Power trading method, device, equipment and storage medium

Abstract

The present invention belongs to the field of electric power technology, and discloses an electric power trading method, device, equipment and storage medium. The present invention obtains multiple historical electric power usage information of the area where the electric power demander is located; performs electric power forecasting based on the multiple historical electric power usage information of the area where the electric power demander is located, and determines the predicted electric power; determines the electric power selling unit price according to the single purchase electric power, the predicted electric power and the electric power cost; and conducts electric power trading according to the electric power selling unit price. In the above manner, electric power forecasting is performed based on multiple historical electric power usage information of the area where the electric power demander is located, and the predicted electric power of the electric power demander in the future time period is determined. Based on the predicted electric power and the single purchase amount of the electric power demander, combined with the electric power cost, the electric power selling unit price is determined, and thus electric power trading is conducted based on the electric power selling unit price, which ensures the rationality of the electric power selling unit price, and at the same time can comply with market laws and meet the profit needs of the seller.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to electric power trading methods, devices, equipment and storage media. Background Art

[0002] The electric power industry is a basic industry in my country. With the rapid development of my country's economy, the demand for residential and industrial electricity continues to rise, and electricity prices have become a core factor affecting the development of the industry. How to formulate reasonable electricity prices so that they can both comply with market laws and meet the profit needs of sellers?

[0003] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of this application is to provide an electricity trading method, device, equipment and storage medium, aiming to solve the technical problem of how to formulate a reasonable electricity price.

[0005] To achieve the above objectives, the present application proposes a power trading method, which comprises the following steps:

[0006] Upon receiving a power purchase request from a power demander, obtaining a plurality of historical power usage information of the area where the power demander is located;

[0007] Performing power forecasting based on multiple historical power usage information of the area where the power demander is located to determine the predicted power quantity of the power demander;

[0008] Determining a single purchase amount of electricity by the power demander based on the power purchase request;

[0009] Determine the unit price of electricity sales according to the single purchase amount of electricity, the predicted amount of electricity and the electricity cost;

[0010] Electricity trading is performed according to the electricity selling unit price.

[0011] Optionally, the acquiring a plurality of historical power usage information of the area where the power demander is located includes:

[0012] Determining the region where the power demander is located and the type of power consumption of the power demander according to the power purchase request of the power demander;

[0013] Determining a plurality of electricity demanders according to the regions where the electricity demanders are located;

[0014] Determining a plurality of target demand parties from a plurality of electricity demand parties according to the historical electricity purchase type of each electricity demand party and the electricity consumption type;

[0015] Obtain the historical electricity usage information of each target demand party within a preset time period.

[0016] Optionally, the performing power forecasting based on a plurality of historical power usage information of the area where the power demander is located to determine the predicted power quantity of the power demander includes:

[0017] Determine the power usage data at each time point in each historical power usage information based on multiple historical power usage information of the area where the power demander is located;

[0018] Determine the power usage sequence corresponding to each piece of historical power usage information according to the power usage data at each time point in each piece of historical power usage information;

[0019] A prediction model is constructed based on multiple power usage sequences to obtain a power prediction model;

[0020] Perform power forecasting on the power demander according to the power forecasting model to determine the predicted power quantity of the power demander.

[0021] Optionally, constructing a prediction model based on multiple power usage sequences to obtain a power prediction model includes:

[0022] determining extreme usage data of each power usage sequence based on a plurality of power usage sequences;

[0023] Determine the grey correlation coefficient of each power usage sequence according to the extreme usage data of each power usage sequence;

[0024] Determine the electricity consumption characteristic information according to the grey correlation coefficient of each electricity use sequence;

[0025] Extracting features from a plurality of power usage sequences according to the power usage feature information to determine a plurality of model training sequences;

[0026] A prediction model is constructed based on multiple model training sequences to obtain a power prediction model.

[0027] Optionally, performing power forecasting on the power demander according to the power forecasting model to determine the predicted power of the power demander includes:

[0028] Obtaining historical power usage information of the power demander within a preset time period;

[0029] Determining a historical electricity usage sequence of the electricity demander based on historical electricity usage information of the electricity demander within a preset time period;

[0030] The historical electricity consumption sequence is input into the electricity consumption prediction model to perform electricity prediction, and the predicted electricity consumption of the electricity demander is determined.

