A photovoltaic base station energy consumption estimation method and device

Abstract

The present application belongs to the field of mobile communication technology, and provides a photovoltaic base station energy consumption estimation method, device and electronic equipment, the method comprising: obtaining historical energy consumption data, estimating the estimated energy consumption in the preset time period after the current time according to the historical energy consumption data; obtaining historical photovoltaic power generation data, estimating the estimated power generation in the preset time period after the current time according to the historical photovoltaic power generation data; establishing an energy consumption warning model, and estimating whether the energy consumption in the preset time period meets the warning condition according to the estimated energy consumption and the estimated power generation. The present application uses the historical energy consumption data and the historical photovoltaic power generation data in the photovoltaic base station, establishes an energy consumption warning model to predict whether the power generation and the energy consumption of the photovoltaic base station are balanced in the future period of time, effectively estimates and predicts the electricity demand of the photovoltaic base station, and then reasonably plans and allocates the electricity of each module, ensures the long-term reliable operation of the base station, saves the energy consumption of the photovoltaic base station, and improves the power utilization rate.

Description

Technical Field

[0001] This invention belongs to the field of mobile communication technology, and more specifically relates to a method, apparatus, electronic device and computer-readable medium for predicting the energy consumption of photovoltaic base stations. Background Technology

[0002] With the rapid development of my country's telecommunications industry and the explosive growth of mobile terminal devices such as smartphones, major operators are continuously increasing their investment in hardware infrastructure. To expand communication coverage, operators are actively promoting the construction of base stations and achieving full signal coverage in remote areas. Currently, a base station generally includes the following modules: power supply equipment, main equipment, environmental equipment, and other auxiliary equipment. The energy consumption of each module in a base station is as follows:

[0003] (1) Power supply equipment: accounting for about 5% to 10% of the total energy consumption. Most base station power supplies are switching power supplies. Power supply equipment is cascaded with electrical equipment, and there is a cascading effect between them in terms of energy consumption, which plays an important role in the energy saving process of base stations.

[0004] (2) Main equipment: accounting for approximately 50% to 80% of the total energy consumption. Main equipment refers to the core of the base station, consisting of the base transceiver station, transmitting and receiving antennas, and the feeder system. Its main function is to receive, transmit, and process mobile signals. This part of the equipment consumes the most energy during base station communication.

[0005] (3) Environmental equipment: accounting for about 10% to 25% of the total energy consumption. It mainly includes equipment such as air conditioners and humidifiers, which are used to regulate the temperature and humidity in the equipment room. During the operation of the base station, the air conditioning system needs to be turned on frequently in order to regulate the temperature in the equipment room. The energy consumption of these devices is also not negligible, and it is only lower than the energy consumption of the main equipment.

[0006] (4) Other auxiliary equipment: accounting for about 5% to 15% of the total energy consumption. Most of them are modules that do not perform core functions, including providing light sources and ensuring operation.

[0007] Currently, there are three main power supply methods for remote tower base stations: (1) building new power supply circuits to achieve mains power supply; (2) using oil pumping machines to generate electricity on-site and provide continuous power supply; and (3) using new energy power generation and regulating continuous power supply through energy storage.

[0008] In remote areas of Northwest China, new energy base stations primarily employ the third approach: powering the base station equipment with solar or wind power and using batteries as backup energy storage to power various communication devices, achieving integrated wind, solar, and energy storage power supply. During the day, when weather conditions are good, the power generation is sufficient to meet the energy needs of the base station equipment. However, during cloudy or rainy weather or at night without sunlight, the power generation is insufficient to meet the equipment's energy requirements. Therefore, battery energy storage is needed as a backup power source for short periods. If the equipment load is high or backup power is used for extended periods, it can lead to over-discharge of the batteries, causing irreversible damage. During specific time periods, such as nighttime to early morning, the communication service volume accessed by the base station is generally low, especially in remote areas where base stations have no communication service access at all, yet the communication equipment remains fully operational, resulting in significant energy waste. In extreme weather conditions, such as prolonged cloudy or rainy weather, or insufficient sunlight, if there is a lack of effective planning for the energy storage of the new energy photovoltaic base station, the batteries will continuously discharge and eventually lose power, causing the photovoltaic base station to shut down due to power shortage, resulting in serious consequences. Summary of the Invention

[0009] (a) Technical problems to be solved

[0010] The present invention aims to solve the technical problem of how to accurately predict the energy consumption of photovoltaic base stations.

