CN112257958A - Power saturation load prediction method and device - Google Patents
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Abstract
The invention is suitable for the technical field of electric power, and provides a method and a device for predicting electric power saturated load, wherein the method comprises the following steps: determining at least one key factor influencing the power saturation load, establishing a first prediction model based on historical data of each key factor, and determining prediction data of each key factor according to the first prediction model; establishing a second prediction model based on the historical data of each key factor and the historical data of the power consumption corresponding to the historical data of each key factor, and determining the prediction data of the power consumption according to the second prediction model; and determining the time for reaching the power saturated load and the size of the power saturated load based on the prediction data of the key factors and the prediction data of the power consumption. The method can effectively improve the prediction accuracy of the power saturation load.
Description
Technical Field
The invention belongs to the technical field of electric power, and particularly relates to a method and a device for predicting electric power saturated load.
Background
Due to the development of social economy, the enlargement of urban scale and the increase of power consumption, power grid enterprises need to carry out power grid development planning so as to meet future power requirements of local areas. The prediction of the future power saturation load of the local area is an important part of the power grid development planning.
However, as the construction scale of the smart grid is gradually enlarged, large electric power data including production data, marketing data, relevant social and economic data and the like are formed, the traditional electric power load prediction methods such as total amount prediction, planned prediction, extrapolation prediction and the like cannot meet the requirements of electric power load characteristic analysis and prediction under new situations, and the accuracy of prediction results is poor.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for predicting a power saturation load, so as to improve the prediction accuracy of the power saturation load.
A first aspect of an embodiment of the present invention provides a power saturation load prediction method, including:
determining at least one key factor affecting the power saturation load;
acquiring historical data of each key factor, establishing a first prediction model of each key factor based on the historical data of each key factor, and determining the prediction data of each key factor according to the first prediction model of each key factor;
acquiring historical data of power consumption corresponding to the historical data of each key factor, establishing a second prediction model based on the historical data of each key factor and the historical data of the power consumption, and determining the prediction data of the power consumption corresponding to the prediction data of each key factor according to the prediction data of each key factor and the second prediction model;
and determining the time for reaching the power saturated load and the size of the power saturated load based on the prediction data of the key factors and the prediction data of the power consumption.
A second aspect of an embodiment of the present invention provides a power saturation load prediction apparatus, including:
the determining module is used for determining at least one key factor influencing the power saturation load;
the first prediction module is used for acquiring historical data of each key factor, establishing a first prediction model of each key factor based on the historical data of each key factor, and determining the prediction data of each key factor according to the first prediction model of each key factor;
the second prediction module is used for acquiring historical data of the power consumption corresponding to the historical data of each key factor, establishing a second prediction model based on the historical data of each key factor and the historical data of the power consumption, and determining the prediction data of the power consumption corresponding to the prediction data of each key factor according to the prediction data of each key factor and the second prediction model;
and the saturated load calculation module is used for determining the time for reaching the power saturated load and the size of the power saturated load based on the prediction data of each key factor and the prediction data of the power consumption.
A third aspect of embodiments of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the power saturation load prediction method according to the first aspect when executing the computer program.
A fourth aspect of embodiments of the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the power saturation load prediction method according to the first aspect.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
the method comprises the steps of screening out factors with high relevance degree with the power saturation load by determining key factors influencing the power saturation load, and establishing a first prediction model to determine prediction data of each key factor; establishing a second prediction model according to the historical data of each key factor and the historical data of the power consumption corresponding to the historical data of each key factor, and determining the prediction data of the power consumption corresponding to the prediction data of each key factor according to the prediction data of each key factor and the second prediction model; and finally, determining the time for reaching the power saturated load and the size of the power saturated load based on the prediction data of each key factor and the prediction data of the power consumption. According to the method, the time for reaching the power saturated load and the size of the power saturated load are predicted by screening the key factors and establishing the power consumption prediction model integrating the key factors, so that the prediction accuracy of the power saturated load is effectively improved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in 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 based on these drawings without inventive exercise.
Fig. 1 is a schematic flow chart of a power saturation load prediction method according to an embodiment of the present invention;
FIG. 2 is a graphical illustration of an LSTM predictive model provided by an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electric saturation load prediction apparatus according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to 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.
A first aspect of an embodiment of the present invention provides a power saturation load prediction method, as shown in fig. 1, the method may specifically include the following steps:
step S101, determining at least one key factor influencing the power saturation load.
Optionally, as a specific implementation manner of the power saturation load prediction method provided in the first aspect of the embodiment of the present invention, a method for determining key factors affecting the power saturation load may be detailed as follows:
acquiring a plurality of factors influencing the power saturation load;
performing grey correlation analysis on the factors to obtain grey correlation degrees of the factors;
and determining the factors of which the grey correlation degree is not less than a preset threshold value as key factors influencing the power saturation load.
