A method, device, electronic device and storage medium for predicting power consumption

Abstract

The present invention discloses a method, device, electronic device and storage medium for predicting power consumption. The method comprises: determining at least one first historical day information corresponding to a target object, the historical day information at least comprising temperature data and power consumption data, and each historical day information is different for each historical day; determining the temperature data of the predicted day corresponding to the target object; based on the temperature data of the predicted day, selecting at least one second historical day information from at least one first historical day information; and determining the power consumption data of the predicted day corresponding to the target object based on at least one second historical day information. By adopting the technical solution of the present application, when predicting the power consumption of the target object, the influence of temperature on power consumption can be fully considered, thereby ensuring the accuracy of the power consumption prediction result.

Description

Technical Field

[0001] The present invention relates to the technical field of power forecasting, and in particular to a method, device, electronic equipment and storage medium for predicting power consumption. Background Art

[0002] For power plants, the accuracy of short-term user load forecasting will become a key indicator of whether their energy is wasted. Therefore, a scientific and efficient power forecasting method is what every power plant expects.

[0003] When predicting user electricity consumption, existing methods often only consider the changes in electricity consumption data. This causes problems such as large deviations in prediction results when predicting user electricity consumption, affecting the power generation decisions of power plants. Summary of the invention

[0004] The present invention provides a method, device, electronic device and storage medium for predicting power consumption, so as to solve the problem that power consumption is difficult to predict, thus causing waste of power generation energy in power plants.

[0005] According to one aspect of the present invention, a method for predicting power consumption is provided, the method comprising:

[0006] Determine at least one first historical day information corresponding to the target object, the historical day information at least includes temperature data and electricity consumption data, and each historical day information is different from the historical day used;

[0007] Determine the predicted daily temperature data corresponding to the target object;

[0008] Filtering at least one second historical day information from at least one first historical day information according to the predicted daily temperature data;

[0009] According to at least one second historical day information, predicted daily electricity consumption data corresponding to the target object is determined.

[0010] According to another aspect of the present invention, there is provided a power consumption prediction device, the device comprising:

[0011] A first information determination module is used to determine at least one first historical day information corresponding to the target object, the historical day information at least including temperature data and power consumption data, and each historical day information is used for different historical days;

[0012] The predicted temperature determination module is used to determine the predicted daily temperature data corresponding to the target object;

[0013] A second information determination module, configured to filter out at least one second historical day information from at least one first historical day information according to the predicted daily temperature data;

[0014] The power consumption data determination module is used to determine the predicted daily power consumption data corresponding to the target object based on at least one second historical day information.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute the power consumption prediction method of any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the power consumption prediction method of any embodiment of the present invention when executed.

[0020] The technical solution of the embodiment of the present invention is to determine at least one first historical day information corresponding to the target object; determine the temperature data of the predicted day corresponding to the target object; based on the temperature data of the predicted day, filter out at least one second historical day information from at least one first historical day information; and finally determine the predicted daily electricity consumption data corresponding to the target object based on at least one second historical day information, so that when predicting the electricity consumption of the target object, the influence of the temperature on the electricity consumption can be fully considered, thereby ensuring the accuracy of the electricity consumption prediction result.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 is a flow chart of a method for predicting power consumption provided according to Embodiment 1 of the present invention;

[0024] Figure 2 is a flow chart of another power consumption prediction method provided according to the second embodiment of the present invention;

[0025] Figure 3 is a schematic diagram of the structure of a power consumption prediction device provided according to Embodiment 3 of the present invention;

[0026] Figure 4 It is a structural schematic diagram of an electronic device for implementing the power consumption prediction method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] Embodiment 1

[0030] Figure 1 A flowchart of a power consumption prediction method is provided for the first embodiment of the present invention. This embodiment is applicable to situations where it is necessary to predict the power consumption of a target object and adjust the power generation capacity of the power grid in a timely manner. The method can be executed by a power consumption prediction device, which can be implemented in the form of hardware and / or software. The power consumption prediction device can be configured in an electronic device with data processing capabilities. Figure 1 As shown, the method includes:

[0031] S110: Determine at least one first historical day information corresponding to the target object.

[0032] The historical daily information includes at least temperature data and electricity consumption data, and each historical daily information is different for each historical day.

[0033] The target object may be an object for which power consumption forecasting is required, including but not limited to users, factories, enterprises, and regions. The temperature data may be the temperature and humidity of the area where the target object is located. The power consumption data may be the power consumption of the target object within 24 hours.

[0034] When the target object uses electricity, different temperature environments will cause changes in electricity consumption. For example, electricity consumption in summer is often greater than that in winter. Therefore, the impact of temperature environment on electricity consumption is more important.