[0011] (II) Technical Solution

[0012] To address the aforementioned technical problems, one aspect of the present invention proposes a method for predicting the energy consumption of a photovoltaic base station, comprising:

[0013] Acquire historical energy consumption data, and estimate the estimated energy consumption within a preset time period from the current time based on the historical energy consumption data;

[0014] Obtain historical photovoltaic power generation data, and estimate the estimated power generation within a preset time period from the current time based on the historical photovoltaic power generation data;

[0015] An energy consumption early warning model is established to predict whether the energy consumption for the preset time period meets the early warning conditions based on the estimated energy consumption and estimated power generation.

[0016] According to a preferred embodiment of the present invention, the step of acquiring historical energy consumption data and estimating the estimated energy consumption within a preset time period after the current time based on the historical energy consumption data further includes:

[0017] Get the maximum daily energy consumption for the month preceding the current time;

[0018] Calculate the energy consumption level, which is the range of the ratio of energy consumption per unit time to the maximum energy consumption per unit time.

[0019] Calculate the estimated energy consumption level for a preset time period starting from the current time based on the energy consumption level.

[0020] According to a preferred embodiment of the present invention, the step of acquiring historical photovoltaic power generation data and estimating the estimated power generation within a preset time period after the current time based on the historical photovoltaic power generation data further includes:

[0021] Get daily historical weather information for the past four years and weather information for a preset time period starting from the current time;

[0022] Set the expected power generation value based on different historical weather information;

[0023] The estimated power generation during the preset time period is calculated based on the expected power generation value and the weather information for the preset time period starting from the current time.

[0024] According to a preferred embodiment of the present invention, setting the expected power generation value based on different historical weather information further includes:

[0025] The expected power generation value is set to 1 for sunny days, 0.5 for cloudy days, and 0.2 for rainy days, and is dynamically adjusted according to the actual situation.

[0026] According to a preferred embodiment of the present invention, the step of establishing an energy consumption early warning model and estimating whether the energy consumption for the preset time period meets the early warning conditions based on the estimated energy consumption and estimated power generation further includes:

[0027] Establish an energy consumption early warning model and input the calculated estimated energy consumption and estimated power generation into the energy consumption early warning model;

[0028] Obtain the current remaining stored power of the photovoltaic base station, set model indicators to determine whether the sum of the current remaining stored power and the estimated power generation within the preset time period is greater than the estimated energy consumption, and issue an early warning if it is not greater.

[0029] According to a preferred embodiment of the present invention, the step of setting model indicators to determine whether the sum of the current remaining stored power and the estimated power generation within the preset time period is greater than the estimated energy consumption further includes:

[0030] If the sum of the current remaining stored power and the estimated power generation within the preset time period is greater than the power required for the photovoltaic base station to operate at full load, then no warning is required;

[0031] If the sum of the current remaining stored power and the estimated power generation within the preset time period is less than the power required for the photovoltaic base station to operate at full load, but greater than the product of the energy consumption required to complete the minimum business volume within the preset time period and the estimated energy consumption level, an early warning will be issued and the base station will be controlled to operate in a low power mode.

[0032] If the sum of the current remaining storage power and the estimated power generation within the preset time period is less than the product of the energy consumption required to complete the minimum service volume within the preset time period and the estimated energy consumption level, an early warning will be issued and the base station will be controlled to increase power generation or reduce energy consumption.

[0033] According to a preferred embodiment of the present invention, the method further includes:

[0034] The historical energy consumption data and historical photovoltaic power generation data are updated regularly, as are the estimated energy consumption and estimated power generation.

[0035] The updated estimated energy consumption and estimated power generation are input into the energy consumption early warning model to update the parameters of the energy consumption early warning model.

[0036] A second aspect of the present invention provides a photovoltaic base station energy consumption prediction device, comprising:

[0037] The energy consumption acquisition module is used to acquire historical energy consumption data and estimate the estimated energy consumption within a preset time period after the current time based on the historical energy consumption data.

