CN112651563A - Load prediction method and device, computer readable storage medium and electronic equipment - Google Patents
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Abstract
The invention is suitable for the technical field of energy, and provides a load prediction method, a device, a computer readable storage medium and electronic equipment, wherein the load prediction method comprises the following steps: acquiring basic load data of a user on a prediction day and load movement evaluation data of the prediction day; determining a load movement decision value corresponding to the reference load movement duration according to the reference load movement duration, the basic load data and the load movement evaluation data; determining a target load moving duration according to the load moving decision value corresponding to each reference load moving duration; and determining the target load data of the forecast day according to the target load moving duration and the basic load data. By the technical scheme, the load data of the user can be predicted more accurately.
Description
Technical Field
The invention belongs to the technical field of energy, and particularly relates to a load prediction method and device, a computer readable storage medium and electronic equipment.
Background
The load prediction is to scientifically predict the load of hours, days or months in the future according to the historical load change rule and by combining factors such as weather, temperature, economy, politics and the like.
At present, the influence of objective conditions on the load is mainly considered, so that data mining is carried out, the rule of load change is found, and load prediction is realized.
However, as the off-the-shelf power market is opened, the uncertainty of the load is gradually increased, and the accuracy of the load prediction result is low.
Disclosure of Invention
In view of this, embodiments of the present invention provide a load prediction method and apparatus, a computer-readable storage medium, and an electronic device, so as to solve the problem of low load prediction accuracy in the prior art.
A first aspect of an embodiment of the present invention provides a load prediction method, including:
acquiring basic load data of a user on a prediction day and load movement evaluation data of the prediction day;
determining a load movement decision value corresponding to the reference load movement duration according to the reference load movement duration, the basic load data and the load movement evaluation data;
determining a target load moving duration according to the load moving decision value corresponding to each reference load moving duration;
and determining the target load data of the forecast day according to the target load moving duration and the basic load data.
A second aspect of an embodiment of the present invention provides a load prediction apparatus, including:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring basic load data of a user on a prediction day and load movement evaluation data of the prediction day;
a decision value determining module, configured to determine a load movement decision value corresponding to a reference load movement duration according to the reference load movement duration, the basic load data, and the load movement evaluation data;
a time length determining module, configured to determine a target load movement time length according to the load movement decision value corresponding to each of the reference load movement time lengths;
and the prediction module is used for determining the target load data of the prediction day according to the target load moving duration and the basic load data.
A third aspect of an embodiment of the present invention provides an electronic device, including: comprising a memory, a processor and a computer program stored in said memory and executable on said processor, said processor implementing the steps of the method according to the first aspect when executing said computer program.
A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium, including: the computer readable storage medium stores a computer program which, when executed by a processor, performs the steps of the method according to the first aspect.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
the basic load data are moved by considering the load moving time length, so that the predicted load data with higher accuracy is obtained, the production practice is guided, and the energy allocation is optimized.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flow chart of a load prediction method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of another load prediction method according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a load prediction apparatus according to an embodiment of the present invention;
fig. 4 is a schematic diagram of an electronic device provided in an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
The method provided by the embodiment of the invention can be applied to electronic equipment, and can be particularly applied to a server or a general computer, which is not limited herein. The embodiment of the present invention is described with an electronic device as an execution subject. Fig. 1 is a schematic flow chart of a method according to an embodiment of the present invention. The method provided by the embodiment of the invention can comprise the following steps:
The basic load data refers to the load obtained by simply performing load prediction through a historical rule, and it should be understood that the method is particularly suitable for predicting the medium and long-term load, the load prediction can be realized based on a load prediction method, and the basic load data of the prediction day is obtained; wherein the load prediction method comprises the existing load prediction method and the future development, the existing load prediction method comprises a trend analysis method, a regression analysis method, a gray system method and a neural network method, wherein, the trend analysis method is realized by adopting a trend model, the trend model can be a linear trend model, a polynomial trend model, a logarithmic trend model, a power function trend model or an exponential trend model, the regression analysis method is used for determining the relation or the numerical value among the influencing factors, and the method mainly utilizes the historical data or the data of the electricity consumption in a certain area to simply analyze and count the influencing factors, thereby obtaining a series of functional relational expressions, a gray system method is a gray prediction technology based on the gray system theory, the load prediction model can be built by finding out a certain change which has influence in a period of time under the condition that the data is not much. Of course, in practical applications, a person skilled in the art may also predict the load on the prediction day by using other load prediction methods, and the embodiment of the present invention does not limit what prediction method is used to predict the load on the prediction day. Optionally, the base load data includes at least hour load data corresponding to a forecast day, i.e., loads for each 24 hours in the forecast day, and in most cases, may further include hour load data corresponding to a forecast day before the day and hour load data within a forecast day after the day so as to facilitate load shifting. It should be understood that the basic load data specifically needs to include how many days of hour load data around the forecast day needs to be determined by combining the actual situation, which is not specifically limited in this embodiment.
