Inter-provincial medium and long term transaction electric quantity prediction method and device, equipment and storage medium

Abstract

The invention relates to an inter-provincial medium and long term transaction electric quantity prediction method and device, equipment and a storage medium. The method comprises the following steps: acquiring equipment ledger data of power transmission equipment and meteorological data of a power generation system in a historical preset time period; obtaining a target sunshine day number according to the meteorological data and a trained sunshine day number prediction model; evaluating the aging grade of the power transmission equipment according to the equipment ledger data; determining power transmission quantity prediction data according to the target sunshine day number, the preset single-day sunshine power transmission quantity and the preset single-day non-sunshine power transmission quantity; matching a target power transmission quantity optimization strategy from a plurality of preset power transmission quantity optimization strategies according to the aging grade of the power transmission equipment and a preset aging grade range; and according to the target power transmission quantity optimization strategy, determining a lost power transmission quantity in the power transmission quantity prediction data, determining a difference value between the power transmission quantity prediction data and the lost power transmission quantity, and obtaining transaction power quantity prediction data in a future preset time period. By adopting the method, the transaction electric quantity prediction precision can be improved.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for predicting inter-provincial medium- and long-term electricity transactions. Background Technology

[0002] Electricity trading forecasts can be used to rationally arrange power supply and demand, improve energy efficiency, ensure energy security and sustainable economic development, guide the operation and maintenance of power equipment, promote market competition and efficiency improvement, help users rationally arrange their electricity consumption plans, and ensure the revenue of new energy power plants.

[0003] However, existing electricity trading forecasts are inaccurate, and inaccurate forecasts can affect electricity supply and demand forecasts. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for predicting inter-provincial medium- and long-term trading volumes that can improve the accuracy of trading volume prediction, in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a method for predicting electricity volume in inter-provincial medium- and long-term transactions, including:

[0006] Obtain equipment ledger data for power transmission equipment and meteorological data for historical preset time periods of the power generation system;

[0007] Based on meteorological data and a trained sunshine days prediction model, the number of sunshine days in a future preset time period is predicted to obtain the target number of sunshine days.

[0008] Assess the aging level of the power transmission equipment based on equipment ledger data;

[0009] Based on the target number of sunshine days, the preset daily sunshine transmission capacity, and the preset daily non-sunshine transmission capacity, determine the predicted transmission capacity data for the preset time period in the future;

[0010] Based on the aging level of the power transmission equipment and the preset aging level range, the target power transmission optimization strategy is matched from multiple preset power transmission optimization strategies.

[0011] Based on the target transmission volume optimization strategy, the lost transmission volume in the transmission volume forecast data is determined, and the difference between the transmission volume forecast data and the lost transmission volume is determined to obtain the transaction volume forecast data for the future preset time period.

[0012] Secondly, this application also provides a device for predicting inter-provincial medium- and long-term electricity transactions, comprising:

[0013] The data acquisition module is used to acquire equipment ledger data of power transmission equipment and meteorological data of the power generation system for a preset historical time period;

[0014] The sunshine days prediction module is used to predict the number of sunshine days in a future preset time period based on meteorological data and a trained sunshine days prediction model, and obtain the target number of sunshine days.

[0015] The aging level assessment module is used to assess the aging level of power transmission equipment based on equipment ledger data.

[0016] The power transmission forecasting module is used to determine the power transmission forecast data for a future preset time period based on the target number of sunshine days, the preset daily sunshine power transmission, and the preset daily non-sunshine power transmission.

[0017] The power transmission optimization strategy determination module is used to match the target power transmission optimization strategy from multiple preset power transmission optimization strategies based on the aging level of the power transmission equipment and the preset aging level range.

[0018] The transaction power prediction module is used to determine the lost transmission power in the transmission power prediction data according to the target transmission power optimization strategy, determine the difference between the transmission power prediction data and the lost transmission power, and obtain the transaction power prediction data for a future preset time period.

[0019] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in any of the above embodiments of the inter-provincial medium- and long-term electricity trading forecasting method.

[0020] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps in any of the above embodiments of the inter-provincial medium- and long-term electricity trading prediction method.

[0021] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the inter-provincial medium- and long-term electricity trading prediction method.

[0022] The aforementioned inter-provincial medium- and long-term electricity trading forecasting method, device, computer equipment, computer-readable storage medium, and computer program product first acquire equipment ledger data of the transmission equipment and meteorological data of the power generation system for a historical preset time period. Second, based on the meteorological data and a trained sunshine days prediction model, the number of sunshine days within the future preset time period is predicted. Based on the predicted number of sunshine days, the preset daily sunshine transmission volume, and the preset daily non-sunshine transmission volume, the predicted transmission volume data for the future preset time period is determined. Thus, preliminary electricity trading forecast data is obtained by predicting the number of sunshine days. Third, the aging level of the transmission equipment is assessed based on the equipment ledger data, and based on the transmission equipment… The system determines the aging level and preset aging level range, and matches the target transmission power optimization strategy from multiple preset transmission power optimization strategies to determine the lost transmission power in the preliminary trading power forecast data. This yields the trading power forecast data for the future preset time period. By considering the impact of equipment aging on the transmission process, the lost transmission power caused by the impact is subtracted from the transmission power forecast data, improving the accuracy of trading power forecast. This facilitates the rational arrangement of power production and demand based on accurate trading power forecast data. Furthermore, the power sector can better arrange power generation plans and operations, avoid grid overload or insufficient power supply, and achieve supply and demand balance. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a diagram illustrating the application environment of an inter-provincial medium- and long-term electricity trading forecasting method in one embodiment.

[0025] Figure 2 This is a flowchart illustrating a method for predicting inter-provincial medium- and long-term electricity transactions in one embodiment.

[0026] Figure 3 This is a flowchart illustrating the inter-provincial medium- and long-term electricity trading forecasting method in another embodiment;

[0027] Figure 4 This is a flowchart illustrating the inter-provincial medium- and long-term electricity trading forecasting method in yet another embodiment.