[0038] The power generation acquisition module is used to acquire historical photovoltaic power generation data and estimate the estimated power generation within a preset time period after the current time based on the historical photovoltaic power generation data.

[0039] The energy consumption early warning module is used to establish an energy consumption early warning model and estimate whether the energy consumption for the preset time period meets the early warning requirements based on the estimated energy consumption and estimated power generation.

[0040] A third aspect of the present invention provides an electronic device including a processor and a memory, the memory being used to store a computer-executable program, wherein when the computer program is executed by the processor, the processor performs the method described above.

[0041] A fourth aspect of the present invention also provides a computer-readable medium storing a computer-executable program, which, when executed, implements the above-described method.

[0042] (III) Beneficial Effects

[0043] This invention utilizes historical energy consumption data and historical photovoltaic power generation data from photovoltaic base stations to establish an energy consumption early warning model to predict whether the power generation and energy consumption of photovoltaic base stations will be balanced in the future. It effectively estimates and predicts the power demand of photovoltaic base stations, and then rationally plans and allocates power to each module, ensuring the long-term reliable operation of the base station, saving energy consumption of photovoltaic base stations and improving power utilization. Attached Figure Description

[0044] Figure 1 This is a schematic flowchart of a photovoltaic base station energy consumption prediction method according to an embodiment of the present invention;

[0045] Figure 2 This is a schematic diagram of a photovoltaic base station energy consumption prediction device according to an embodiment of the present invention;

[0046] Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention;

[0047] Figure 4 This is a schematic diagram of a computer-readable recording medium according to an embodiment of the present invention. Detailed Implementation

[0048] In the description of specific embodiments, detailed descriptions of structures, performance, effects, or other features are provided to enable those skilled in the art to fully understand the embodiments. However, this does not preclude those skilled in the art from implementing the present invention with technical solutions that do not contain the aforementioned structures, performance, effects, or other features under specific circumstances.

[0049] The flowcharts in the accompanying drawings are merely illustrative examples and do not imply that the solution of this invention must include all the content, operations, and steps shown in the flowcharts, nor do they imply that the execution must be performed in the order shown in the diagrams. For example, some operations / steps in the flowcharts can be decomposed, some operations / steps can be combined or partially combined, etc. Without departing from the inventive spirit of this invention, the execution order shown in the flowcharts can be changed according to the actual situation.

[0050] The box in the attached diagram Figure 1 Generally, these refer to functional entities, and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processing unit devices and / or microcontroller devices.

[0051] The same reference numerals in the accompanying drawings denote the same or similar elements, components, or parts, and therefore, repeated descriptions of the same or similar elements, components, or parts may be omitted below. It should also be understood that although terms such as first, second, third, etc., may be used in this invention to describe various devices, elements, components, or parts, these devices, elements, components, or parts should not be limited by these terms. That is, these terms are only used to distinguish one from another. For example, a first device may also be referred to as a second device, without departing from the essential technical solution of this invention. Furthermore, the terms "and / or" and "and / or" refer to all combinations including any one or more of the listed items.

[0052] In the existing technology, the construction of new energy base stations in remote areas is still relatively backward in terms of energy saving and battery protection. The commonly used methods are as follows: (1) Using intelligent circuit breakers to realize remote control of power on and off. This solution has a single function and is relatively backward in terms of intelligence. It can only be manually controlled and cannot be automatically controlled. (2) Automatically switching on and off load equipment through battery voltage detection devices. This solution can achieve the purpose of protecting the battery. However, the level of intelligence is also relatively low. Power off conditions cannot be configured separately for each household. It cannot distinguish between important loads and non-important loads and cannot achieve differentiated backup power function. (3) It is impossible to effectively predict the electricity demand and photovoltaic power generation of photovoltaic base stations within a predetermined time window in the future, so as to rationally plan and allocate electricity to each module.