The load movement evaluation data refers to data for determining whether or not the load will move within the prediction day. Specifically, the load movement evaluation data may include an hour spot electricity price corresponding to a predicted day, that is, a spot electricity price of each 24 hours in the predicted day, where the spot electricity price is predicted based on a price prediction method, where the price prediction method is the prior art, and will not be described herein in any detail.
And step 102, determining a load movement decision value corresponding to the reference load movement duration according to the reference load movement duration, the basic load data and the load movement evaluation data.
In this embodiment, the load movement decision value corresponding to the reference load movement duration is determined by referring to the load movement duration, the basic load data, and the load movement evaluation data. And the load movement determining value corresponding to the reference load movement time length is used for determining whether the load can move. The reference load movement time period refers to a time period in which the load moves forward or backward, in other words, for each time point of the prediction day, a load corresponding to a time point after the time point moves according to the reference load movement time period is taken as the load at the time point.
As a possible implementation manner, the load movement decision value may be determined specifically by the following method:
moving the basic load data according to the reference load moving duration to determine reference load data corresponding to the reference load moving duration; and determining a load movement decision value corresponding to the reference load movement duration according to the load movement evaluation data and the reference load data corresponding to the reference load movement duration.
Specifically, the reference load data includes hour load data corresponding to a prediction day, that is, loads corresponding to respective hours in the prediction day, the reference load data is obtained after the basic load data is moved, and the reference load data corresponding to the movement time of the reference load can be calculated by the following formula (1):
wherein the content of the first and second substances,
characterizing the load, Q, of the jth hour of the prediction day corresponding to the reference load movement duration tk(j-t)And (4) representing the load of the (j-t) th hour in the prediction day k, and representing the meanings of other data items similarly, which are not described in detail herein. In practical applications, the reference load shifting duration t ranges from [ -11, 12 [ -11 [ ]]Positive numbers indicate load backward movement, and negative numbers indicate load forward movement. It should be understood that when T ≧ 0, j ≦ T, Qk(24+j-t)Represents the predicted load on the day before the day; when T is less than 0 and j-T is more than 24, Qk(j-T-24)Is to predict the load of the day after.
Specifically, the load movement determination value corresponding to the reference load movement time period may be calculated by the following formula (2):
wherein the content of the first and second substances,
the load movement determination value at the prediction day k corresponds to the reference load movement time t, which can be understood as an electric charge,
representing the spot electricity price at the jth moment;
and characterizing the load of the jth hour in the prediction day corresponding to the reference load moving time t.
And 103, determining the target load moving time length according to the load moving decision value corresponding to each reference load moving time length.
Alternatively, the target load movement duration may be specifically determined by the following method:
acquiring the load movement probability of the user; determining a load movement threshold according to the load movement probability of the user and the minimum value of the load movement decision value; and determining the target load moving time length according to the load moving threshold and the load moving decision value corresponding to each reference load moving time length.
In this embodiment, the load movement probability may be understood as a possibility that the load of the user moves, and the load movement threshold may be understood as a threshold for determining whether the load moves, and the load movement decision value is screened based on the load movement threshold to determine the target load movement duration, so that when the load movement probability is higher, the load movement duration is determined, and accuracy of load prediction is further ensured.
As a possible implementation manner, the ratio of the minimum value of the load movement decision value to the load movement probability of the user is determined as the load movement threshold.