[0035] Therefore, when predicting the power consumption of a target object on a certain day, the first historical day information of the target object can be used to predict the power consumption of the target object according to the impact of temperature data on power consumption.

[0036] The temperature data in the first historical day information can be obtained from weather websites; the electricity consumption data can be provided by the target object.

[0037] In an optional solution, before determining at least one first historical day information corresponding to the target object, steps A1-A2 are further included:

[0038] Step A1: Determine at least one third historical day information corresponding to the target object.

[0039] Step A2: Screen at least one third historical day information to obtain at least one first historical day information.

[0040] Before determining at least one first historical day information corresponding to the target object, some data in the collected third historical day information may contain errors, such as abnormal situations such as excessive power consumption or zero power consumption. Therefore, before determining at least one third historical day information corresponding to the target object, it is necessary to screen each of the three historical day information to obtain at least one first historical day information.

[0041] In an optional solution, screening the information of at least one third historical day includes steps B1-B3:

[0042] Step B1: for each third historical day information, determine at least one hour of electricity consumption data.

[0043] Step B2: Determine the hourly electricity consumption variance based on each hourly electricity consumption data.

[0044] Step B3: If the hourly electricity consumption variance is greater than the preset variance, the information of the third historical day corresponding to the hourly electricity consumption variance is eliminated.

[0045] The hourly electricity consumption variance may be the variance between the electricity consumption in a certain hour within 24 hours and the electricity consumption in each hour within the 24 hours.

[0046] When filtering the information of each third historical day, the hourly electricity consumption variance can be used for determination.

[0047] The calculation of hourly electricity consumption variance can be done by the following formula:

[0048]

[0049] Where: Vi is the variance of electricity consumption; Xik is the electricity consumption of the i-th object at the k-th time point; is the average power consumption at 24 o'clock on the same day; k is the size of the data, which is generally 24.

[0050] The hourly electricity consumption variance mainly reflects the fluctuation of data. When a user's electricity consumption data fluctuates greatly, the electricity consumption at 24:00 on the same day is high and low, and the variance is large, then the user's electricity consumption is abnormal. Therefore, if the hourly electricity consumption variance is greater than the preset variance, the information corresponding to the third historical day of the hourly electricity consumption variance will be eliminated.

[0051] In an optional solution, screening the information of at least one third historical day includes steps C1-C2:

[0052] Step C1: for each third historical day information, determine the percentage of electricity consumption corresponding to each third historical day information that contains zero; the percentage of electricity consumption containing zero is the proportion of the time period with zero electricity consumption of the target object in the historical day to the historical day.

[0053] Step C2: If the percentage of electricity consumption containing zero is greater than the preset percentage, the third historical day information corresponding to the percentage of electricity consumption containing zero is eliminated.

[0054] In addition to using the hourly power consumption variance to filter at least one third historical day's information, it is also possible to filter by the power consumption containing zero percentage.

[0055] For the calculation of electricity consumption containing zero percentage, it can be calculated by the following formula:

[0056] Pzeroi=Xj / Xi;

[0057] Where Pzeroi is the percentage of zeros; Xj is the number of zeros in the i-th object at 24:00 on the same day; Xi is the total amount of data in the i-th object, which is usually 24.

[0058] Except for extremely special cases, if a user's electricity consumption is zero at a certain time point, then the possibility of abnormal electricity consumption behavior of the user is very high. If a user's electricity consumption is zero at more than a quarter of the time points, there is a high possibility of abnormal electricity consumption. If a user's electricity consumption is zero at half of the time points, there is a certain possibility of abnormal electricity consumption behavior. Therefore, when the percentage of electricity consumption containing zero is greater than the preset percentage, the third historical day information corresponding to the percentage of electricity consumption containing zero will be eliminated.

[0059] S120, determining the predicted daily temperature data corresponding to the target object.

[0060] S130. Filter out at least one second historical day information from at least one first historical day information according to the predicted daily temperature data.

[0061] After obtaining at least one first historical day information, similar historical day information may be screened out from each first historical day information according to the determined predicted daily temperature data as the second historical day information.

[0062] In an optional solution, at least one second historical day information is selected from at least one first historical day information according to the predicted daily temperature data, which may include steps D1-D3:

[0063] Step D1: Determine the date type of the predicted day and each first historical day, where the date type at least includes holidays, working days, Saturdays, and Sundays.

[0064] Step D2: determine the temperature data for the forecast day.

[0065] Step D3: According to the date type of the predicted day and the temperature data, at least one second historical day information is determined from each first historical day, and the date type of the historical day corresponding to the second historical day information is the same as the date type of the predicted day.