[0053] To address the aforementioned technical problems, this invention proposes a method for handling weak links in the power grid. By utilizing historical energy consumption data and historical photovoltaic power generation data from photovoltaic base stations, an energy consumption early warning model is established to predict whether the power generation and energy consumption of photovoltaic base stations will be balanced in the future. This effectively estimates and predicts the electricity demand of photovoltaic base stations, and then rationally plans and allocates electricity to each module, ensuring the long-term reliable operation of the base stations, saving energy consumption of photovoltaic base stations, and improving power utilization.

[0054] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0055] Figure 1 This is a schematic diagram of a photovoltaic base station energy consumption prediction method according to an embodiment of the present invention.

[0056] like Figure 1 As shown, this method includes:

[0057] S101. Obtain historical energy consumption data, and estimate the estimated energy consumption within a preset time period after the current time based on the historical energy consumption data.

[0058] In some embodiments, historical energy consumption data is first obtained, which may start from the current time. Daily business volume information for the previous month (calculated as four weeks) is obtained from the server, and the maximum daily business volume S per unit time for the previous four weeks is calculated. max (j,l), j = 1, 2, 3, 4, representing the week number, l = 1, 2, 3, ..., 7, representing the number of days in a week, setting the energy consumption level L. S This represents the ratio of energy consumed to complete the workload within a unit of time (taking 1 hour as an example) to the energy consumed to complete the maximum workload within that unit of time, expressed by the following formula:

[0059]

[0060] Among them, S uThis represents the energy consumption (S) required to complete the workload within a unit of time (1 hour) in a day. max This represents the energy consumption required to complete the maximum workload within a unit of time (1 hour) in a day. The energy consumption level L for each day and hour is calculated for the first 28 days. S (i,j,l), where i = 0, 1, 2, ..., 23, represents the number of hours, and the energy consumption level per hour per day over the past four weeks is obtained as L. S (i,J,l)=S u (i,J,l) / S max (j,l), in this embodiment, a total of 5 energy consumption levels are divided, and each energy consumption level has a different range. For example, L is the energy consumption level of the 15th hour of the 25th day in the first 28 days. S If the value of (i,J,l) is 0.8, then the energy consumption level of the 15th hour is level 4.

[0061] Based on historical data, the expected energy consumption level (L) for each day and hour over the next two weeks is calculated using the following formula. S-f (i,l), (taking a week as an example):

[0062]

[0063] The expected daily / hourly energy consumption level (L) for the next two weeks is obtained by weighting and averaging the energy consumption levels for each day and each hour over the previous 28 days. S-f (i,l).

[0064] S102. Obtain historical photovoltaic power generation data, and estimate the estimated power generation within a preset time period after the current time based on the historical photovoltaic power generation data.

[0065] In some embodiments, to obtain historical power generation data of photovoltaic base stations, the system first retrieves historical weather information from the server for the two weeks following the current date over the past four years, and converts it into historical expected photovoltaic power generation P. CP (m,k), k=1,2,3,4, representing the year, m=0,1,2….13, representing the number of days in two weeks. Then, based on weather forecast information, the daily weather for the next two weeks is obtained and converted into the expected photovoltaic power generation P. CF (m), during the conversion process, the expected power generation value is set according to different historical weather information. For example, the expected power generation value is set to 1 for sunny days, 0.5 for cloudy days, and 0.2 for rainy days, and is dynamically adjusted according to the actual situation.

[0066] After setting the historical expected power generation values, calculate the average expected photovoltaic power generation for each day over the next two weeks using the following formula:

[0067]

[0068] For example, if the current day is December 3rd, and we want to obtain the expected power generation for December 6th, we can use a formula to add the historical expected power generation values ​​for December 6th of each of the past four years, and then add them to the expected power generation value for the future December 6th converted from weather forecasts. We get a total of 5 expected values, and then divide them by 5 to get the average expected value. We can then use this method to obtain the expected power generation value for each day in the next two weeks.

[0069] After obtaining the expected daily power generation values ​​for the next two weeks, the estimated power generation expected by the photovoltaic base station for the next two weeks is calculated. First, the maximum daily power generation E of the photovoltaic base station is obtained. C Then, calculate the estimated power generation E for the next two weeks using the following formula. Total :

[0070]

[0071] That is, the product of the average expected photovoltaic power generation per day over two weeks and the maximum power generation of the photovoltaic base station in a single day, multiplied by 14 days.