[0028] Figure 5 This is a flowchart illustrating the inter-provincial medium- and long-term electricity trading forecasting method in another embodiment;

[0029] Figure 6This is a flowchart illustrating the inter-provincial medium- and long-term electricity trading forecasting method in another embodiment;

[0030] Figure 7 This is a flowchart illustrating the inter-provincial medium- and long-term electricity trading forecasting method in another embodiment;

[0031] Figure 8 This is a structural block diagram of an inter-provincial medium- and long-term electricity trading forecasting device in one embodiment;

[0032] Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0034] The inter-provincial medium- and long-term electricity trading forecasting method provided in this application can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104, or it can be located in the cloud or on another network server.

[0035] Specifically, the operator can upload the collected equipment ledger data of the power transmission equipment and the historical meteorological data of the power generation system for a preset time period to the server 104 via terminal 102. Then, the operator can send a transaction power prediction message to the server 104 via terminal 102. The server 104 obtains the equipment ledger data of the power transmission equipment and the historical meteorological data of the power generation system for a preset time period. Next, based on the meteorological data and a trained sunshine days prediction model, it predicts the number of sunshine days in the future preset time period to obtain the target number of sunshine days. Based on the equipment ledger data, it assesses the aging level of the power transmission equipment. Then, based on the target number of sunshine days, the preset daily sunshine transmission volume, and the preset daily non-sunshine transmission volume, it determines the transmission power prediction data for the future preset time period. Based on the aging level of the power transmission equipment and the preset aging level range, it matches the target transmission power optimization strategy from multiple preset transmission power optimization strategies. Finally, based on the target transmission power optimization strategy, it determines the lost transmission volume in the transmission power prediction data and determines the difference between the transmission power prediction data and the lost transmission volume to obtain the transaction power prediction data for the future preset time period.

[0036] The terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle systems, and projection devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted displays. Head-mounted displays can be virtual reality (VR) devices, augmented reality (AR) devices, and smart glasses. The server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0037] In one exemplary embodiment, such as Figure 2 As shown, a method for predicting inter-provincial medium- and long-term electricity transactions is provided, and this method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps (hereinafter referred to as S): S100 to S600. Wherein:

[0038] S100: Obtain equipment ledger data for power transmission equipment and meteorological data for historical preset time periods of the power generation system.

[0039] The meteorological data includes, but is not limited to, sunlight intensity, temperature, humidity, cloud cover, air pressure, and wind speed. The historical preset time period can be determined by the time span of the predicted number of sunny days, such as a quarter or a year. In this embodiment, the power generation system can be a photovoltaic power generation system.

[0040] The equipment ledger data for power transmission equipment includes, but is not limited to, equipment identification, location, technical parameters, and historical maintenance data. For example, the technical parameters of a power transmission line may include voltage level, model specifications, and rated current. Historical maintenance data may include maintenance records, defect types, and failure times.

[0041] In practice, operators can determine a preset time period (quarterly and / or annual) based on the time span requirements of the electricity trading forecast. Operators then obtain data on sunlight intensity, temperature, humidity, cloud cover, air pressure, and wind speed for the power generation system's location within the historical preset time period from publicly available meteorological data sources. Based on the temporal characteristics of the historical preset time period, operators generate temporal characteristics for the meteorological data (such as quarterly, monthly, and whether it is a leap year). The meteorological data also includes temporal characteristics, and this meteorological data is uploaded to the server. Subsequently, the operator obtains equipment ledger data for the transmission equipment through the power system's asset management system and uploads the equipment ledger data to the server.

[0042] S200 predicts the number of sunshine days within a preset time period based on meteorological data and a trained sunshine days prediction model, thus obtaining the target number of sunshine days.

[0043] Here, the number of sunshine days represents the number of days within a preset future time period where the daily sunshine intensity is greater than or equal to a preset sunshine intensity threshold. For example, the preset sunshine intensity threshold can be 120 watts / m².

[0044] The sunshine days prediction model can include a quarterly sunshine days prediction model and an annual sunshine days prediction model. For example, the training method for the quarterly sunshine days prediction model can include: pre-acquiring historical meteorological data and temporal characteristic data for the region where the power generation system is located within a historical time period. The historical meteorological data includes information such as the quarter, month, and whether it is a leap year. Then, the acquired historical meteorological data undergoes data preprocessing, including but not limited to at least one of duplicate data removal, outlier data removal, and missing value imputation. The historical meteorological data is labeled according to light intensity, marking the number of sunshine days in each historical quarter. The labeled historical meteorological data is input into the constructed initial quarterly sunshine days prediction model, and the initial quarterly sunshine days prediction model is iteratively trained until the model's prediction error is less than a preset error threshold, resulting in the trained quarterly sunshine days prediction model. The initial quarterly sunshine days prediction model can be constructed based on models such as logistic regression or random forest. The training process for the annual sunshine days prediction model is the same as that for the quarterly sunshine days prediction model and will not be repeated here.

[0045] In practice, to predict the number of sunshine days in a future quarter, historical meteorological data and temporal characteristic data for that quarter are input into a trained quarterly sunshine days prediction model. This model then predicts the number of sunshine days for the future quarter by capturing meteorological trends. Similarly, to predict the number of sunshine days for a future year, historical meteorological data and temporal characteristic data for that year are input into a trained annual sunshine days prediction model. This model then predicts the number of sunshine days for the future year by capturing meteorological trends.

[0046] S300 assesses the aging level of power transmission equipment based on equipment ledger data.

[0047] Among them, the aging level represents the degree of aging of the power transmission equipment. It can be understood that the higher the aging level of the equipment, the more serious the aging of the equipment.

