data of the target area, the method also includes: obtaining the actual operating data of the target area, calculating the difference between the predicted operating data and the actual operating data, and calculating the ratio of the difference to the actual operating data to obtain the deviation rate; when the deviation rate is greater than or equal to the deviation rate threshold, updating the preset time series algorithm, and executing the step of predicting the power transmission and distribution indicators of each user through the preset time series algorithm and historical operating data based on the updated preset time series algorithm.

[0073] In some examples, in order to improve the accuracy of predicted operating data, it is also necessary to use the deviation between the predicted operating data and the actual operating data as an evaluation indicator to optimize the preset time series algorithm. The calculation formula for the deviation rate can be:

[0074] Deviation rate = (forecasted operating data - actual operating data) / actual operating data * 100%;

[0075] Among them, the smaller the value of the deviation rate is, the smaller the prediction error is and the higher the accuracy of the predicted operation data is.

[0076] This embodiment evaluates the predicted operation data by the deviation rate. When the deviation rate is greater than or equal to the deviation rate threshold, the preset time series algorithm is updated, and the step of predicting the power transmission and distribution index of each user by the preset time series algorithm and the historical operation data is performed based on the updated preset time series algorithm. The accuracy of the predicted operation data is guaranteed.

[0077] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0078] The embodiment of the present application also provides a prediction device for the operation data of power transmission and distribution. It should be noted that the prediction device for the operation data of power transmission and distribution in the embodiment of the present application can be used to execute the prediction method for the operation data of power transmission and distribution provided in the embodiment of the present application. The following introduces the prediction device for the operation data of power transmission and distribution provided in the embodiment of the present application.

[0079] Figure 2 Schematic diagram of a device for predicting power transmission and distribution operation data provided in accordance with an embodiment of the present application. Figure 2 As shown, the device comprises:

[0080] The acquisition unit 201 is used to acquire all users of power transmission and distribution in the target area to be predicted, and determine the user category of each user, wherein the user category includes at least one of the following: industrial users, industrial and commercial users, and agricultural users;

[0081] The prediction unit 202 is used to obtain the historical operation data of the target area, and predict the power transmission and distribution index of each user through a preset time series algorithm and the historical operation data, wherein the power transmission and distribution index includes at least one of the following: power sales, capacity, demand, power regulation increase, power regulation decrease, and power purchase and sales by proxy;

[0082] The calculation unit 203 is used to calculate the sub-operation data of each user through the power transmission and distribution index and the power transmission and distribution price, and calculate the sum of the sub-operation data of all users to obtain the predicted operation data of the target area.

[0083] The prediction device for the operation data of power transmission and distribution provided in the embodiment of the present application obtains all users of power transmission and distribution in the target area to be predicted through the acquisition unit 201, and determines the user category of each user, wherein the user category includes at least one of the following: industrial users, industrial and commercial users, and agricultural users; the prediction unit 202 obtains the historical operation data of the target area, and predicts the power transmission and distribution index of each user through a preset time series algorithm and the historical operation data, wherein the power transmission and distribution index includes at least one of the following: power sales, capacity, demand, power regulation increase, power regulation decrease, and power purchase and sales by proxy; the calculation unit 203 calculates the power transmission and distribution index and the power demand through the power transmission and distribution index. The transmission and distribution price calculates the sub-operation data of each user, calculates the sum of the sub-operation data of all users, and obtains the predicted operation data of the target area, which solves the problem of inaccurate prediction results of transmission and distribution operation data in related technologies. By obtaining and predicting the transmission and distribution indicators of all transmission and distribution users in the target area, the comprehensiveness and accuracy of the prediction results are ensured, and the revenue prediction needs of power grid companies under different user categories are met. The preset time series algorithm is used to analyze and predict historical operation data, accurately predict the future trend of user transmission and distribution indicators, and improve the prediction accuracy, thereby achieving the effect of improving the accuracy of predicted transmission and distribution operation data.

[0084] Optionally, in the prediction device for power transmission and distribution operation data provided in the embodiment of the present application, the prediction unit 202 includes: a first determination module, used to determine, for each power transmission and distribution indicator, first historical data of the target power transmission and distribution indicator of the target area from the historical operation data; a prediction module, used to predict the target data of the target area based on a preset time series algorithm and the first historical data, wherein the target data is the predicted data of the target power transmission and distribution indicator of the target area within the target period; a second determination module, used to determine the indicator proportion of the user in the historical operation data, wherein the indicator proportion is the ratio of the historical data of the user's target power transmission and distribution indicator to the first historical data; a first calculation module, used to calculate the product of the target data and the indicator proportion to obtain the predicted data of the user's target power transmission and distribution indicator.