[0072] S103. Establish an energy consumption early warning model, and estimate whether the energy consumption in the preset time period meets the early warning conditions based on the estimated energy consumption and estimated power generation.

[0073] In some embodiments, an energy consumption early warning model is established, and the estimated energy consumption and estimated power generation calculated in the previous steps are input into the energy consumption early warning model.

[0074] Obtain the current remaining stored power C of the photovoltaic base station GB-C This refers to the current remaining power of the base station's battery pack. A model indicator is set to determine whether the sum of the current remaining stored power and the estimated power generation within the preset time period is greater than the estimated energy consumption. If it is not greater, an early warning is issued.

[0075] Preferably, the model metrics in the embodiments of the present invention include:

[0076] 1) If This means that the sum of the current remaining stored power of the base station and the estimated power generation in the next two weeks is greater than the energy consumption Q required for the photovoltaic base station to operate at full load for one day. BS Multiplying by 14 days indicates that there will be sufficient power generation in the next two weeks, there will be no energy warning, and the system will operate normally.

[0077] 2) If This means that the sum of the current remaining stored power of the base station and the estimated power generation in the next two weeks is less than the energy consumption Q required for the photovoltaic base station to operate at full load for one day. BS Multiply by 14 days, but greater than the energy consumption E required to complete the minimum business volume within the next two weeks. minThe product of the energy consumption level and the estimated energy consumption level requires a mild warning, indicating that although there may be insufficient energy in the next two weeks, the system can operate with low power consumption based on the original business volume model.

[0078] 3) If This means that the sum of the base station's current remaining storage capacity and the estimated power generation over the next two weeks is less than the energy consumption E required to complete the minimum service volume over the next two weeks. min The product of the estimated energy consumption level and the energy consumption E required to complete the minimum business volume within the next week. min The product of the energy consumption level and the estimated energy consumption level indicates that there will be insufficient energy in the next two weeks, requiring a poisoning warning. The system can only guarantee low power consumption operation for one week based on the original business volume model. If normal operation is to be guaranteed for two weeks, energy consumption needs to be reduced or power generation needs to be increased.

[0079] 4) If the sum of the current remaining storage power and the estimated power generation in the next two weeks is less than the product of the energy consumption required to complete the minimum business volume in the next week and the estimated energy consumption level, it indicates that there will be a severe energy shortage in the next two weeks. Even if the system refers to the original business volume model, it will not be able to guarantee low power operation for one week. A severe warning is required, and the administrator should discuss solutions.

[0080] Preferably, the historical energy consumption data and historical photovoltaic power generation data are updated periodically, and the estimated energy consumption and estimated power generation are also updated. The updated estimated energy consumption and estimated power generation are then input into the energy consumption early warning model to update the parameters of the energy consumption early warning model, so as to ensure the accuracy of the model.

[0081] This invention utilizes historical energy consumption data and historical photovoltaic power generation data from photovoltaic base stations to establish an energy consumption early warning model to predict whether the power generation and energy consumption of photovoltaic base stations will be balanced in the future. It effectively estimates and predicts the power demand of photovoltaic base stations, and then rationally plans and allocates power to each module, ensuring the long-term reliable operation of the base station, saving energy consumption of photovoltaic base stations and improving power utilization.

[0082] Those skilled in the art will understand that all or part of the steps of the above embodiments are implemented as a program (computer program) executed by a computer data processing device. When the computer program is executed, the method provided by the present invention can be implemented. Moreover, the computer program can be stored in a computer-readable storage medium, which can be a disk, optical disk, ROM, RAM, or other readable storage medium, or a storage array composed of multiple storage media, such as a disk or magnetic tape storage array. The storage medium is not limited to centralized storage; it can also be distributed storage, such as cloud storage based on cloud computing.

[0083] The following describes an embodiment of the apparatus of the present invention, which can be used to perform the method embodiments of the present invention. The details described in the apparatus embodiments of the present invention should be considered as supplements to the above method embodiments; details not disclosed in the apparatus embodiments of the present invention can be implemented with reference to the above method embodiments.