[0031] Optionally, determining the electricity selling price according to the single purchased electricity amount, the predicted electricity amount and the electricity cost includes:

[0032] Determining an electricity quantity relationship according to the single purchased electricity quantity and the predicted electricity quantity;

[0033] Determine the electricity pricing coefficient according to the electricity relationship and pricing index;

[0034] The unit price is calculated based on the electricity pricing coefficient and the electricity cost to determine the unit price of electricity sales.

[0035] Optionally, the calculating the unit price based on the electricity pricing coefficient and the electricity cost to determine the electricity selling unit price includes:

[0036] Determine the initial price based on the electricity pricing coefficient and electricity cost;

[0037] Determining a profit from electricity sales based on the single purchase amount and the initial price;

[0038] The initial pricing is adjusted according to the electricity selling profit and the electricity constraint condition to determine the electricity selling unit price.

[0039] In addition, to achieve the above-mentioned purpose, the present invention also proposes an electric power trading device, the electric power trading device comprising:

[0040] An acquisition module, configured to acquire, upon receiving a purchase request for electricity from an electricity demander, a plurality of historical electricity usage information of an area where the electricity demander is located;

[0041] A prediction module, configured to perform power prediction based on a plurality of historical power usage information of the area where the power demander is located, and determine the predicted power quantity of the power demander;

[0042] A processing module, configured to determine a single purchase amount of electricity of the electricity demander based on the electricity purchase request;

[0043] A processing module, used to determine a unit price of electricity sales according to the single purchase electricity quantity, the predicted electricity quantity and electricity cost;

[0044] A trading module is used to conduct electricity trading according to the electricity selling unit price.

[0045] In addition, to achieve the above objectives, the present application also proposes an electricity trading device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program is configured to implement the steps of the electricity trading method described above.

[0046] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the electricity trading method described above are implemented.

[0047] The present application provides a method for power trading, which obtains multiple historical power usage information of the area where the power demander is located upon receiving a power purchase request from the power demander; performs power forecasting based on the multiple historical power usage information of the area where the power demander is located to determine the predicted power quantity of the power demander; determines the single purchase power quantity of the power demander based on the power purchase request; determines the power selling unit price according to the single purchase power quantity, the predicted power quantity and the power cost; and conducts power trading according to the power selling unit price. In the above manner, power forecasting is performed based on multiple historical power usage information of the area where the power demander is located to determine the predicted power quantity of the power demander in the future time period; the power selling unit price is determined based on the predicted power quantity and the single purchase quantity of the power demander in combination with the power cost, thereby conducting power trading based on the power selling unit price, ensuring the rationality of the power selling unit price, and at the same time, it can comply with market rules and meet the profit needs of the seller. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0049] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0050] Figure 1 A schematic diagram of a flow chart provided for the first embodiment of the power trading method of the present application;

[0051] Figure 2 A schematic diagram of a flow chart provided for Embodiment 2 of the power trading method of this application;

[0052] Figure 3 A flowchart of the third embodiment of the power trading method of the present application is provided;

[0053] Figure 4 This is a schematic diagram of the module structure of the power trading device according to an embodiment of the present application;

[0054] Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the power trading method in the embodiment of the present application.

[0055] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0056] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0057] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0058] The main solution of the embodiment of the present application is: when receiving a power purchase request from a power demander, obtaining multiple historical power usage information of the area where the power demander is located; performing power forecasting based on the multiple historical power usage information of the area where the power demander is located, and determining the predicted power quantity of the power demander; determining the single purchase power quantity of the power demander based on the power purchase request; determining the power selling unit price according to the single purchase power quantity, the predicted power quantity and the power cost; and conducting power transactions according to the power selling unit price.

[0059] The present application makes an electricity forecast based on multiple historical electricity usage information of the area where the electricity demander is located, determines the predicted electricity quantity of the electricity demander in the future time period, determines the electricity selling unit price based on the predicted electricity quantity and the single purchase quantity of the electricity demander, and combines them with the electricity cost, so as to conduct electricity transactions based on the electricity selling unit price, thereby ensuring the rationality of the electricity selling unit price, and at the same time conforming to market rules and meeting the profit needs of the seller.

[0060] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a power trading device, etc. The following takes the power trading device as an example to illustrate this embodiment and the following embodiments.