[0048] In practice, the aging level of power transmission equipment can be assessed from two aspects: operating time and historical maintenance data. Specifically, based on the commissioning year in the equipment's technical parameters from the equipment ledger data, the operating time of the power transmission equipment is determined as: Current Time - Commissioning Time (in months). The lifespan score is then determined as: (1 - Operating Time / Design Lifespan of the Power Transmission Equipment) × 100%. Subsequently, the number of defects, failures, and maintenance methods over the past three years are statistically analyzed from the historical maintenance data. Based on the number of defects, failures, and maintenance methods, and a preset defect scoring standard, a defect score is determined. Finally, a comprehensive score is obtained by weighted summation of the preset lifespan score weight, preset defect score weight, lifespan score, and defect score. The aging level of the power transmission equipment is then determined based on the comprehensive score range corresponding to each of the preset aging levels. The defect score is determined based on the number of defects, the number of failures, the repair method, and a preset defect scoring standard. Specifically, for the number of defects, a defect number score is determined based on the number of defects and a preset defect number scoring standard. For example, if the number of defects is greater than or equal to 3, the defect number score is 100 points; if the number of defects is 0, the defect number score is 10 points; and vice versa, the defect number score is 30 points. For the number of failures, a failure number score is determined based on the number of defects and a preset failure number scoring standard, which is referenced from the defect number score standard. For the repair method, if a large number of parts are replaced, the repair method score is 100 points; if a small number of parts are replaced, the repair method score is 30 points; and if no repair is performed, the repair method score is 10 points. Therefore, the defect score can be obtained by weighted summation of preset defect number score weights, preset failure number score weights, preset repair method score weights, the defect number score, the failure number score, and the repair method score.

[0049] S400 determines the power transmission forecast data for a future preset time period based on the target number of sunshine days, the preset daily sunshine transmission capacity, and the preset daily non-sunshine transmission capacity.

[0050] Among them, daily solar power transmission capacity represents the power transmission capacity of the transmission equipment on a day when the solar radiation amplitude is greater than or equal to a preset solar radiation amplitude threshold. Conversely, daily non-solar power transmission capacity represents the power transmission capacity of the transmission equipment on a day when the solar radiation amplitude is less than a preset solar radiation amplitude threshold.

[0051] In practical applications, daily solar power transmission capacity and daily non-solar power transmission capacity are preset. Specifically, this can be achieved by obtaining the daily power generation capacity of the power generation equipment when the solar irradiance is greater than or equal to a preset solar irradiance threshold from historical power generation data, and setting the daily solar power transmission capacity based on this capacity (ignoring losses between power generation and transmission). Conversely, the daily non-solar power transmission capacity can be set by obtaining the daily power generation capacity of the power generation equipment when the solar irradiance is less than a preset solar irradiance threshold from historical power generation data.

[0052] In practice, the number of non-sunshine days within a preset future time period is first determined based on the target number of sunshine days. For example, the number of non-sunshine days = total number of days in the quarter - number of sunshine days in the quarter. The power transmission forecast data for the preset future time period can be: a power transmission forecast for a future quarter or a future year, where the power transmission forecast data = target number of sunshine days * preset daily sunshine power transmission + non-sunshine days * daily non-sunshine power transmission.

[0053] S500 selects the target power transmission optimization strategy from multiple preset power transmission optimization strategies based on the aging level of the power transmission equipment and the preset aging level range.

[0054] The preset aging level range represents the range of aging levels where the equipment is less aged. For example, if the aging level is divided into 6 levels: L1 to L6, and the preset aging level range can be L1 to L3, then L4 to L6 represent the equipment with a more severe aging.

[0055] In this embodiment, considering that the power transmission capacity fluctuates due to various factors during the power transmission process of the power transmission equipment, and thus the trading power volume fluctuates, power transmission capacity optimization strategies that take into account different influencing factors are set in advance to make the power transmission capacity prediction data close to the actual power transmission capacity.

[0056] For example, when the aging of power transmission equipment is severe, it needs to be replaced or maintained, and the transmission speed and the distance of the transmission line will affect the transmission capacity. When the aging of power transmission equipment is mild, periodic maintenance is still required, and the maintenance cycle and maintenance speed will affect the transmission capacity.

[0057] In practical applications, multiple power transmission optimization strategies are pre-set based on different influencing factors. These power transmission optimization strategies include a first, second, third, fourth, and fifth strategy. For example, the first power transmission optimization strategy considers the impact of equipment maintenance cycles on power transmission. Specifically, the first power transmission optimization strategy may include: determining the maintenance cycle of the transmission equipment within a preset future time period based on the aging level of the transmission equipment; determining the lost power transmission based on the maintenance cycle; and obtaining the predicted trading power based on the difference between the predicted power transmission data and the lost power transmission, where the lost power transmission represents the lost power transmission.

[0058] For example, considering the impact of equipment maintenance speed on transmission capacity, the second transmission capacity optimization strategy may include: determining the lost transmission capacity based on the maintenance speed of the transmission equipment, and obtaining the transaction capacity prediction data based on the difference between the transmission capacity prediction data and the lost transmission capacity.

[0059] For example, considering that some transmission equipment is replaced instead of repaired during the maintenance of transmission equipment, and that the number of maintenance personnel may change, thus changing the maintenance speed, the third transmission capacity optimization strategy may include: determining the maintenance speed of the transmission equipment and the loss of transmission capacity corresponding to the maintenance speed according to the number of transmission equipment to be maintained, and obtaining the transaction power prediction data based on the difference between the transmission capacity prediction data and the loss of transmission capacity.

[0060] For example, considering that the maintenance route distance of the transmission equipment line affects the maintenance speed, the fourth power transmission optimization strategy may include: determining the maintenance speed based on the maintenance route distance, determining the power loss corresponding to the maintenance speed, and obtaining the power trading prediction data based on the difference between the power transmission prediction data and the power loss.

[0061] For example, considering that the maintenance method affects the maintenance speed, the fifth power transmission optimization strategy may include: determining the maintenance speed based on the maintenance method and the distance of the maintenance line, determining the power loss corresponding to the maintenance speed, and obtaining the power trading prediction data based on the difference between the power transmission prediction data and the power loss.

[0062] In practical applications, candidate transmission capacity optimization strategies that match the aging level of the transmission equipment can be pre-set. For example, continuing the previous example, suppose the aging levels are divided into six levels: L1, L2, ..., L6, with a preset aging level range of L1 to L3. Let the first, second, third, fourth, and fifth transmission capacity optimization strategies be S1, S2, ..., S5, respectively. Then, the target transmission capacity optimization strategy can include at least one of these five strategies. For example, if the aging level of the transmission equipment is within the preset aging level range, the target transmission capacity optimization strategy can be: S1, S2, or (S1-S2). Here, (S1-S2) represents a sequentially superimposed optimization method, that is, first, the transmission capacity prediction data is optimized using the S1 optimization strategy to obtain the transaction volume prediction data, and the transmission capacity prediction data in the S2 optimization strategy is the transaction volume prediction data obtained after the S1 optimization. If the aging level of the power transmission equipment exceeds the preset aging level range, the target power transmission capacity optimization strategy is (S1-S2-S3), (S1-S2-S3-S4), or (S1-S2-S3-S4-S5).