[0085] Optionally, in the prediction device for the operation data of power transmission and distribution provided in the embodiment of the present application, the device also includes: a first determination unit, used to determine the expected electricity sales data of the target area, calculate the ratio of the expected electricity sales data to the target data, and obtain an adjustment coefficient; an adjustment unit, used to calculate the product of the user's predicted electricity sales data and the adjustment coefficient, and obtain the user's adjusted predicted electricity sales data.

[0086] Optionally, in the prediction device for power transmission and distribution operation data provided in the embodiment of the present application, the prediction module includes: a determination submodule, used to determine at least one time series algorithm, and determine the weight of each time series algorithm to obtain a set of weights; an input submodule, used to input the first historical data into each time series algorithm for calculation, to obtain prediction data of each time series algorithm; a summation submodule, used to perform weighted summation of the prediction data of each time series algorithm based on a set of weights to obtain target data.

[0087] Optionally, in the prediction device for the operation data of power transmission and distribution provided in the embodiment of the present application, the calculation unit 203 includes: a second calculation module, which is used to determine the power transmission and distribution unit price from the power transmission and distribution price when the power transmission and distribution indicator is the power sales volume, calculate the product of the power transmission and distribution unit price and the predicted power sales volume data, and obtain the kilowatt-hour electricity price data; a third determination module, which is used to determine the capacity electricity price and the demand electricity price from the power transmission and distribution price when the power transmission and distribution indicator is the capacity and demand, calculate the product of the capacity electricity price and the predicted capacity data, obtain the capacity electricity fee, calculate the product of the demand electricity price and the predicted demand data, obtain the demand electricity fee, calculate the sum of the capacity electricity fee and the demand electricity fee, and obtain the basic electricity fee; a fourth determination module, which is used to determine the first power regulation prediction data of the power regulation increase and the second power regulation prediction data of the power regulation decrease when the power transmission and distribution indicator is the power regulation increase and the power regulation decrease The power regulation prediction data is calculated, and the difference between the first power regulation prediction data and the second power regulation prediction data is calculated to obtain the power regulation electricity fee; the fifth determination module is used to determine the peak power consumption period prediction data and the valley power consumption period prediction data of the proxy power purchase and sales when the power transmission and distribution indicator is the proxy power purchase and sales volume, determine the peak electricity price, valley electricity price and flat electricity price from the power transmission and distribution price, calculate the difference between the peak electricity price and the flat electricity price, and calculate the product of the difference and the peak power consumption period prediction data to obtain the first profit and loss, calculate the difference between the valley electricity price and the flat electricity price, and calculate the product of the difference and the valley power consumption period prediction data to obtain the second profit and loss, calculate the sum of the first profit and loss and the second profit and loss to obtain the peak-valley profit and loss; the sixth determination module is used to determine the accumulated value of at least one of the following as the user's sub-operation data: electricity price data, basic electricity fee, power regulation electricity fee and peak-valley profit and loss.

[0088] Optionally, in the prediction device for the operation data of power transmission and distribution provided in the embodiment of the present application, the device also includes: a second determination unit, used to determine the line loss rate and the average purchase price of electricity in the target area, wherein the line loss rate is the ratio of the line transmission loss electricity in the power transmission and distribution process to the electricity sales volume; a line loss cost calculation unit, used to calculate the ratio of the electricity sales volume to the transmission efficiency, and calculate the product of the ratio, the line loss rate and the average purchase price of electricity to obtain the line loss cost, wherein the transmission efficiency is the difference between the preset value and the line loss rate; an operation data calculation unit, used to calculate the difference between the predicted operation data and the line loss cost, to obtain the target operation data of the target area excluding line loss.

[0089] Optionally, in the prediction device for power transmission and distribution operation data provided in the embodiment of the present application, the device also includes: a deviation rate calculation unit, used to obtain actual operation data of the target area, calculate the difference between the predicted operation data and the actual operation data, and calculate the ratio of the difference to the actual operation data to obtain the deviation rate; an updating unit, used to update the preset time series algorithm when the deviation rate is greater than or equal to the deviation rate threshold, and execute the step of predicting the power transmission and distribution indicators of each user through the preset time series algorithm and historical operation data based on the updated preset time series algorithm.