[0061] Based on this, the present application embodiment provides a power trading method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the power trading method of the present application.

[0062] In this embodiment, the method includes steps S10 to S50:

[0063] Step S10: upon receiving a power purchase request from a power demander, obtaining a plurality of historical power usage information of the area where the power demander is located.

[0064] It should be noted that the execution subject of this embodiment is an electric power trading device, wherein the electric power trading device has functions such as data processing, data communication and program running, and the electric power trading device can be an integrated controller, a control computer and other devices, and of course can also be other devices with similar functions, which are not limited in this embodiment. In this embodiment, the electric power trading device is the seller of electric power.

[0065] It can be understood that the electricity demander refers to the user who purchases electricity, and the electricity purchase request includes but is not limited to the identity information of the electricity demander, the area information, the type of electricity used, the purchase amount, and other information.

[0066] In a specific implementation, when a power purchase request is received from a power demander, the region where the power demander is located is determined based on the power purchase request, and power usage information of other power purchasers in the region where the power demander is located over a period of history is obtained. The power usage information of other power purchasers over a period of history is multiple historical power usage information of the region where the power demander is located.

[0067] It should be noted that in order to ensure that relevant historical electricity usage information can be obtained, further, the acquisition of multiple historical electricity usage information of the area where the electricity demand party is located includes: determining the area where the electricity demand party is located and the electricity usage type of the electricity demand party based on the electricity purchase request of the electricity demand party; determining multiple electricity demand parties based on the area where the electricity demand party is located; determining multiple target electricity demand parties among the multiple electricity demand parties based on the historical electricity purchase type and the electricity usage type of each electricity demand party; and obtaining the historical electricity usage information of each target electricity demand party within a preset time period.

[0068] It is understandable that the electricity usage type includes but is not limited to residential electricity usage type and industrial electricity usage type, etc., and information is extracted from the electricity purchase request of the electricity demander to obtain the area and electricity usage type of the electricity demander.

[0069] In a specific implementation, after determining the area where the power demander is located, determine other power purchasers who have purchased power in the area where the power demander is located within a period of time in history. Other power purchasers are the power demanders. Since there are differences between the power consumption types of multiple power demanders and the power demander, multiple power demanders are screened based on the power consumption types of multiple power demanders and the power demander, and determine the power demanders with the same power consumption type as the power demander. The power demanders with the same power consumption type as the power demander are the target demanders.

[0070] It should be noted that historical power usage information refers to the power usage information of the target demand party within a historical preset time period, including but not limited to the power usage information of the target demand party at each time point. In this implementation, the historical power usage information of each target demand party within a preset time period is multiple historical power usage information of the area where the power demand party is located.

[0071] Step S20: Perform power forecasting based on multiple historical power usage information of the area where the power demander is located to determine the predicted power quantity of the power demander.

[0072] It should be noted that feature extraction is performed based on multiple historical power usage information in the area where the power demander is located, and the deep learning network is trained based on the extracted multiple feature sequences to obtain a trained power prediction model. The power prediction model can predict the power usage of the power prediction party in the future. The power usage of the power prediction party in the future is the predicted power of the power demander.

[0073] Step S30: determining the single purchase amount of electricity of the electricity demander based on the electricity purchase request.

[0074] It should be noted that information is extracted from the electricity purchase request to determine the electricity purchase amount of the electricity demander this time, and the electricity purchase amount of the electricity demander this time is the single purchase amount of electricity by the electricity demander.

[0075] Step S40: determining the electricity selling price according to the single purchase electricity amount, the predicted electricity amount and the electricity cost.

[0076] It should be noted that electricity cost refers to the cost of electricity produced by the electricity seller or the cost of electricity wholesaled from the power plant by the electricity seller. The unit price of electricity sold to the electricity demander in this transaction is determined based on the single purchase of electricity, the predicted electricity volume of the electricity demander and the electricity cost.

[0077] Step S50: Conducting electricity trading according to the electricity selling unit price.