[0084] Figure 2 This is a schematic diagram of a photovoltaic base station energy consumption prediction device according to an embodiment of the present invention, as shown below. Figure 2 As shown, the device 200 includes:

[0085] The energy consumption acquisition module 201 is used to acquire historical energy consumption data and estimate the estimated energy consumption in a preset time period after the current time based on the historical energy consumption data.

[0086] The power generation acquisition module 202 is used to acquire historical photovoltaic power generation data and estimate the estimated power generation within a preset time period after the current time based on the historical photovoltaic power generation data.

[0087] The energy consumption early warning module 203 is used to establish an energy consumption early warning model and estimate whether the energy consumption during the preset time period meets the early warning requirements based on the estimated energy consumption and estimated power generation.

[0088] The energy consumption acquisition module 201 further includes:

[0089] The historical energy consumption acquisition unit is used to acquire the maximum daily energy consumption within the previous month at the current time;

[0090] The historical energy consumption level calculation unit is used to calculate the range in which the energy consumption level is the ratio of the energy consumption per unit time to the maximum energy consumption per unit time.

[0091] An energy consumption level estimation unit is used to calculate the estimated energy consumption level for a preset time period after the current time based on the energy consumption level.

[0092] According to a preferred embodiment of the present invention, the power generation acquisition module 202 further includes:

[0093] The weather information acquisition unit is used to acquire daily historical weather information for the four years prior to the current time and weather information for a preset time period starting from the current time.

[0094] The power generation expectation calculation and setting unit is used to set the power generation expectation value based on different historical weather information.

[0095] The power generation estimation unit is used to calculate the estimated power generation within the preset time period based on the expected power generation value and weather information for the preset time period after the current time.

[0096] According to a preferred embodiment of the present invention, the power generation expectation calculation and setting unit is further configured to: set the power generation expectation value for sunny days to 1, the power generation expectation value for cloudy days to 0.5, and the power generation expectation value for rainy days to 0.2, and dynamically adjust it according to the actual situation.

[0097] According to a preferred embodiment of the present invention, the energy consumption early warning module 203 further includes:

[0098] The model building unit is used to build an energy consumption early warning model, and inputs the calculated estimated energy consumption and estimated power generation into the energy consumption early warning model.

[0099] The early warning unit is used to obtain the current remaining stored power of the photovoltaic base station, set model indicators to determine whether the sum of the current remaining stored power and the estimated power generation within the preset time period is greater than the estimated energy consumption, and if it is not greater, an early warning is issued.

[0100] According to a preferred embodiment of the present invention, the early warning unit is further configured to:

[0101] If the sum of the current remaining stored power and the estimated power generation within the preset time period is greater than the power required for the photovoltaic base station to operate at full load, then no warning is required;

[0102] If the sum of the current remaining stored power and the estimated power generation within the preset time period is less than the power required for the photovoltaic base station to operate at full load, but greater than the product of the energy consumption required to complete the minimum business volume within the preset time period and the estimated energy consumption level, an early warning will be issued and the base station will be controlled to operate in a low power mode.

[0103] If the sum of the current remaining storage power and the estimated power generation within the preset time period is less than the product of the energy consumption required to complete the minimum service volume within the preset time period and the estimated energy consumption level, an early warning will be issued and the base station will be controlled to increase power generation or reduce energy consumption.

[0104] According to a preferred embodiment of the present invention, the device further includes a model update module for periodically updating the historical energy consumption data and historical photovoltaic power generation data, and updating the estimated energy consumption and estimated power generation.

[0105] The updated estimated energy consumption and estimated power generation are input into the energy consumption early warning model to update the parameters of the energy consumption early warning model.

[0106] Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. The electronic device includes a processor and a memory. The memory is used to store a computer-executable program. When the computer program is executed by the processor, the processor executes a method for handling weak links in the power grid.

[0107] like Figure 3As shown, the electronic device is embodied in the form of a general-purpose computing device. There can be one or more processors working collaboratively. This invention also does not preclude distributed processing, meaning that processors can be distributed across different physical devices. The electronic device of this invention is not limited to a single entity, but can also be the sum of multiple physical devices.

[0108] The memory stores a computer-executable program, typically machine-readable code. The computer-readable program can be executed by the processor to enable the electronic device to perform the method of the present invention, or at least some steps of the method.