As a possible implementation manner, specifically, determining the target load movement duration according to the load movement threshold and the load movement decision value corresponding to each of the reference load movement durations may be implemented as follows:
determining the reference load moving duration corresponding to the load moving decision value which is not greater than the load moving threshold value as a candidate load moving duration; determining a target production weight corresponding to the candidate load moving duration according to the initial production weight, the candidate load moving duration and the basic load data which are respectively corresponding to each hour in the prediction day; and determining the candidate load moving duration corresponding to the maximum value of the target production weight as a target load moving duration.
It can be understood that the initial production weight indicates the possibility of production for the user, and can be determined by the "comfort level" of the worker, that is, the initial production weight indicates the comfort level of the worker, and the initial production weight can be obtained by presetting, and it should be noted that the initial production weight can be adjusted according to regions and customs, for example, the work and rest of regions with hot weather are moved backward as a whole, and the work and rest time of regions with large longitude deviation, such as Xinjiang, are also greatly different. Specifically, the target production weight corresponding to the candidate load movement duration may be determined by the following formula (3):
wherein, the dayComf (t) represents the target production weight corresponding to the candidate load moving time t; qk(j+t)Characterizing the load of the predicted day k at the (j + t) th moment; comf (j + t) characterizes the initial production weight of the prediction day k at the (j + t) th moment. It should be noted that the initial production weight is 1 at the highest and 0 at the lowest, and a larger value indicates a more natural rule.
As a possible implementation manner, specifically, the obtaining of the load movement probability of the user may be implemented by:
acquiring historical load data of the user and historical load movement evaluation data corresponding to the historical load data;
according to the historical load data and the historical load data evaluation data, determining a daily load movement probability corresponding to each historical day in the historical load data;
and determining the load movement probability of the user according to the daily load movement probability corresponding to each historical day and the historical load data.
It can be understood that the daily load movement probability of the historical day indicates the possibility of daily load movement of the historical day, and can be specifically determined by the following formula (4):
wherein, susiRepresenting the daily load weight of the ith historical day;
representing the mobile electric charge of the hour load data after the movement time t of the mobile reference load on the ith historical day; cos (chemical oxygen demand)iRaw electricity rates for the ith historical day. Wherein the content of the first and second substances,
the calculation method of (2) is similar to the method in the above formula (1) and formula (2), the catalog electricity price is changed from the spot electricity price, the forecast date is changed into the historical date, and the initial electricity charge of the historical date is determined by the load of each hour of the historical date and the catalog electricity price corresponding to each hour. In particular, the catalog electricity prices refer to electricity prices in transactions effected by the medium and long term electric energy market. It should be noted that the medium and long-term electric energy market adopts a trade mode of combining off-site bilateral negotiation trade and on-site centralized competition trade and mutually complementing a common curve contract and a custom curve contract, and flexibly realizes the signing and adjustment of a differential contract through year, month and week trade varieties organized for many times. The trade achieved by the medium and long-term electric energy market is a financial contract, has financial settlement significance and does not need to be executed physically.
It is understood that the load movement probability indicates the possibility of the load movement of the user, and can be specifically determined by the following formula (5):
wherein sus represents a load movement probability of the user; qijRepresenting the load of the jth hour in the ith historical day; susiRepresenting the daily load weight of the ith historical day; dDays of the characterization history day.
And step 104, determining the target load data of the forecast day according to the target load moving duration and the basic load data.
The predicted load data of the predicted day can be specifically determined by the following formula (6):
wherein the content of the first and second substances,
representing the load of the jth hour in the forecast day k corresponding to the target load movement duration T, Qk(j-T)And (4) representing the load of the (j-T) th hour in the prediction day k, and representing the meanings of other data items similarly, which is not described in detail herein.
According to the technical scheme, the beneficial effects of the embodiment are as follows:
and determining a load movement decision value corresponding to the reference load movement duration through the basic load data and the load movement evaluation data of the prediction day, and selecting the reference load movement duration based on the load movement decision value to determine the target load movement duration, so that the basic load data is subjected to load movement, and the target load data of the prediction day with relatively high accuracy is obtained.
Fig. 1 shows only a basic embodiment of the method of the present invention, and based on this, certain optimization and expansion can be performed, and other preferred embodiments of the method can also be obtained.