[0078] This embodiment obtains multiple historical power usage information of the area where the power demander is located upon receiving a power purchase request from the power demander; performs power forecasting based on the multiple historical power usage information of the area where the power demander is located to determine the predicted power quantity of the power demander; determines the single purchase power quantity of the power demander based on the power purchase request; determines the power selling unit price according to the single purchase power quantity, the predicted power quantity and the power cost; and conducts power trading according to the power selling unit price. In the above manner, power forecasting is performed based on multiple historical power usage information of the area where the power demander is located to determine the predicted power quantity of the power demander in a future time period; the power selling unit price is determined based on the predicted power quantity and the single purchase quantity of the power demander in combination with the power cost, thereby conducting power trading based on the power selling unit price, thereby ensuring the rationality of the power selling unit price, and at the same time, it can comply with market rules and meet the profit needs of the seller.

[0079] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 2 , step S20, the power trading method further includes steps S21~S24:

[0080] Step S21: determining the power usage data at each time point in each piece of historical power usage information based on a plurality of pieces of historical power usage information of the area where the power demander is located.

[0081] It should be noted that the power usage data of each target demand party at each time point is determined based on a plurality of historical power usage information of the area where the power demand party is located.

[0082] Step S22: determining a power usage sequence corresponding to each piece of historical power usage information according to the power usage data at each time point in each piece of historical power usage information.

[0083] It should be noted that the first The power usage sequence corresponding to the historical power usage information , where t represents a time point, J represents a specific time point among multiple time points, Indicates the power usage data at time point t.

[0084] Step S23: construct a prediction model based on multiple power usage sequences to obtain a power prediction model.

[0085] It should be noted that the deep learning network is trained based on multiple power usage sequences to obtain a trained power prediction model.

[0086] It can be understood that in order to ensure the performance of the electricity prediction model, further, the prediction model is constructed based on multiple electricity usage sequences to obtain the electricity prediction model, including: determining the extreme usage data of each electricity usage sequence based on multiple electricity usage sequences; determining the grey correlation coefficient of each electricity usage sequence according to the extreme usage data of each electricity usage sequence; determining electricity usage characteristic information according to the grey correlation coefficient of each electricity usage sequence; performing feature extraction on multiple electricity usage sequences according to the electricity usage characteristic information to determine multiple model training sequences; and constructing the prediction model based on multiple model training sequences to obtain the electricity prediction model.

[0087] In the specific implementation, in multiple power usage sequences, the maximum difference between the two levels and the minimum difference between the two levels can be calculated. The maximum difference between the two levels and the minimum difference between the two levels in the sequence are the extreme usage data. When calculating the minimum difference between the two levels and the maximum difference between the two levels, each indicator is regarded as a column vector, and each vector in the subsequence is subtracted from the vector in the parent sequence and the absolute value is taken, and the minimum and maximum values ​​are found. The minimum value becomes the minimum difference between the two levels, and the maximum value becomes the maximum difference between the two levels. and .

[0088] It should be noted that after determining the extreme value usage data, the grey correlation coefficient between each power usage sequence can be determined. ,in is the resolution coefficient, and the range of the resolution coefficient is generally [0.5, 0.8]. Based on the grey correlation coefficient between each power usage sequence, the grey correlation degree of all power usage sequences can be determined. In this embodiment, the calculated grey correlation degrees of all power usage sequences are the power usage characteristic information of the multiple power usage sequences.

[0089] It can be understood that, based on the electricity consumption feature information, feature extraction is performed on multiple electricity usage sequences to obtain sequences with high feature similarity as multiple model training sequences, and the deep learning network is trained based on the multiple model training sequences to obtain the electricity prediction model.

[0090] In a specific implementation, an LSTM deep learning network, a GC-LSTM deep learning network or other deep learning networks may be used to construct a power prediction model, and this embodiment does not limit this. Multiple model training sequences are input into the deep learning network so that the deep learning network is trained, and the power prediction model is finally obtained after the training is completed.

[0091] Step S24: Performing power forecasting for the power demander according to the power forecasting model to determine the forecasted power quantity of the power demander.

[0092] It should be noted that the power consumption of the power demander in a future preset time period is predicted based on the power prediction model, so as to obtain the predicted power consumption of the power demander.

[0093] It can be understood that in order to accurately obtain the predicted electricity quantity of the electricity demand side, further, the electricity prediction is performed on the electricity demand side according to the electricity prediction model to determine the predicted electricity quantity of the electricity demand side, including: obtaining the historical electricity usage information of the electricity demand side within a preset time period; determining the historical electricity consumption sequence of the electricity demand side based on the historical electricity usage information of the electricity demand side within the preset time period; inputting the historical electricity consumption sequence into the electricity prediction model to perform electricity prediction to determine the predicted electricity quantity of the electricity demand side.