[0109] The memory includes volatile memory, such as random access memory (RAM) and / or cache memory, and may also be non-volatile memory, such as read-only memory (ROM).

[0110] Optionally, in this embodiment, the electronic device further includes an I / O interface for exchanging data with external devices. The I / O interface can represent one or more of several bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0111] It should be understood that Figure 3 The electronic device shown is merely one example of the present invention, and the electronic device of the present invention may also include elements or components not shown in the above examples. For example, some electronic devices also include display units such as displays, and some electronic devices also include human-computer interaction elements such as buttons and keyboards. Any electronic device capable of executing a computer-readable program in memory to implement the method of the present invention or at least some steps of the method can be considered as an electronic device covered by the present invention.

[0112] Figure 4 This is a schematic diagram of a computer-readable recording medium according to an embodiment of the present invention. Figure 4As shown, a computer-readable recording medium stores a computer-executable program, which, when executed, implements the power grid weak link processing method described above. The computer-readable storage medium may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0113] The aforementioned computer-readable medium carries one or more programs, which, when executed by a device, cause the computer-readable medium to perform the following functions: acquire historical energy consumption data and estimate the estimated energy consumption within a preset time period after the current time based on the historical energy consumption data; acquire historical photovoltaic power generation data and estimate the estimated power generation within a preset time period after the current time based on the historical photovoltaic power generation data; establish an energy consumption early warning model and estimate whether the energy consumption within the preset time period meets the early warning conditions based on the estimated energy consumption and the estimated power generation.

[0114] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0115] From the above description of the embodiments, those skilled in the art will readily understand that the present invention can be implemented by hardware capable of executing specific computer programs, such as the system of the present invention, and the electronic processing unit, server, client, mobile phone, control unit, processor, etc. included in the system. The present invention can also be implemented by computer software that executes the methods of the present invention. However, it should be noted that the computer software executing the methods of the present invention is not limited to execution in one or a specific set of hardware entities; it can also be implemented in a distributed manner by unspecified hardware. For example, some method steps executed by the computer program can be executed in a mobile client, while others can be executed in a smart watch, smart pen, etc. For computer software, the software product can be stored in a computer-readable storage medium (such as a CD-ROM, USB flash drive, portable hard drive, etc.) or distributed across a network, as long as it enables electronic devices to execute the methods according to the present invention.

[0116] In summary, this invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that in practice, general-purpose data processing devices such as microprocessors or digital signal processors (DSPs) can be used to implement some or all of the functions of some or all of the components according to the embodiments of the invention. The invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the invention can be stored on a computer-readable medium or can take the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0117] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the present invention is not inherently related to any specific computer, virtual device, or electronic device, and various general-purpose devices can also implement the present invention. The above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for predicting the energy consumption of a photovoltaic base station, characterized in that, include: S101. Obtain historical energy consumption data, and estimate the estimated energy consumption within a preset time period after the current time based on the historical energy consumption data. The step of acquiring historical energy consumption data and estimating the estimated energy consumption within a preset time period from the current time based on the historical energy consumption data further includes: Get the maximum daily energy consumption for the month preceding the current time; Calculate the energy consumption level, which is the range of the ratio of energy consumption per unit time to the maximum energy consumption per unit time; Calculate the estimated energy consumption level for a preset time period starting from the current time based on the energy consumption level; S102. Obtain historical photovoltaic power generation data, and estimate the estimated power generation within a preset time period after the current time based on the historical photovoltaic power generation data; The step of acquiring historical photovoltaic power generation data and estimating the estimated power generation within a preset time period from the current time based on the historical photovoltaic power generation data further includes: Get daily historical weather information for the past four years and weather information for a preset time period starting from the current time; Set the expected power generation value based on different historical weather information; The estimated power generation within the preset time period is calculated based on the expected power generation value and the weather information for the preset time period after the current time. S103. Establish an energy consumption early warning model, and estimate whether the energy consumption in the preset time period meets the early warning conditions based on the estimated energy consumption and estimated power generation. The step of establishing an energy consumption early warning model, and estimating whether the energy consumption for the preset time period meets the early warning conditions based on the estimated energy consumption and estimated power generation, further includes: Establish an energy consumption early warning model and input the calculated estimated energy consumption and estimated power generation into the energy consumption early warning model; Obtain the current remaining stored power of the photovoltaic base station, set model indicators to determine whether the sum of the current remaining stored power and the estimated power generation within the preset time period is greater than the estimated energy consumption, and issue an early warning if it is not greater.