To more clearly illustrate the technical solution of the present invention, please refer to fig. 2, an embodiment of the present invention provides another load prediction method, and this embodiment is further described with reference to specific application scenarios on the basis of the foregoing embodiment.
The specific scenario combined in this embodiment is as follows:
the load movement evaluation data of the forecast day k comprises the spot electricity price corresponding to each hour in the forecast day k and the jthThe spot electricity price in hours is expressed as
The base load data on the prediction day k includes loads corresponding to the prediction day k, the day before the prediction day k, and the day after the prediction day k for each 24 hours, and the load on the jth hour on the prediction day k is represented by QkjThe reference load moving time t has 24 reference load moving time periods t, which are respectively-11, -10, …, 0, 1, … and 12, the unit is hour, the initial production weight Comf (j) has 24, which are respectively Comf (1), Comf (2), …, Comf (j), … and Comf (24), the Comf (j) represents the initial production weight of the jth hour, the historical load data comprises loads corresponding to the hours in d historical days, and the load of the jth hour in the ith historical day represents QijThe historical load movement evaluation data comprises the corresponding catalog electricity price of each hour in d historical days, and the catalog electricity price of the jth hour in the ith historical day represents
In this embodiment, the method may specifically include the following steps:
Specifically, load change rules are mined based on historical load data of users, and loads on a prediction day are predicted by adopting a load prediction method to obtain basic load data on the prediction day.
For each reference load movement time t, the determination is made according to the above equation (1)Deciding the load Q corresponding to the j hour in the forecast day kkjCalculating a load movement decision value corresponding to the reference load movement time period t according to the above equations (1) and (2)
For the ith history day, the daily load movement probability sus corresponding to the ith history day is calculated according to the formula (4)i. Changing the spot electricity price in the formula (2) into the catalog electricity price, changing the forecast day k into the ith historical day, and calculating the mobile electricity fee corresponding to the ith historical day
The load movement probability sus of the user is calculated according to the above equation (5). And will be
A load movement threshold is determined.
And (4) determining a target production weight dayComf (t) corresponding to the candidate load moving time length according to the step (3).
And (4) determining the candidate load moving time length corresponding to the maximum dayComf (T) as the target load moving time length T, and determining the load of the jth hour in the prediction day k according to the formula (6). The target load moving duration T is obtained, the economy and the worker comfort level are both considered, the reference value is relatively high, and the accuracy of the load in the prediction day obtained based on the target load moving duration is ensured.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Of course, the above scenario is only an exemplary scenario and is not intended to limit the method provided by the present invention.
According to the technical scheme, the beneficial effects of the embodiment are as follows:
the method comprises the steps of selecting load moving time with higher load moving probability by considering the electricity charge of a predicted day after load moving and the load moving probability determined based on historical load data and historical catalog electricity prices, determining target load moving time in the predicted day from the load moving time based on worker comfort, moving basic load data in the predicted day based on the target load moving time to realize load prediction, and comprehensively considering the electricity utilization economy of a user and the worker comfort of the user through the predicted load data, so that the method has relatively higher accuracy, and can be used for guiding production life and resource scheduling according to the predicted load subsequently.
Referring to fig. 3, based on the same concept as the method embodiment of the present invention, an embodiment of the present invention further provides a load prediction apparatus, including:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring basic load data of a user on a prediction day and load movement evaluation data of the prediction day;
a decision value determining module, configured to determine a load movement decision value corresponding to a reference load movement duration according to the reference load movement duration, the basic load data, and the load movement evaluation data;
a time length determining module, configured to determine a target load movement time length according to the load movement decision value corresponding to each of the reference load movement time lengths;
and the prediction module is used for determining the target load data of the prediction day according to the target load moving duration and the basic load data.
In one embodiment, the duration determination module includes: the device comprises a weight obtaining unit, a threshold value determining unit and a duration determining unit; wherein the content of the first and second substances,
the weight obtaining unit is used for obtaining the load movement probability of the user;
the threshold value determining unit is used for determining a load movement threshold value according to the load movement probability of the user and the minimum value of the load movement decision value;
and the time length determining unit is used for determining the target load moving time length according to the load moving threshold and the load moving decision value corresponding to each reference load moving time length.