[0094] In a specific implementation, historical power information of the power demander within a preset time period is obtained, the power usage data of the power demander at each time point is determined, and based on the power usage data of the power demander at each time point, the historical power usage sequence corresponding to the historical power information is determined, and the historical power usage sequence is input into the power consumption prediction model to obtain the predicted power consumption of the power demander.

[0095] This embodiment determines the power usage data at each time point in each historical power usage information based on multiple historical power usage information in the area where the power demander is located; determines the power usage sequence corresponding to each historical power usage information based on the power usage data at each time point in each historical power usage information; constructs a prediction model based on multiple power usage sequences to obtain a power prediction model; and performs power prediction on the power demander based on the power prediction model to determine the predicted power of the power demander. In the above manner, the accuracy of power prediction is guaranteed, laying the foundation for subsequent pricing.

[0096] Based on the above embodiments of the present application, in the third embodiment of the present application, the same or similar contents as those in the above embodiments can be referred to the above introduction, and will not be described in detail later. Figure 3 , step S40, the power trading method further includes steps S41 to S43:

[0097] Step S41: determining an electricity quantity relationship according to the single purchase electricity quantity and the predicted electricity quantity.

[0098] It should be noted that the single purchased electricity amount and the predicted electricity amount are compared to determine the relationship between the two, and the relationship between the two is the electricity relationship.

[0099] Step S42: Determine the electricity quantity pricing coefficient according to the electricity quantity relationship and the pricing index.

[0100] It should be noted that when purchasing electricity at a single time Not less than the predicted power When the single purchase of electricity is less than the predicted electricity, it means that the electricity demander will purchase electricity again in the future preset time period. When the electricity trading device enables the permission for the excess use of electricity, the electricity demander will not have the situation of over-use, and the electricity demander will not have the risk of defaulting on fees. Therefore, the electricity pricing coefficient of the electricity demander can be smaller. When the single purchase of electricity is less than the predicted electricity, it means that the electricity demander will purchase electricity again in the future preset time period. When the electricity trading device enables the permission for the excess use of electricity, the electricity demander will have the situation of over-use, and the electricity demander may have the risk of defaulting on fees. Therefore, the electricity pricing coefficient of the electricity demander will be appropriately increased. In this embodiment, when When the electricity pricing coefficient ; , electricity pricing coefficient ,in is a pre-set pricing indicator. The value range is (0.25, 0.75).

[0101] Step S43: Calculate the unit price based on the electricity pricing coefficient and the electricity cost to determine the electricity selling unit price.

[0102] It should be noted that after determining the electricity pricing coefficient, and electricity costs The unit price is calculated to determine the unit price of electricity sale. In order to ensure the rationality of the unit price of electricity sale, the unit price is further calculated based on the electricity pricing coefficient and the electricity cost to determine the unit price of electricity sale, including: determining an initial price according to the electricity pricing coefficient and the electricity cost; determining the electricity sale profit according to the single purchase of electricity and the initial price; adjusting the initial price according to the electricity sale profit and electricity constraints to determine the unit price of electricity sale.

[0103] It is understandable that according to the electricity pricing coefficient and electricity costs Determine initial pricing , based on the single purchase of electricity and the initial pricing, determine the profit from the electricity sales .

[0104] In practice, the power constraint refers to the upper limit of the power price set by the market. , determine whether the profit from electricity sales reaches the preset profit percentage If the preset profit percentage is not reached When the preset profit percentage is Determine Profit Pricing , based on profit pricing and initial pricing, the unit price of electricity sales is obtained , during the adjustment process, .

[0105] This embodiment determines the electricity price by determining the electricity price relationship according to the single purchase electricity and the predicted electricity; determining the electricity price pricing coefficient according to the electricity price relationship and the pricing index; and calculating the unit price based on the electricity price pricing coefficient and the electricity cost. Through the above method, the rationality of the electricity price setting is guaranteed.

[0106] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the power trading method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0107] This application also provides an electricity trading device, please refer to Figure 4 , the power trading device comprises:

[0108] The acquisition module 10 is used to acquire a plurality of historical power usage information of the area where the power demander is located when receiving a power purchase request from the power demander.