2. The photovoltaic base station energy consumption prediction method according to claim 1, characterized in that, The step of setting the expected power generation value based on different historical weather information further includes: The expected power generation value is set to 1 for sunny days, 0.5 for cloudy days, and 0.2 for rainy days, and is dynamically adjusted according to the actual situation.

3. The photovoltaic base station energy consumption prediction method according to claim 1, characterized in that, The step of setting model indicators to determine whether the sum of the current remaining stored power and the estimated power generation within the preset time period is greater than the estimated energy consumption further includes: If the sum of the current remaining stored power and the estimated power generation within the preset time period is greater than the power required for the photovoltaic base station to operate at full load, then no warning is required; If the sum of the current remaining stored power and the estimated power generation within the preset time period is less than the power required for the photovoltaic base station to operate at full load, but greater than the product of the energy consumption required to complete the minimum business volume within the preset time period and the estimated energy consumption level, an early warning will be issued and the base station will be controlled to operate in a low power mode. If the sum of the current remaining storage power and the estimated power generation within the preset time period is less than the product of the energy consumption required to complete the minimum service volume within the preset time period and the estimated energy consumption level, an early warning will be issued and the base station will be controlled to increase power generation or reduce energy consumption.

4. The photovoltaic base station energy consumption prediction method according to claim 1, characterized in that, The method further includes: The historical energy consumption data and historical photovoltaic power generation data are updated regularly, as are the estimated energy consumption and estimated power generation. The updated estimated energy consumption and estimated power generation are input into the energy consumption early warning model to update the parameters of the energy consumption early warning model.

5. A photovoltaic base station energy consumption prediction device, characterized in that, include: An energy consumption acquisition module (201) is used to acquire historical energy consumption data and estimate the estimated energy consumption within a preset time period after the current time based on the historical energy consumption data; wherein, the energy consumption acquisition module (201) further includes: Historical energy consumption acquisition unit is used to acquire the maximum daily energy consumption within the previous month at the current time; The historical energy consumption level calculation unit is used to calculate the range in which the energy consumption level is the ratio of the energy consumption per unit time to the maximum energy consumption per unit time. An energy consumption level estimation unit is used to calculate the estimated energy consumption level for a preset time period after the current time based on the energy consumption level. A power generation acquisition module (202) is used to acquire historical photovoltaic power generation data and estimate the estimated power generation within a preset time period after the current time based on the historical photovoltaic power generation data; wherein, the power generation acquisition module (202) further includes: The weather information acquisition unit is used to acquire daily historical weather information for the four years prior to the current time and weather information for a preset time period starting from the current time. The power generation expectation calculation and setting unit is used to set the power generation expectation value based on different historical weather information. The power generation estimation unit is used to calculate the estimated power generation within the preset time period based on the expected power generation value and weather information for the preset time period after the current time. An energy consumption early warning module (203) is used to establish an energy consumption early warning model and estimate whether the energy consumption for the preset time period meets the early warning requirements based on the estimated energy consumption and estimated power generation; wherein, the energy consumption early warning module (203) further includes: The model building unit is used to build an energy consumption early warning model, and inputs the calculated estimated energy consumption and estimated power generation into the energy consumption early warning model. The early warning unit is used to obtain the current remaining stored power of the photovoltaic base station, set model indicators to determine whether the sum of the current remaining stored power and the estimated power generation within the preset time period is greater than the estimated energy consumption, and if it is not greater, an early warning is issued.

6. An electronic device comprising a processor and a memory, the memory being used to store a computer-executable program, characterized in that, When the computer program is executed by the processor, the processor performs the method as described in any one of claims 1-4.

7. A computer-readable medium storing a computer-executable program, characterized in that, When the computer executable program is executed, it implements the method as described in any one of claims 1-4.

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