[0063] S600 determines the lost transmission volume in the transmission volume prediction data based on the target transmission volume optimization strategy, and obtains the transaction volume prediction data for the future preset time period based on the difference between the transmission volume prediction data and the lost transmission volume.

[0064] In specific implementation, for long-term forecasting, the server can use a target transmission capacity optimization strategy to subtract the lost transmission capacity from the transmission capacity forecast data for the next year to obtain the transaction volume forecast data for the next year. For short-term forecasting, the server can use a target transmission capacity optimization strategy to subtract the lost transmission capacity from the transmission capacity forecast data for the next quarter to obtain the transaction volume forecast data for the next quarter. The implementation steps for subtracting the lost transmission capacity from the transmission capacity forecast data using the target transmission capacity optimization strategy refer to the steps described in the first, second, third, fourth, and fifth transmission capacity optimization strategies above, determining the lost transmission capacity, obtaining the transaction volume forecast data based on the difference between the transmission capacity forecast data and the lost transmission capacity, and sequentially superimposing and optimizing the transmission capacity forecast data according to multiple transmission capacity optimization strategies in the target transmission capacity optimization strategy. These steps will not be elaborated further here.

[0065] The aforementioned method for forecasting inter-provincial medium- and long-term electricity transactions involves three steps: First, acquiring equipment ledger data for transmission equipment and historical meteorological data for a preset time period from the power generation system. Second, based on the meteorological data and a trained sunshine days prediction model, forecasting the number of sunshine days within a future preset time period. Then, determining the predicted transmission volume for the future preset time period based on the predicted sunshine days, preset daily sunshine transmission volume, and preset daily non-sunshine transmission volume. Thus, preliminary electricity transaction forecasting data is obtained by predicting the number of sunshine days. Third, assessing the aging level of the transmission equipment based on the equipment ledger data, and then determining the predicted transmission volume based on the aging level of the transmission equipment and a preset aging level. The system matches the target transmission power optimization strategy from multiple preset transmission power optimization strategies to determine the lost transmission power in the preliminary trading power forecast data, thus obtaining the trading power forecast data for the future preset time period. In this way, considering the impact of equipment aging on the transmission process, the lost transmission power caused by the impact is subtracted from the transmission power forecast data, improving the accuracy of trading power forecast. This is conducive to rationally arranging power production and demand based on accurate trading power forecast data. Furthermore, the power sector can better arrange power generation plans and operations, avoid grid overload or power supply shortages, and achieve supply and demand balance.

[0066] When conducting electricity trading, regular maintenance of transmission lines is required. However, the power grid needs to shut down during maintenance, resulting in a loss of transmission capacity. The more aged the equipment, the longer the maintenance cycle, leading to greater transmission capacity loss and a decrease in traded electricity volume. Therefore, when forecasting traded electricity volume, the transmission capacity loss due to equipment maintenance cycles must be considered. In an exemplary embodiment, such as... Figure 3 As shown, based on the target transmission capacity optimization strategy, the lost transmission capacity in the transmission capacity forecast data is determined, and the difference between the transmission capacity forecast data and the lost transmission capacity is determined to obtain the transaction volume forecast data for a future preset time period, including S612 to S616. Wherein:

[0067] S612, determine the target maintenance cycle of the power transmission equipment based on the aging level of the equipment and the preset maintenance cycle.

[0068] The preset maintenance cycle represents the regular maintenance cycle of the power transmission equipment, which can be set by the historical maintenance cycle of the power transmission equipment in a historical time period.

[0069] In practice, this can be achieved by determining the average number of aging levels for each power transmission device, then determining the ratio of this average to the total number of aging levels, and finally calculating the product of this ratio and a preset maintenance cycle to obtain the target maintenance cycle for the power transmission device. That is:

[0070]

[0071] in, For the target maintenance cycle, The preset maintenance cycle, The total number of aging levels for power transmission equipment is exemplified by aging level classifications including L1 to L6, with a total of 6 levels. This represents the average of the aging levels of the power transmission equipment.

[0072] S614, determine the product of the maintenance cycle and the preset unit cycle loss transmission capacity to obtain the first loss transmission capacity.

[0073] The preset unit cycle loss transmission capacity represents the loss of transmission capacity of the transmission equipment within a preset single maintenance cycle. The first loss transmission capacity represents the transmission capacity lost due to the maintenance cycle.

[0074] For example, suppose The first loss is the amount of transmitted power. Given the preset unit cycle loss transmission capacity, the first loss transmission capacity is obtained by the following formula:

[0075]

[0076] S616, determine the difference between the predicted transmission volume and the first lost transmission volume to obtain the predicted first trading volume, and determine the predicted first trading volume as the predicted trading volume for a future preset time period.

[0077] For example, suppose This is the predicted electricity volume for the first transaction. Given the predicted transmission volume data, the predicted volume data for the first transaction is obtained using the following formula:

[0078]

[0079] In this embodiment, the accuracy of the transaction power prediction is improved by subtracting the power loss due to equipment maintenance from the power transmission prediction data.

[0080] In other embodiments, after obtaining the first transaction electricity prediction data, the method further includes updating the transmission electricity prediction data to the first transaction electricity prediction data, i.e., K= This is to further optimize the power transmission loss caused by other influencing factors and improve the accuracy of power trading forecasts.

[0081] When maintaining power transmission equipment, the more maintenance personnel present, the faster the maintenance can be performed; conversely, the fewer personnel, the slower the maintenance. This allows for optimization of power transmission prediction data based on maintenance speed. In an exemplary embodiment, such as... Figure 4As shown, based on the target transmission capacity optimization strategy, the lost transmission capacity in the transmission capacity forecast data is determined, the difference between the transmission capacity forecast data and the lost transmission capacity is determined, and the transaction volume forecast data for the future preset time period is obtained, including steps S622 to S628:

[0082] S622, Get the number of maintenance personnel.