In one embodiment, the duration determining unit includes: a first duration determining subunit, a weight determining subunit, and a second duration determining subunit; wherein the content of the first and second substances,
the first time length determining subunit is configured to determine a reference load movement time length corresponding to the load movement decision value that is not greater than the load movement threshold value as a candidate load movement time length;
the weight determining subunit is configured to determine, according to the initial production weight, the candidate load movement duration, and the basic load data that each hour in the prediction day corresponds to, a target production weight corresponding to the candidate load movement duration;
and the second duration determining subunit is configured to determine the candidate load movement duration corresponding to the maximum value of the target production weight as a target load movement duration.
In one embodiment, the weight obtaining unit includes: the device comprises a data acquisition unit, a first weight determination unit and a second weight determination unit; wherein the content of the first and second substances,
the data acquisition unit is used for acquiring historical load data of the user and historical load movement evaluation data corresponding to the historical load data;
the first weight determination unit is used for determining the daily load movement probability corresponding to each historical day in the historical load data according to the historical load data and the historical load data evaluation data;
and the second weight determination unit is used for determining the load movement probability of the user according to the daily load movement probability corresponding to each historical day and the historical load data.
In one embodiment, the base load data is predicted based on the historical load data;
the historical load data comprises hour load data of each of a plurality of historical days;
the historical load movement evaluation data comprises hour catalog electricity price data corresponding to the plurality of historical days;
the daily load movement probability is determined based on the minimum value of the mobile electric charge corresponding to the historical day and the original electric charge corresponding to the historical day, and the mobile electric charge is determined based on the target hour load data obtained after the load data of the historical day is moved by the reference load movement time and the hour catalog electricity price data corresponding to the historical day.
In one embodiment, the determinant value determination module includes: a mobile unit and a decision value determining unit; wherein the content of the first and second substances,
the mobile unit is used for moving the basic load data according to a reference load moving time length so as to determine reference load data corresponding to the reference load moving time length;
and the decision value determining unit is used for determining a load movement decision value corresponding to the reference load movement time length according to the load movement evaluation data and the reference load data corresponding to the reference load movement time length.
In one embodiment, the load movement assessment data for the predicted day includes: the current price of the goods in the hour corresponding to the forecast day;
the load movement decision value is determined based on the hour spot electricity price corresponding to the forecast day and the hour load data corresponding to the forecast day.
Fig. 4 is a schematic diagram of an electronic device according to an embodiment of the present invention. As shown in fig. 4, the electronic apparatus 4 of this embodiment includes: a processor 40, a memory 41 and a computer program 42 stored in said memory 41 and executable on said processor 40. The processor 40 executes the computer program 42 to implement the steps in the above-mentioned embodiments of the load prediction method, such as the steps 101 to 104 shown in fig. 1 and the steps 201 to 206 shown in fig. 1. Alternatively, the processor 40, when executing the computer program 42, implements the functions of the modules/units in the above-mentioned device embodiments, such as the functions of the modules 301 to 304 shown in fig. 3.
Illustratively, the computer program 42 may be partitioned into one or more modules/units that are stored in the memory 41 and executed by the processor 40 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 42 in the electronic device 4.
The electronic device 4 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The electronic device may include, but is not limited to, a processor 40, a memory 41. Those skilled in the art will appreciate that fig. 4 is merely an example of an electronic device 4 and does not constitute a limitation of the electronic device 4 and may include more or fewer components than shown, or some components may be combined, or different components, e.g., the electronic device may also include input-output devices, network access devices, buses, etc.
The Processor 40 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 41 may be an internal storage unit of the electronic device 4, such as a hard disk or a memory of the electronic device 4. The memory 41 may also be an external storage device of the electronic device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the electronic device 4. The memory 41 is used for storing the computer program and other programs and data required by the electronic device. The memory 41 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/electronic device and method may be implemented in other ways. For example, the above-described apparatus/electronic device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A method of load prediction, comprising:
acquiring basic load data of a user on a prediction day and load movement evaluation data of the prediction day;
determining a load movement decision value corresponding to the reference load movement duration according to the reference load movement duration, the basic load data and the load movement evaluation data;
determining a target load moving duration according to the load moving decision value corresponding to each reference load moving duration;
and determining the target load data of the forecast day according to the target load moving duration and the basic load data.