[0090] The prediction device for the operation data of power transmission and distribution includes a processor and a memory. The acquisition unit 201, prediction unit 202 and calculation unit 203 are all stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.

[0091] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and the accuracy of the operation data of the power transmission and distribution forecast can be improved by adjusting the kernel parameters.

[0092] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0093] An embodiment of the present invention provides a computer-readable storage medium having a program stored thereon, which implements a method for predicting operational data of power transmission and distribution when the program is executed by a processor.

[0094] An embodiment of the present invention provides a processor, which is used to run a program, wherein the program executes a method for predicting operation data of power transmission and distribution when running.

[0095] Figure 3 is a schematic diagram of an electronic device provided according to an embodiment of the present application. Figure 3 As shown, the electronic device 301 includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the following steps are implemented: obtaining all users of power transmission and distribution in the target area to be predicted, and determining the user category of each user, wherein the user category includes at least one of the following: industrial users, industrial and commercial users, and agricultural users; obtaining historical operation data of the target area, and predicting the power transmission and distribution index of each user through a preset time series algorithm and historical operation data, wherein the power transmission and distribution index includes at least one of the following: power sales, capacity, demand, power regulation increase, power regulation decrease, and power purchase and sales by proxy; calculating the sub-operation data of each user through the power transmission and distribution index and the power transmission and distribution price, calculating the sum of the sub-operation data of all users, and obtaining the predicted operation data of the target area. The device in this article can be a server, PC, PAD, mobile phone, etc.

[0096] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that initializes the following method steps: obtaining all users of power transmission and distribution in the target area to be predicted, and determining the user category of each user, wherein the user category includes at least one of the following: industrial users, industrial and commercial users, and agricultural users; obtaining historical operating data of the target area, and predicting the power transmission and distribution indicators of each user through a preset time series algorithm and historical operating data, wherein the power transmission and distribution indicators include at least one of the following: power sales, capacity, demand, power regulation increase, power regulation decrease, and agency power purchase and sales; calculating the sub-operation data of each user through the power transmission and distribution indicators and the power transmission and distribution price, and calculating the sum of the sub-operation data of all users to obtain the predicted operating data of the target area.

[0097] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0098] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0099] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0100] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0101] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0102] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0103] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0104] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0105] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0106] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for predicting operational data of power transmission and distribution, characterized in that: include: Acquire all users of power transmission and distribution in the target area to be predicted, and determine the user category of each user, wherein the user category includes at least one of the following: industrial users, industrial and commercial users, and agricultural users; Obtaining historical operation data of the target area, and predicting the power transmission and distribution index of each user by using a preset time series algorithm and the historical operation data, wherein the power transmission and distribution index includes at least one of the following: power sales, capacity, demand, power regulation increase, power regulation decrease, and power purchase and sales by proxy; The sub-operation data of each user is calculated by using the power transmission and distribution index and the power transmission and distribution price, and the sum of the sub-operation data of all users is calculated to obtain the predicted operation data of the target area.

2. The method according to claim 1, characterized in that The transmission and distribution indicators of each user predicted by the preset time series algorithm and the historical operation data include: For each power transmission and distribution index, determining first historical data of the target power transmission and distribution index of the target area from the historical operation data; Predicting target data of the target area based on the preset time series algorithm and the first historical data, wherein the target data is predicted data of target power transmission and distribution indicators of the target area within a target period; Determine the indicator proportion of the user in the historical operation data, wherein the indicator proportion is the ratio of the historical data of the target power transmission and distribution indicator of the user to the first historical data; The product of the target data and the indicator ratio is calculated to obtain the forecast data of the target power transmission and distribution indicator of the user.

3. The method according to claim 2, characterized in that In the case where the target power transmission and distribution index is the power sales volume, after obtaining the forecast data of the target power transmission and distribution index of the user, the method further includes: Determine the expected electricity sales data of the target area, calculate the ratio of the expected electricity sales data to the target data, and obtain an adjustment coefficient; The product of the predicted data of the power sales amount of the user and the adjustment coefficient is calculated to obtain the adjusted predicted power sales amount data of the user.