[0109] The prediction module 20 is used to perform power prediction based on a plurality of historical power usage information of the area where the power demander is located, and determine the predicted power quantity of the power demander.

[0110] The processing module 30 is used to determine the single purchase amount of electricity of the power demander based on the power purchase request.

[0111] The processing module 30 is further configured to determine a unit price for selling electricity according to the single purchased electricity quantity, the predicted electricity quantity and the electricity cost.

[0112] The transaction module 40 is used to carry out electricity transaction according to the electricity selling unit price.

[0113] In one embodiment, the acquisition module 10 is further used to determine the region where the power demander is located and the power consumption type of the power demander according to the power purchase request of the power demander;

[0114] Determining a plurality of electricity demanders according to the regions where the electricity demanders are located;

[0115] Determining a plurality of target demand parties from a plurality of electricity demand parties according to the historical electricity purchase type of each electricity demand party and the electricity consumption type;

[0116] Obtain the historical electricity usage information of each target demand party within a preset time period.

[0117] In one embodiment, the prediction module 20 is further used to determine the power usage data at each time point in each historical power usage information based on multiple historical power usage information of the area where the power demander is located;

[0118] Determine the power usage sequence corresponding to each piece of historical power usage information according to the power usage data at each time point in each piece of historical power usage information;

[0119] A prediction model is constructed based on multiple power usage sequences to obtain a power prediction model;

[0120] Perform power forecasting on the power demander according to the power forecasting model to determine the predicted power quantity of the power demander.

[0121] In one embodiment, the processing module 30 is further used to determine the power usage data at each time point in each historical power usage information based on multiple historical power usage information of the area where the power demander is located;

[0122] Determine the power usage sequence corresponding to each piece of historical power usage information according to the power usage data at each time point in each piece of historical power usage information;

[0123] A prediction model is constructed based on multiple power usage sequences to obtain a power prediction model;

[0124] Perform power forecasting on the power demander according to the power forecasting model to determine the predicted power quantity of the power demander.

[0125] In one embodiment, the processing module 30 is further configured to determine the extreme usage data of each power usage sequence based on the plurality of power usage sequences;

[0126] Determine the grey correlation coefficient of each power usage sequence according to the extreme usage data of each power usage sequence;

[0127] Determine the electricity consumption characteristic information according to the grey correlation coefficient of each electricity use sequence;

[0128] Extracting features from a plurality of power usage sequences according to the power usage feature information to determine a plurality of model training sequences;

[0129] A prediction model is constructed based on multiple model training sequences to obtain a power prediction model.

[0130] In one embodiment, the processing module 30 is further used to obtain historical power usage information of the power demander within a preset time period;

[0131] Determining a historical electricity usage sequence of the electricity demander based on historical electricity usage information of the electricity demander within a preset time period;

[0132] The historical electricity consumption sequence is input into the electricity consumption prediction model to perform electricity prediction, and the predicted electricity consumption of the electricity demander is determined.

[0133] In one embodiment, the processing module 30 is further used to determine the power relationship according to the single purchase power and the predicted power;

[0134] Determine the electricity pricing coefficient according to the electricity relationship and pricing index;

[0135] The unit price is calculated based on the electricity pricing coefficient and the electricity cost to determine the unit price of electricity sales.

[0136] In one embodiment, the processing module 30 is further used to determine the initial price according to the electricity pricing coefficient and the electricity cost;

[0137] Determining a profit from electricity sales based on the single purchase amount and the initial price;

[0138] The initial pricing is adjusted according to the electricity selling profit and the electricity constraint condition to determine the electricity selling unit price.

[0139] The power trading device provided by the present application adopts the power trading method in the above embodiment, which can solve the technical problem of how to formulate a reasonable power price. Compared with the prior art, the beneficial effects of the power trading device provided by the present application are the same as the beneficial effects of the power trading method provided by the above embodiment, and the other technical features in the power trading device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0140] The present application provides an electricity trading device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the electricity trading method in the above-mentioned embodiment one.

[0141] Reference below Figure 5 , which shows a schematic diagram of the structure of a power trading device suitable for implementing the embodiment of the present application. The power trading device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The electricity trading device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0142] like Figure 5 As shown, the power trading device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the power trading device are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the power trading device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a power trading device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0143] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0144] The power trading device provided by this application adopts the power trading method in the above embodiment, which can solve the technical problem of how to formulate a reasonable power price. Compared with the prior art, the beneficial effects of the power trading device provided by this application are the same as the beneficial effects of the power trading method provided by the above embodiment, and the other technical features in the power trading device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0145] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0146] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0147] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the power trading method in the above-mentioned embodiment.