[0083] In practice, the operator can determine the number of maintenance personnel based on the number of power transmission equipment and upload the number of maintenance personnel to the server.

[0084] S624 determines the product of the number of maintenance personnel and the preset unit maintenance speed level to obtain the first maintenance speed level of the power transmission equipment.

[0085] The preset unit maintenance speed level represents the maintenance speed level of a single maintenance worker. The unit maintenance speed level can be set based on the maintenance speed levels of multiple maintenance workers. For example, the maintenance speed level of each maintenance worker is pre-assessed based on the ratio of the actual maintenance operation time to the planned maintenance operation time of multiple maintenance workers, and the unit maintenance speed level is set based on the average maintenance speed level of all maintenance workers.

[0086] In specific implementation, set The first maintenance speed level, To maintain the number of personnel, If the preset unit maintenance speed level is used, then the first maintenance speed level of the transmission equipment is... We obtain it from the following formula:

[0087]

[0088] S626, determine the second loss transmission capacity based on the preset unit maintenance speed level, the first maintenance speed level, and the preset unit level loss transmission capacity.

[0089] The preset unit-level loss transmission capacity represents the transmission capacity loss when the maintenance speed level of the transmission equipment is Level 1. The unit-level loss transmission capacity can be set based on experiments. The second loss transmission capacity represents the transmission capacity loss due to the impact of the maintenance speed of the transmission equipment.

[0090] In specific implementation, set For the second loss of transmission capacity, The preset unit level loss transmission capacity. The second loss transmission capacity is obtained by the following formula:

[0091]

[0092] S628, determine the difference between the first transaction power prediction data and the second loss transmission power to obtain the second transaction power prediction data, and determine the second transaction power prediction data as the transaction power prediction data for a future preset time period.

[0093] In practice, the second transaction electricity prediction data The second power transmission forecast data is determined as the power trading forecast data for a future preset time period.

[0094] In this embodiment, considering the impact of maintenance speed on transmission capacity during the maintenance of transmission equipment, the lost transmission capacity is subtracted from the transmission capacity prediction data, which improves the accuracy of the trading capacity prediction and further reduces the possibility of deviations in power generation output and power demand prediction due to inaccurate trading capacity prediction data.

[0095] In other embodiments, after obtaining the second transaction electricity prediction data, the method further includes updating the transmission electricity prediction data to the second transaction electricity prediction data, i.e., K= This is to further optimize the power transmission loss caused by other influencing factors and improve the accuracy of power trading forecasts.

[0096] For transmission equipment with a high aging level, replacement is necessary, reducing the number of maintenance personnel. This reduction in personnel also slows down the maintenance of other transmission equipment. Therefore, the lost transmission capacity due to the replacement of the high-aging equipment must be considered. In an exemplary embodiment, such as... Figure 5 As shown, when there are target transmission equipment in the transmission equipment whose aging level exceeds the preset aging level range, the lost transmission volume in the transmission volume prediction data is determined according to the target transmission volume optimization strategy. The difference between the transmission volume prediction data and the lost transmission volume is determined to obtain the transaction volume prediction data for the future preset time period. This also includes steps S632 to S638, wherein:

[0097] S632, obtain the number of target power transmission equipment.

[0098] Among them, the target power transmission equipment is the power transmission equipment whose aging level exceeds the preset aging level range.

[0099] In practice, the server matches the aging level of each power transmission device with the preset aging level range and counts the number of target power transmission devices whose aging level exceeds the preset aging level range.

[0100] S634, determine the difference between the number of maintenance personnel and the number of target transmission equipment, determine the product of the difference and the preset unit maintenance speed level, and obtain the second maintenance speed level of the transmission equipment.

[0101] In specific implementation, set To reduce the number of maintenance personnel, The target number of power transmission equipment is the number of new power transmission equipment that needs to be replaced. Second maintenance speed level .

[0102] S636, determine the ratio of the preset unit maintenance speed level to the second maintenance speed level, determine the product of the ratio and the preset unit level loss transmission capacity, and obtain the third loss transmission capacity.

[0103] Among them, the third loss of transmission capacity represents the loss of transmission capacity affected by the replacement of the target transmission equipment.

[0104] In practice, the third loss of transmission capacity .

[0105] S638, determine the difference between the second transaction power prediction data and the third loss transmission power to obtain the third transaction power prediction data, and determine the third transaction power prediction data as the transaction power prediction data for a future preset time period.

[0106] In practice, the third transaction electricity prediction data Wherein, K represents the second transaction electricity prediction data, and the third transaction electricity prediction data is determined as the transaction electricity prediction data for a future preset time period.

[0107] In this embodiment, the loss of transmission capacity due to the replacement of transmission equipment with a higher aging level is subtracted from the second transaction power prediction data, thereby improving the accuracy of transaction power prediction.

[0108] In one exemplary embodiment, such as Figure 6 As shown, when there are target transmission equipment in the transmission equipment whose aging level exceeds the preset aging level range, the lost transmission volume in the transmission volume prediction data is determined according to the target transmission volume optimization strategy. The difference between the transmission volume prediction data and the lost transmission volume is determined to obtain the transaction volume prediction data for the future preset time period. This also includes steps S642 to S648, wherein:

[0109] S642, obtain the distance between power transmission equipment and the maximum distance among the distances.

[0110] In practice, the server can determine the distance between power transmission equipment based on the equipment location in the equipment ledger information, determine the maximum value of the distance, and obtain the maximum distance.

[0111] S644, determine the product of the ratio of distance and maximum distance and the preset unit maintenance speed level, determine the average of the second maintenance speed level and the product, and obtain the third maintenance speed level of the power transmission equipment.

[0112] In this embodiment, maintenance personnel perform maintenance from both ends of the power transmission line, and the distance between the power transmission equipment affects the maintenance speed. The third maintenance speed level characterizes the maintenance speed of the power transmission equipment affected by the distance between the power transmission equipment and the maintenance method.