4. The method according to claim 2, characterized in that: Predicting the target data of the target area based on the preset time series algorithm and the first historical data includes: Determine at least one time series algorithm and determine the weight of each time series algorithm to obtain a set of weights; Inputting the first historical data into each time series algorithm for calculation to obtain forecast data of each time series algorithm; The predicted data of each time series algorithm is weightedly summed based on the set of weights to obtain the target data.

5. The method according to claim 1, characterized in that The sub-operation data of each user calculated by the transmission and distribution index and the transmission and distribution price include: In the case where the power transmission and distribution indicator is the power sales volume, determining the power transmission and distribution unit price from the power transmission and distribution price, calculating the product of the power transmission and distribution unit price and the predicted power sales volume data, and obtaining the kilowatt-hour power price data; In the case where the power transmission and distribution index is the capacity and the demand, determining the capacity electricity price and the demand electricity price from the power transmission and distribution price, calculating the product of the capacity electricity price and the predicted capacity data to obtain the capacity electricity fee, calculating the product of the demand electricity price and the predicted demand data to obtain the demand electricity fee, and calculating the sum of the capacity electricity fee and the demand electricity fee to obtain the basic electricity fee; In the case where the power transmission and distribution index is the power regulation increase amount and the power regulation decrease amount, determining first power regulation prediction data of the power regulation increase amount and second power regulation prediction data of the power regulation decrease amount, calculating the difference between the first power regulation prediction data and the second power regulation prediction data, and obtaining the power regulation electricity fee; In the case where the power transmission and distribution indicator is the power purchase and sale amount on behalf of the power purchase and sale amount, the power consumption peak time period forecast data and the power consumption valley time period forecast data of the power purchase and sale amount on behalf of the power purchase and sale amount are determined, the peak power price, the valley power price and the flat power price are determined from the power transmission and distribution price, the difference between the peak power price and the flat power price is calculated, and the product of the difference and the power consumption peak time period forecast data is calculated to obtain a first profit and loss, the difference between the valley power price and the flat power price is calculated, and the product of the difference and the power consumption valley time period forecast data is calculated to obtain a second profit and loss, and the sum of the first profit and loss and the second profit and loss is calculated to obtain a peak-valley profit and loss; The accumulated value of at least one of the following is determined as the sub-operation data of the user: the electricity price data, the basic electricity fee, the power regulation electricity fee and the peak-valley profit and loss.

6. The method according to claim 1, characterized in that After calculating the sum of the sub-operation data of all users to obtain the predicted operation data of the target area, the method further includes: Determine the line loss rate and average power purchase price of the target area, wherein the line loss rate is the ratio of the line transmission loss power in the power transmission and distribution process to the power sales; Calculating the ratio of the power sales amount to the transmission efficiency, and calculating the product of the ratio, the line loss rate and the average power purchase price to obtain the line loss cost, wherein the transmission efficiency is the difference between a preset value and the line loss rate; The difference between the predicted operating data and the line loss cost is calculated to obtain the target operating data of the target area excluding the line loss.

7. The method according to claim 1, characterized in that After calculating the sum of the sub-operation data of all users to obtain the predicted operation data of the target area, the method further includes: Acquire actual operation data of the target area, calculate the difference between the predicted operation data and the actual operation data, and calculate the ratio of the difference to the actual operation data to obtain a deviation rate; When the deviation rate is greater than or equal to the deviation rate threshold, the preset time series algorithm is updated, and based on the updated preset time series algorithm, the step of predicting the power transmission and distribution index of each user through the preset time series algorithm and the historical operation data is performed.

8. A device for predicting operational data of power transmission and distribution, characterized in that: include: an acquisition unit, used to acquire all users of power transmission and distribution in the target area to be predicted, and determine a user category of each user, wherein the user category includes at least one of the following: industrial users, industrial and commercial users, and agricultural users; A prediction unit, configured to obtain the historical operation data of the target area, and predict the power transmission and distribution index of each user by using a preset time series algorithm and the historical operation data, wherein the power transmission and distribution index includes at least one of the following: power sales, capacity, demand, power regulation increase, power regulation decrease, and power purchase and sales by proxy; The calculation unit is used to calculate the sub-operation data of each user through the power transmission and distribution index and the power transmission and distribution price, calculate the sum of the sub-operation data of all users, and obtain the predicted operation data of the target area.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for predicting the operation data of power transmission and distribution as claimed in any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: It comprises one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method for predicting operational data of power transmission and distribution as described in any one of claims 1 to 7.

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