[0066] When filtering the second historical day information, you can filter according to the date type. For example, if the predicted day is Sunday and the temperature is 20 degrees Celsius, you can filter out the first historical day information that is also Sunday and the temperature data is close to 20 degrees Celsius from each first historical day as the second historical day information.

[0067] In addition to relying on a single date type, you can also combine multiple date types. For example, if the forecast date is Saturday and a holiday, you can combine these two conditions and filter based on the combined result.

[0068] S140: Determine predicted daily electricity consumption data corresponding to the target object based on at least one second historical day information.

[0069] After obtaining at least one second historical day information, the power consumption of the target object under the same temperature environment can be determined. At this time, the power consumption data of the target object on the predicted day can be predicted with the help of the power consumption in each second historical day information.

[0070] According to the technical solution of the embodiment of the present invention, by determining at least one first historical day information corresponding to the target object; determining the predicted day temperature data corresponding to the target object; based on the predicted day temperature data, screening out at least one second historical day information from at least one first historical day information; and finally determining the predicted day electricity consumption data corresponding to the target object based on at least one second historical day information, when predicting the electricity consumption of the target object, the impact of temperature on electricity consumption can be fully considered, thereby ensuring the accuracy of the electricity consumption prediction result.

[0071] Embodiment 2

[0072] Figure 2 The present invention provides a flowchart of another method for predicting power consumption. Based on the above embodiment, this embodiment further optimizes the process of determining the power consumption data of the target object corresponding to the predicted day according to at least one second historical day information in the above embodiment. This embodiment can be combined with various optional solutions in one or more of the above embodiments. Figure 2 As shown, the power consumption prediction method of this embodiment may include the following steps:

[0073] S210: Determine at least one first historical day information corresponding to the target object.

[0074] The historical daily information includes at least temperature data and electricity consumption data, and each historical daily information is different for each historical day.

[0075] S220, determining the predicted daily temperature data corresponding to the target object.

[0076] S230: Filter out at least one second historical day information from at least one first historical day information according to the predicted daily temperature data.

[0077] S240, performing arithmetic averaging on the electricity consumption data in each second historical day information to obtain the predicted daily electricity consumption data corresponding to the target object.

[0078] Based on at least one second historical day information, the predicted daily electricity consumption data corresponding to the target object is determined. To ensure the accuracy of the determination result, the arithmetic average and integer operation can be performed on the electricity consumption data in each second historical day information. At this time, the rounded result can be used as the predicted daily electricity consumption data corresponding to the target object.

[0079] According to the technical solution of the embodiment of the present invention, the predicted daily electricity consumption data corresponding to the target object is obtained by taking the arithmetic average of the electricity consumption data in each second historical day information, so that when using the electricity consumption data in each second historical day information, it is possible to ensure that each electricity consumption data is used more reasonably, thereby ensuring the accuracy of the final determined result.

[0080] Embodiment 3

[0081] Figure 3 The present invention provides a block diagram of a power consumption prediction device. This embodiment is applicable to situations where it is necessary to predict the power consumption of a target object and adjust the power generation capacity of a power grid in a timely manner. The power consumption prediction device can be implemented in the form of hardware and / or software, and the power consumption prediction device can be configured in an electronic device with data processing capabilities. Figure 3 As shown, the power consumption prediction device of this embodiment may include: a first information determination module 310, a predicted temperature determination module 320, a second information determination module 330 and a power consumption data determination module 340. Among them:

[0082] A first information determination module 310 is used to determine at least one first historical day information corresponding to the target object, the historical day information at least including temperature data and power consumption data, and each historical day information is used for different historical days;

[0083] The predicted temperature determination module 320 is used to determine the predicted daily temperature data corresponding to the target object;

[0084] A second information determination module 330 is used to filter out at least one second historical day information from at least one first historical day information according to the predicted daily temperature data;

[0085] The power consumption data determination module 340 is used to determine the predicted daily power consumption data corresponding to the target object according to at least one second historical day information.

[0086] Based on the above embodiment, optionally, the second information determination module 330 includes:

[0087] Determine the date type of the predicted day and each first historical day, and the date type at least includes holidays, working days, Saturdays and Sundays;

[0088] Determine the temperature data for the forecast day;

[0089] At least one second historical day information is determined from each first historical day according to the date type of the predicted day and the temperature data, and the date type of the historical day corresponding to the second historical day information is the same as the date type of the predicted day.

[0090] Based on the above embodiment, optionally, the power consumption data determination module 340 includes:

[0091] The electricity consumption data in each second historical day information is arithmetic averaged to obtain the electricity consumption data of the target object corresponding to the predicted day.