[0113] In practice, the third maintenance speed level We obtain it from the following formula:

[0114]

[0115] in, The distance between power transmission equipment. This represents the maximum distance between power transmission equipment.

[0116] S646, determine the ratio of the preset unit maintenance level to the third maintenance speed level, and determine the product of the ratio and the preset unit level loss transmission capacity to obtain the fourth loss transmission capacity.

[0117] Among them, the fourth loss of electricity represents the loss of transmission power corresponding to the third maintenance speed level.

[0118] In practice, the fourth loss is the transmission capacity. .

[0119] S648, determine the difference between the third transaction power prediction data and the fourth loss transmission power to obtain the fourth transaction power prediction data, and determine the fourth transaction power prediction data as the transaction power prediction data for the future preset time period.

[0120] In practice, the fourth transaction electricity prediction data The four transaction volume forecast data are determined as the transaction volume forecast data for the future preset time period.

[0121] In this embodiment, the maintenance method of maintaining the transmission line from both ends and the impact of the distance between the transmission equipment on the maintenance speed are considered. The lost transmission volume is subtracted from the third transaction volume prediction data, thereby improving the accuracy of the transaction volume prediction.

[0122] In one exemplary embodiment, such as Figure 7 As shown, when there are target transmission equipment in the transmission equipment whose aging level exceeds the preset aging level range, the lost transmission volume in the transmission volume prediction data is determined according to the target transmission volume optimization strategy. The difference between the transmission volume prediction data and the lost transmission volume is determined to obtain the transaction volume prediction data for the future preset time period, including S652 to S656, wherein:

[0123] S652, determine the product of the ratio of distance to maximum distance and the preset unit maintenance speed level, and determine the fifth maintenance speed level of the transmission equipment based on the second maintenance speed level, the product and the preset fourth maintenance speed level.

[0124] In this embodiment, when maintenance personnel perform maintenance from both ends of the transmission line, the maintenance speeds of the personnel at both ends are inconsistent. The faster maintenance personnel can assist the slower ones, thereby increasing the overall maintenance speed. Therefore, the preset fourth maintenance speed level represents the maintenance speed level when maintenance personnel work together. The fourth maintenance speed level can be obtained by evaluating the ratio of the actual maintenance time to the planned maintenance time during collaborative maintenance.

[0125] The fifth maintenance speed level represents the maintenance speed affected by the combined maintenance method and maintenance distance.

[0126] In practice, the fifth maintenance speed level We obtain it from the following formula:

[0127]

[0128] in, This is the preset fourth maintenance speed level.

[0129] S654, determine the ratio of the preset unit maintenance level to the fifth maintenance speed level, determine the product of the ratio and the preset unit level loss transmission capacity, and obtain the fifth loss transmission capacity.

[0130] In practice, the fifth loss is the amount of transmission power lost. .

[0131] S656, determine the difference between the fourth transaction power prediction data and the fifth loss transmission power to obtain the fifth transaction power prediction data, and determine the fifth transaction power prediction data as the transaction power prediction data for the future preset time period.

[0132] In practice, the fifth transaction electricity prediction data The fifth transaction volume forecast data will be used as the transaction volume forecast data for a future preset time period.

[0133] In this embodiment, the loss of transaction electricity is subtracted from the fourth transaction electricity prediction data based on the influence of the combined maintenance method and maintenance distance on the maintenance speed, thereby improving the accuracy of transaction electricity prediction.

[0134] To provide a clearer explanation of the inter-provincial medium- and long-term electricity trading forecasting method provided in this application, a specific embodiment is described below, which includes the following steps:

[0135] S1, acquire equipment ledger data of power transmission equipment and meteorological data of the power generation system for a preset historical time period.

[0136] S2, based on meteorological data and a trained sunshine days prediction model, predicts the number of sunshine days within a preset time period in the future, and obtains the target number of sunshine days.

[0137] S3, assess the aging level of power transmission equipment based on equipment ledger data.

[0138] S4. Based on the target number of sunshine days, the preset daily sunshine transmission capacity, and the preset daily non-sunshine transmission capacity, determine the predicted transmission capacity data for the future preset time period.

[0139] S5, based on the aging level of the power transmission equipment and the preset aging level range, matches the target power transmission optimization strategy from multiple preset power transmission optimization strategies.

[0140] S6. Based on the aging level of the power transmission equipment and the preset maintenance cycle, determine the target maintenance cycle of the power transmission equipment, determine the product of the maintenance cycle and the preset unit cycle loss power transmission, obtain the first loss power transmission, determine the difference between the power transmission prediction data and the first loss power transmission, and obtain the first transaction power prediction data.

[0141] S7. Obtain the number of maintenance personnel, determine the product of the number of maintenance personnel and the preset unit maintenance speed level to obtain the first maintenance speed level of the transmission equipment, determine the second loss transmission amount based on the preset unit maintenance speed level, the first maintenance speed level and the preset unit level loss transmission amount, determine the difference between the first transaction volume prediction data and the second loss transmission amount to obtain the second transaction volume prediction data, and determine the transaction volume prediction data for the future preset time period if there is no target transmission equipment in the transmission equipment whose aging level exceeds the preset aging level range.

[0142] S8. In the case of a target power transmission equipment whose aging level exceeds the preset aging level range, the number of target power transmission equipment is obtained, the difference between the number of maintenance personnel and the number of target power transmission equipment is determined, the product of the difference and the preset unit maintenance speed level is determined to obtain the second maintenance speed level of the power transmission equipment, the ratio of the preset unit maintenance level to the second maintenance speed level is determined, the product of the ratio and the unit level loss transmission capacity is determined to obtain the third loss transmission capacity, and the difference between the second transaction power prediction data and the third loss transmission capacity is determined to obtain the third transaction power prediction data.

[0143] S9, obtain the distance between transmission equipment and the maximum distance, determine the product of the ratio of the distance and the maximum distance and the preset unit maintenance speed level, determine the average of the second maintenance speed level and the product, obtain the third maintenance speed level of the transmission equipment, determine the ratio of the preset unit maintenance level and the third maintenance speed level, determine the product of the ratio and the unit level loss transmission capacity, obtain the fourth loss transmission capacity, determine the difference between the third transaction power prediction data and the fourth loss transmission capacity, and obtain the fourth transaction power prediction data.