2. The method according to claim 1, wherein the determining a target load shifting duration according to the load shifting decision value corresponding to each of the reference load shifting durations comprises:
acquiring the load movement probability of the user;
determining a load movement threshold according to the load movement probability of the user and the minimum value of the load movement decision value;
and determining the target load moving time length according to the load moving threshold and the load moving decision value corresponding to each reference load moving time length.
3. The method according to claim 2, wherein determining a target load shifting duration based on the load shifting threshold and the load shifting decision value corresponding to each of the reference load shifting durations comprises:
determining the reference load moving duration corresponding to the load moving decision value which is not greater than the load moving threshold value as a candidate load moving duration;
determining a target production weight corresponding to the candidate load moving duration according to the initial production weight, the candidate load moving duration and the basic load data which are respectively corresponding to each hour in the prediction day;
and determining the candidate load moving duration corresponding to the maximum value of the target production weight as a target load moving duration.
4. The method of claim 2, wherein the obtaining the load movement probability of the user comprises:
acquiring historical load data of the user and historical load movement evaluation data corresponding to the historical load data;
according to the historical load data and the historical load data evaluation data, determining a daily load movement probability corresponding to each historical day in the historical load data;
and determining the load movement probability of the user according to the daily load movement probability corresponding to each historical day and the historical load data.
5. The method of claim 4, wherein the base load data is predicted based on the historical load data;
the historical load data comprises hour load data of each of a plurality of historical days;
the historical load movement evaluation data comprises hour catalog electricity price data corresponding to the plurality of historical days;
the daily load movement probability is determined based on the minimum value of the mobile electric charge corresponding to the historical day and the original electric charge corresponding to the historical day, and the mobile electric charge is determined based on the target hour load data obtained after the load data of the historical day is moved by the reference load movement time and the hour catalog electricity price data corresponding to the historical day.
6. The method according to claim 1, wherein the determining the load movement decision value corresponding to the reference load movement duration according to the reference load movement duration, the base load data and the load movement evaluation data comprises:
moving the basic load data according to the reference load moving duration to determine reference load data corresponding to the reference load moving duration;
and determining a load movement decision value corresponding to the reference load movement duration according to the load movement evaluation data and the reference load data corresponding to the reference load movement duration.
7. The method of claim 6, wherein the load movement assessment data for the predicted day comprises: the current price of the goods in the hour corresponding to the forecast day;
the basic load data comprises hour load data corresponding to the prediction day, hour load data corresponding to the prediction day before and hour load data corresponding to the prediction day after;
the load movement decision value is determined based on the hour spot electricity price corresponding to the forecast day and the hour load data corresponding to the forecast day.
8. A load prediction apparatus, comprising:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring basic load data of a user on a prediction day and load movement evaluation data of the prediction day;
a decision value determining module, configured to determine a load movement decision value corresponding to a reference load movement duration according to the reference load movement duration, the basic load data, and the load movement evaluation data;
a time length determining module, configured to determine a target load movement time length according to the load movement decision value corresponding to each of the reference load movement time lengths;
and the prediction module is used for determining the target load data of the prediction day according to the target load moving duration and the basic load data.
9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1 to 7 are implemented when the computer program is executed by the processor.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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| CN202011596183.XA CN112651563B (en) | 2020-12-29 | 2020-12-29 | Load prediction method and device, computer readable storage medium and electronic equipment |
| PCT/CN2021/113691 WO2022142389A1 (en) | 2020-12-29 | 2021-08-20 | Load forecasting method and apparatus, computer readable storage medium, and electronic device |
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| WO2022142389A1 (en) * | 2020-12-29 | 2022-07-07 | 新奥数能科技有限公司 | Load forecasting method and apparatus, computer readable storage medium, and electronic device |
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| CN116526567A (en) * | 2023-06-26 | 2023-08-01 | 深圳智电时代科技有限公司 | Energy management method and device for household photovoltaic power supply system and storage medium |
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