[0092] Based on the above embodiment, optionally, before the first information determination module 310, the following further includes:

[0093] Determine at least one third historical day information corresponding to the target object;

[0094] At least one third historical day information is screened to obtain at least one first historical day information.

[0095] Based on the above embodiment, optionally, filtering at least one third historical day information includes:

[0096] For each third historical day information, determine at least one hour of electricity consumption data;

[0097] According to each hourly electricity consumption data, determine the hourly electricity consumption variance;

[0098] If the hourly electricity consumption variance is greater than the preset variance, the information of the third historical day corresponding to the hourly electricity consumption variance is eliminated.

[0099] Based on the above embodiment, optionally, filtering at least one third historical day information includes:

[0100] For each third historical day information, determine the percentage of electricity consumption corresponding to each third historical day information that contains zero; the percentage of electricity consumption containing zero is the proportion of the time period in which the target object has zero electricity consumption in the historical day to the historical day;

[0101] If the percentage of electricity consumption containing zero is greater than the preset percentage, the third historical day information corresponding to the percentage of electricity consumption containing zero will be eliminated.

[0102] The power consumption prediction device provided in the embodiment of the present invention can execute the power consumption prediction method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0103] Embodiment 4

[0104] Figure 4A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0105] like Figure 4 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0106] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0107] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the power consumption prediction method.

[0108] In some embodiments, the power consumption prediction method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the power consumption prediction method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the power consumption prediction method in any other appropriate manner (e.g., by means of firmware).

[0109] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0110] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0111] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0112] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0113] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0114] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0115] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0116] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for predicting power consumption, characterized in that: include: Determine at least one first historical day information corresponding to the target object, wherein the historical day information includes at least temperature data and electricity consumption data, and each historical day information corresponds to a different historical day; Determine the predicted daily temperature data corresponding to the target object; Filtering at least one second historical day information from at least one first historical day information according to the predicted daily temperature data; According to at least one second historical day information, predicted daily electricity consumption data corresponding to the target object is determined.

2. The method according to claim 1, characterized in that According to the predicted daily temperature data, at least one second historical day information is selected from at least one first historical day information, including: Determine the date type of the predicted day and each first historical day, wherein the date type at least includes holidays, working days, Saturdays, and Sundays; Determine the temperature data for the forecast day; At least one second historical day information is determined from each first historical day according to the date type of the predicted day and the temperature data, wherein the date type of the historical day corresponding to the second historical day information is the same as the date type of the predicted day.

3. The method according to claim 1, characterized in that Determining predicted daily electricity consumption data corresponding to the target object according to at least one second historical day information includes: The electricity consumption data in each second historical day information is arithmetic averaged to obtain the predicted daily electricity consumption data of the target object.

4. The method according to claim 1, characterized in that: Before determining at least one first historical day information corresponding to the target object, the method further includes: Determine at least one third historical day information corresponding to the target object; At least one third historical day information is screened to obtain at least one first historical day information.

5. The method according to claim 4, characterized in that Filter at least one third historical day information, including: For each third historical day information, determine at least one hour of electricity consumption data; According to each hourly electricity consumption data, determine the hourly electricity consumption variance; If the hourly electricity consumption variance is greater than the preset variance, the information of the third historical day corresponding to the hourly electricity consumption variance is eliminated.

6. The method according to claim 4, characterized in that Filter at least one third historical day information, including: For each third historical day information, determine the percentage of electricity consumption corresponding to each third historical day information that contains zero; the percentage of electricity consumption containing zero is the proportion of the time period in which the target object has zero electricity consumption in the historical day to the historical day; If the percentage of electricity consumption containing zero is greater than the preset percentage, the third historical day information corresponding to the percentage of electricity consumption containing zero will be eliminated.

7. A power consumption prediction device, characterized in that: include: A first information determination module is used to determine at least one first historical day information corresponding to the target object, wherein the historical day information at least includes temperature data and power consumption data, and each historical day information is used for different historical days; The predicted temperature determination module is used to determine the predicted daily temperature data corresponding to the target object; A second information determination module, configured to filter out at least one second historical day information from at least one first historical day information according to the predicted daily temperature data; The power consumption data determination module is used to determine the predicted daily power consumption data corresponding to the target object based on at least one second historical day information.

8. The device according to claim 7, characterized in that The second information determination module includes: Determine the date type of the predicted day and each first historical day, wherein the date type at least includes holidays, working days, Saturdays, and Sundays; According to the date type of the predicted day, at least one second historical day information is determined from each first historical day, and the date type of the historical day corresponding to the second historical day information is the same as the date type of the predicted day.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the power consumption prediction method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the power consumption prediction method according to any one of claims 1 to 6 when executed.

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