[0144] S10, determine the product of the ratio of distance and maximum distance with the preset unit maintenance speed level, determine the fifth maintenance speed level of the transmission equipment based on the second maintenance speed level, the product and the preset fourth maintenance speed level, determine the ratio of the preset unit maintenance level to the fifth maintenance speed level, determine the product of the ratio and the preset unit level loss transmission capacity to obtain the fifth loss transmission capacity, determine the difference between the fourth transaction power prediction data and the product to obtain the fifth transaction power prediction data, and determine the fifth transaction power prediction data as the transaction power prediction data for the future preset time period.

[0145] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0146] In one exemplary embodiment, such as Figure 8 As shown, an inter-provincial medium- and long-term electricity trading forecasting device 600 is provided, comprising: a data acquisition module 610, a sunshine days forecasting module 620, an aging level assessment module 630, a transmission capacity forecasting module 640, a transmission capacity optimization strategy determination module 650, and a trading capacity forecasting module 660, wherein:

[0147] Data acquisition module 610 is used to acquire equipment ledger data of power transmission equipment and meteorological data of power generation system for a preset historical time period;

[0148] The sunshine days prediction module 620 is used to predict the number of sunshine days in a future preset time period based on meteorological data and a trained sunshine days prediction model, and obtain the target number of sunshine days.

[0149] The aging level assessment module 630 is used to assess the aging level of power transmission equipment based on equipment ledger data.

[0150] The power transmission prediction module 640 is used to determine the power transmission prediction data for a future preset time period based on the target number of sunshine days, the preset daily sunshine power transmission and the preset daily non-sunshine power transmission.

[0151] The power transmission optimization strategy determination module 650 is used to match the target power transmission optimization strategy from multiple preset power transmission optimization strategies based on the aging level of the power transmission equipment and the preset aging level range.

[0152] The transaction power prediction module 660 is used to determine the lost transmission power in the transmission power prediction data according to the target transmission power optimization strategy, determine the difference between the transmission power prediction data and the lost transmission power, and obtain the transaction power prediction data for a future preset time period.

[0153] In an exemplary embodiment, the transaction power prediction module 660 is further configured to determine the target maintenance cycle of the transmission equipment based on the aging level of the transmission equipment and the preset maintenance cycle; determine the product of the maintenance cycle and the preset unit cycle loss transmission power to obtain the first loss transmission power; determine the difference between the transmission power prediction data and the first loss transmission power to obtain the first transaction power prediction data; and determine the first transaction power prediction data as the transaction power prediction data for a future preset time period.

[0154] In an exemplary embodiment, the transaction power prediction module 660 is further configured to: obtain the number of maintenance personnel; determine the product of the number of maintenance personnel and a preset unit maintenance speed level to obtain a first maintenance speed level of the transmission equipment; determine a second loss transmission capacity based on the preset unit maintenance speed level, the first maintenance speed level, and the preset unit level loss transmission capacity; determine the difference between the first transaction power prediction data and the second loss transmission capacity to obtain second transaction power prediction data; and determine the second transmission capacity prediction data as the transaction power prediction data for a preset future time period.

[0155] In an exemplary embodiment, the transaction power prediction module 660 is further configured to: acquire the number of target transmission equipment; determine the difference between the number of maintenance personnel and the number of target transmission equipment; determine the product of the difference and a preset unit maintenance speed level to obtain a second maintenance speed level for the transmission equipment; determine the ratio of the preset unit maintenance level to the second maintenance speed level; determine the product of the ratio and the unit level loss transmission power to obtain a third loss transmission power; determine the difference between the second transaction power prediction data and the third loss transmission power to obtain third transaction power prediction data; and determine the third transaction power prediction data as the transaction power prediction data for a preset future time period.

[0156] In an exemplary embodiment, the transaction power prediction module 660 is further configured to: obtain the distance between transmission equipment and the maximum distance; determine the product of the ratio of the distance and the maximum distance with a preset unit maintenance speed level; determine the average of the second maintenance speed level and the product to obtain the third maintenance speed level of the transmission equipment; determine the ratio of the preset unit maintenance level to the third maintenance speed level; determine the product of the ratio and the unit level loss transmission power to obtain the fourth loss transmission power; determine the difference between the third transaction power prediction data and the fourth loss transmission power to obtain the fourth transaction power prediction data; and determine the fourth transaction power prediction data as the transaction power prediction data for a preset future time period.

[0157] In an exemplary embodiment, the transaction power prediction module 660 is further configured to determine the product of the ratio of distance and maximum distance with a preset unit maintenance speed level; determine the fifth maintenance speed level of the transmission equipment based on the second maintenance speed level, the product, and a preset fourth maintenance speed level; determine the ratio of the preset unit maintenance level to the fifth maintenance speed level; determine the product of the ratio and a preset unit level loss transmission power to obtain the fifth loss transmission power; determine the difference between the fourth transaction power prediction data and the product to obtain the fifth transaction power prediction data; and determine the fifth transaction power prediction data as the transaction power prediction data for a preset future time period.

[0158] Each module in the aforementioned inter-provincial medium- and long-term electricity trading forecasting device 600 can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0159] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network. When the computer program is executed by the processor, it implements a method for predicting inter-provincial medium- and long-term electricity transactions.

[0160] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0161] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in any of the above embodiments of the inter-provincial medium- and long-term electricity trading forecasting method.

[0162] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps in any of the above embodiments of the inter-provincial medium- and long-term electricity trading prediction method.

[0163] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the inter-provincial medium- and long-term electricity trading forecasting method.