[0148] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0149] The computer-readable storage medium may be included in the power trading device; or may exist independently without being installed in the power trading device.

[0150] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the power trading device, the power trading device enables the power trading device to: upon receiving a power purchase request from a power demander, obtain multiple historical power usage information of the area where the power demander is located; perform power forecasting based on multiple historical power usage information of the area where the power demander is located, and determine the predicted power quantity of the power demander; determine the single purchase power quantity of the power demander based on the power purchase request; determine the power selling unit price according to the single purchase power quantity, the predicted power quantity and the power cost; and perform power trading according to the power selling unit price.

[0151] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0152] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0153] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0154] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned power trading method, and can solve the technical problem of how to formulate a reasonable power price. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the power trading method provided in the above-mentioned embodiment, and will not be repeated here.

[0155] The present application also provides a computer program product, comprising a computer program, which implements the steps of the above-mentioned power trading method when executed by a processor.

[0156] The computer program product provided in this application can solve the technical problem of how to formulate a reasonable electricity price. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the electricity trading method provided in the above embodiment, which will not be repeated here.

[0157] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for power trading, characterized in that: The power trading method comprises: Upon receiving a power purchase request from a power demander, obtaining a plurality of historical power usage information of the area where the power demander is located; Performing power forecasting based on multiple historical power usage information of the area where the power demander is located to determine the predicted power quantity of the power demander; Determining a single purchase amount of electricity by the power demander based on the power purchase request; Determine the unit price of electricity sales according to the single purchase amount of electricity, the predicted amount of electricity and the electricity cost; Conducting electricity trading according to the electricity selling unit price; The step of performing power forecasting based on a plurality of historical power usage information of the area where the power demander is located to determine the predicted power quantity of the power demander includes: Determine the power usage data at each time point in each historical power usage information based on multiple historical power usage information of the area where the power demander is located; Determine the power usage sequence corresponding to each piece of historical power usage information according to the power usage data at each time point in each piece of historical power usage information; A prediction model is constructed based on multiple power usage sequences to obtain a power prediction model; Performing power forecasting on the power demander according to the power forecasting model to determine the predicted power quantity of the power demander; The prediction model is constructed based on multiple power usage sequences to obtain a power prediction model, including: determining extreme usage data of each power usage sequence based on a plurality of power usage sequences; The grey correlation coefficient of each power usage sequence is determined according to the extreme usage data of each power usage sequence. The grey correlation coefficient of each power usage sequence is , and is the extreme usage data of each power usage sequence, is the resolution coefficient, where is the power usage data at time point t in the 0th historical power usage information, Indicates The power usage data at time point t in the historical power usage information; Determine the electricity consumption characteristic information according to the grey correlation coefficient of each electricity use sequence; Extracting features from a plurality of power usage sequences according to the power usage feature information to determine a plurality of model training sequences; A prediction model is constructed based on multiple model training sequences to obtain a power prediction model; The step of determining the electricity selling price according to the single purchased electricity quantity, the predicted electricity quantity and the electricity cost includes: Determining an electricity quantity relationship according to the single purchased electricity quantity and the predicted electricity quantity; The electricity pricing coefficient is determined according to the electricity relationship and the pricing index, where the electricity relationship is the single purchase electricity Greater than or equal to the predicted power When the electricity pricing coefficient is , the power relationship is the single purchase power Less than the predicted power When the electricity pricing coefficient is ,in It is a preset pricing indicator; Calculate the unit price based on the electricity pricing coefficient and the electricity cost to determine the unit price of electricity sales; The unit price calculation based on the electricity pricing coefficient and the electricity cost to determine the electricity selling unit price includes: The initial price is determined according to the electricity pricing coefficient and the electricity cost, and the initial price is , is the electricity pricing coefficient, For electricity costs; Determining a profit from electricity sales based on the single purchase amount and the initial price; The initial pricing is adjusted according to the electricity selling profit and the electricity constraint condition to determine the electricity selling unit price. When the electricity selling profit does not reach the preset profit percentage, the profit pricing is determined according to the preset profit percentage. The electricity selling unit price is obtained according to the profit pricing and the initial pricing. ,in, The price is set for profit, and the electricity selling unit price satisfies the electricity constraint condition.