[0164] 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 used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0165] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0166] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0167] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for predicting electricity volume in inter-provincial medium- and long-term transactions, characterized in that, The method includes: Obtain equipment ledger data for power transmission equipment and meteorological data for historical preset time periods of the power generation system; Based on the meteorological data and the trained sunshine days prediction model, the number of sunshine days in the future within a preset time period is predicted to obtain the target number of sunshine days. The aging level of the power transmission equipment is assessed based on the equipment ledger data. Based on the target number of sunshine days, the preset daily sunshine transmission capacity, and the preset daily non-sunshine transmission capacity, the predicted transmission capacity data for the future preset time period is determined; Based on the aging level of the power transmission equipment and the preset aging level range, a target power transmission optimization strategy is matched from multiple preset power transmission optimization strategies. Based on the target transmission capacity optimization strategy, the lost transmission capacity in the transmission capacity prediction data is determined, and the difference between the transmission capacity prediction data and the lost transmission capacity is determined to obtain the transaction power prediction data for the future preset time period.

2. The method according to claim 1, characterized in that, Based on the target transmission capacity optimization strategy, the lost transmission capacity in the transmission capacity prediction data is determined, the difference between the transmission capacity prediction data and the lost transmission capacity is determined, and the transaction volume prediction data for the future preset time period is obtained, including: The target maintenance cycle of the power transmission equipment is determined based on the aging level and preset maintenance cycle of the power transmission equipment. The first loss transmission capacity is obtained by multiplying the maintenance cycle and the preset unit cycle loss transmission capacity. The difference between the predicted transmission volume and the first lost transmission volume is determined to obtain the first predicted trading volume, and the first predicted trading volume is determined as the predicted trading volume for the future preset time period.

3. The method according to claim 2, characterized in that, The step of determining the lost transmission volume in the transmission volume prediction data according to the target transmission volume optimization strategy, determining the difference between the transmission volume prediction data and the lost transmission volume, and obtaining the transaction volume prediction data for the future preset time period includes: Obtain the number of maintenance personnel; The first maintenance speed level of the power transmission equipment is obtained by multiplying the number of maintenance personnel by the preset unit maintenance speed level. The second loss of transmission capacity is determined based on the preset unit maintenance speed level, the first maintenance speed level, and the preset unit level loss of transmission capacity. The difference between the first transaction power prediction data and the second loss transmission power is determined to obtain the second transaction power prediction data, and the second transmission power prediction data is determined as the transaction power prediction data for the future preset time period.

4. The method according to claim 3, characterized in that, In the case where there are target transmission equipment in the transmission equipment whose aging level exceeds the preset aging level range, the step of determining the lost transmission capacity in the transmission capacity prediction data according to the target transmission capacity optimization strategy, determining the difference between the transmission capacity prediction data and the lost transmission capacity, and obtaining the transaction volume prediction data for the future preset time period includes: Obtain the quantity of the target power transmission equipment; The difference between the number of maintenance personnel and the number of target power transmission equipment is determined, and the product of the difference and the preset unit maintenance speed level is determined to obtain the second maintenance speed level of the power transmission equipment; Determine the ratio of the preset unit maintenance speed level to the second maintenance speed level, and determine the product of the ratio and the preset unit level loss transmission capacity to obtain the third loss transmission capacity; The difference between the second transaction power prediction data and the third loss transmission power is determined to obtain the third transaction power prediction data, and the third transaction power prediction data is determined as the transaction power prediction data for the future preset time period.

5. The method according to claim 4, characterized in that, In the case where there are target transmission equipment in the transmission equipment whose aging level exceeds the preset aging level range, the step of determining the lost transmission capacity in the transmission capacity prediction data according to the target transmission capacity optimization strategy, determining the difference between the transmission capacity prediction data and the lost transmission capacity, and obtaining the transaction volume prediction data for the future preset time period includes: Obtain the distances between power transmission equipment and the maximum distance among those distances; The product of the ratio of the distance to the maximum distance and the preset unit maintenance speed level is determined, and the average of the second maintenance speed level and the product is determined to obtain the third maintenance speed level of the power transmission equipment. Determine the ratio of the preset unit maintenance level to the third maintenance speed level, and determine the product of the ratio and the preset unit level loss transmission capacity to obtain the fourth loss transmission capacity; The difference between the third transaction power prediction data and the fourth loss transmission power is determined to obtain the fourth transaction power prediction data, and the fourth transaction power prediction data is determined as the transaction power prediction data for the future preset time period.

6. The method according to claim 5, characterized in that, In the case where there are target transmission equipment in the transmission equipment whose aging level exceeds the preset aging level range, the step of determining the lost transmission capacity in the transmission capacity prediction data according to the target transmission capacity optimization strategy, determining the difference between the transmission capacity prediction data and the lost transmission capacity, and obtaining the transaction volume prediction data for the future preset time period includes: The product of the ratio of the distance to the maximum distance and the preset unit maintenance speed level is determined, and the fifth maintenance speed level of the power transmission equipment is determined based on the second maintenance speed level, the product, and the preset fourth maintenance speed level. Determine the ratio of the preset unit maintenance level to the fifth maintenance speed level, and determine the product of the ratio and the preset unit level loss transmission capacity to obtain the fifth loss transmission capacity; The difference between the fourth transaction electricity prediction data and the product is determined to obtain the fifth transaction electricity prediction data, and the fifth transaction electricity prediction data is determined as the transaction electricity prediction data for the future preset time period.

7. A device for predicting inter-provincial medium- and long-term electricity transactions, characterized in that, The device includes: The data acquisition module is used to acquire equipment ledger data of power transmission equipment and meteorological data of the power generation system for a preset historical time period; The sunshine days prediction module is used to predict the number of sunshine days in a future preset time period based on the meteorological data and the trained sunshine days prediction model, so as to obtain the target number of sunshine days. The aging level assessment module is used to assess the aging level of the power transmission equipment based on the equipment ledger data. The power transmission prediction module is used to determine the power transmission prediction data for the future preset time period based on the target number of sunshine days, the preset daily sunshine power transmission, and the preset daily non-sunshine power transmission. The power transmission optimization strategy determination module is used to match a target power transmission optimization strategy from multiple preset power transmission optimization strategies based on the aging level of the power transmission equipment and a preset aging level range. The transaction power prediction module is used to determine the lost transmission power in the transmission power prediction data according to the target transmission power optimization strategy, determine the difference between the transmission power prediction data and the lost transmission power, and obtain the transaction power prediction data for the future preset time period.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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