2. The power trading method according to claim 1, characterized in that: The obtaining of a plurality of historical power usage information of the area where the power demander is located includes: Determining the region where the power demander is located and the type of power consumption of the power demander according to the power purchase request of the power demander; Determining a plurality of electricity demanders according to the regions where the electricity demanders are located; Determining a plurality of target demand parties from a plurality of electricity demand parties according to the historical electricity purchase type of each electricity demand party and the electricity consumption type; Obtain the historical electricity usage information of each target demand party within a preset time period.

3. The power trading method according to claim 1, characterized in that: The step of performing power forecasting on the power demander according to the power forecasting model to determine the predicted power quantity of the power demander includes: Obtaining historical power usage information of the power demander within a preset time period; Determining a historical electricity usage sequence of the electricity demander based on historical electricity usage information of the electricity demander within a preset time period; The historical electricity consumption sequence is input into the electricity consumption prediction model to perform electricity prediction, and the predicted electricity consumption of the electricity demander is determined.

4. An electric power trading device, characterized in that: The power trading device comprises: An acquisition module, configured to acquire, upon receiving a purchase request for electricity from an electricity demander, a plurality of historical electricity usage information of an area where the electricity demander is located; A prediction module, configured to perform power prediction based on a plurality of historical power usage information of the area where the power demander is located, and determine the predicted power quantity of the power demander; A processing module, configured to determine a single purchase amount of electricity of the electricity demander based on the electricity purchase request; A processing module, used to determine a unit price of electricity sales according to the single purchase electricity quantity, the predicted electricity quantity and electricity cost; A trading module, used for conducting electricity trading according to the electricity selling unit price; The prediction module is further used to determine the power usage data at each time point in each historical power usage information based on multiple historical power usage information in the area where the power demander is located; determine the power usage sequence corresponding to each historical power usage information according to the power usage data at each time point in each historical power usage information; construct a prediction model based on multiple power usage sequences to obtain a power prediction model; perform power prediction on the power demander according to the power prediction model to determine the predicted power of the power demander; The prediction module is further used to determine the extreme usage data of each power usage sequence based on multiple power usage sequences; determine the grey correlation coefficient of each power usage sequence according to the extreme usage data of each power usage sequence, and the grey correlation coefficient of each power usage sequence is , and is the extreme usage data of each power usage sequence, is the resolution coefficient, where is the power usage data at time point t in the 0th historical power usage information, Indicates power usage data at time point t in historical power usage information; determining power usage feature information according to the grey correlation coefficient of each power usage sequence; extracting features from multiple power usage sequences according to the power usage feature information to determine multiple model training sequences; constructing a prediction model based on the multiple model training sequences to obtain a power prediction model; The processing module is further used to determine the power relationship according to the single purchase power and the predicted power; determine the power pricing coefficient according to the power relationship and the pricing index, where the power relationship is the single purchase power Greater than or equal to the predicted power When the electricity pricing coefficient is , the power relationship is the single purchase power Less than the predicted power When the electricity pricing coefficient is ,in is a preset pricing indicator; a unit price is calculated based on the electricity pricing coefficient and the electricity cost to determine the unit price of electricity sold; The processing module is further used to determine the initial price according to the electricity pricing coefficient and the electricity cost. The initial price is , is the electricity pricing coefficient, The power cost is determined according to the single purchase quantity and the initial price; the power selling profit is determined according to the power selling profit and the power constraint condition, and the power selling unit price is determined; when the power selling profit does not reach the preset profit percentage, the profit pricing is determined according to the preset profit percentage, and the power selling unit price is obtained according to the profit pricing and the initial pricing. ,in, The price is set for profit, and the electricity selling unit price satisfies the electricity constraint condition.

5. An electric power trading device, characterized in that: The power trading device comprises: a memory, a processor, and a power trading program stored in the memory and executable on the processor, wherein the power trading program is configured to implement the power trading method according to any one of claims 1 to 3.

6. A storage medium, characterized in that: The storage medium stores an electricity trading program, and when the electricity trading program is executed by the processor, the electricity trading method according to any one of claims 1 to 3 is implemented.

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