The determination indexes of the power saturation load can be divided into two types, one is a main index such as a power consumption increase rate, an annual maximum load increase rate and the like, and the other is an auxiliary index such as a third industry proportion, a standing population increase rate, a urbanization rate, a per capita GDP and the like. Therefore, in order to determine whether or not a certain area has reached the power saturation load, it is necessary to consider the auxiliary index in addition to the main index satisfying the standard of the power saturation load.
Because the factors in the auxiliary indexes influence the development of the power consumption, in the embodiment of the invention, in order to make the prediction of the power saturation load more accurate, the influence degree of the factors in the auxiliary indexes needs to be quantized, and the key factors with higher influence degree on the development of the power saturation load level are screened out. Specifically, the following can be realized by grey correlation analysis:
(1) firstly, historical data of each factor is obtained to form a comparison matrix.
There are m factors, each factor selects n data, XijIs the j-th data of the i-th factor, wherein i is 1,2.. m, and j is 1,2.. n. Then call Xi＝(xi1,xi2,....xin) Is a factor XiThe comparison matrix is:
(2) and (3) standardizing the sequences of various action factors by adopting a Z-Score model:
in the formula (I), the compound is shown in the specification,
is a behavioral factor sequence XiMean value of SiIs a behavioral factor sequence XiThe standard deviation of (2), the row factor sequence after the standardization processing is X* i＝(x* i1,x* i2,....x* in)。
(3) Let X0＝(x01,x02,....x0n) Calculating X for a reference sequence, i.e. the city power consumption over the years* iAnd X* 0The absolute value sequence Δ i of the differences of the corresponding components:
Δi＝(|X* 0j-X* ij|)
(4) and solving the maximum value and the minimum value of data delta ij in the sequence delta i:
MAX＝Δimaxjmax
MIN＝Δiminjmin
(5) calculating a gray correlation coefficient gamma0ij：
Where ξ is a resolution factor and ξ (0, 1), in general, ξ is 0.5.
(6) Calculating grey correlation degree gamma by using averaging method0i：
Degree of gray correlation gamma0iRepresenting a sequence of behavioral factors XiWith power consumption sequence X0Gray correlation of (a) < gamma >0iThe larger the expression of the behavioral factor sequence XiWith power consumption sequence X0The more similar the changing situation, the more the behavior factor sequence X is illustratediFor use inElectric quantity sequence X0The greater the degree of influence of (c). To improve the accuracy of the saturation load prediction, a threshold value γ may be determined*When is γ0i≥γ*When the power consumption is large, the influence degree of the factor on the power consumption is considered to be a key factor; when gamma is0i<γ*And considering that the influence degree of the factor on the electricity consumption is small, and eliminating the factor when establishing the prediction model. It is noted that the threshold γ*The value of (a) can be determined by combining the calculation result of the grey correlation degree with the actual situation, which is not limited in the present invention.
Step S102, obtaining historical data of each key factor, establishing a first prediction model of each key factor based on the historical data of each key factor, and determining the prediction data of each key factor according to the first prediction model of each key factor.
Optionally, as a specific implementation manner of the power saturated load prediction method provided in the first aspect of the embodiment of the present invention, the historical data of the key factors is a historical value of the key factors in a historical year, and the establishing of the first prediction model of each key factor based on the historical data of each key factor includes:
performing data fitting on the historical years and the historical values of the key factors to obtain a first prediction model of the key factors; the first prediction model is a Logistic curve model.
Optionally, as a specific implementation manner of the power saturated load prediction method provided in the first aspect of the embodiment of the present invention, the determining, according to the first prediction model of each key factor, the prediction data of each key factor as a prediction value of the key factor in a prediction year includes:
and inputting the preset prediction years of all the key factors into the first prediction model to obtain the prediction values of all the key factors under the prediction years.
In the embodiment of the invention, curve parameters k, a and b of the Logistic curve model are determined by performing data fitting on the historical years and the historical values of the key factors, and then the Logistic curve model of the key factors is established:
the Logistic curve model can well reflect the curve rule of the key factors in the development process, preset prediction years of the key factors are input into the Logistic curve model, and prediction values of the key factors under the prediction years can be obtained.
Step S103, obtaining historical data of the power consumption corresponding to the historical data of each key factor, establishing a second prediction model based on the historical data of each key factor and the historical data of the power consumption, and determining the prediction data of the power consumption corresponding to the prediction data of each key factor according to the prediction data of each key factor and the second prediction model.
Optionally, as a specific implementation manner of the power saturated load prediction method provided in the first aspect of the embodiment of the present invention, establishing a second prediction model based on the historical data of each key factor and the historical data of the power consumption includes:
carrying out normalization processing on historical data of each key factor and historical data of power consumption to obtain training sample data;
establishing a second prediction model based on the training sample data; wherein the second prediction model is an LSTM prediction model.
Optionally, as a specific implementation manner of the power saturated load prediction method provided in the first aspect of the embodiment of the present invention, determining, according to the prediction data of each key factor and the second prediction model, the prediction data of the power consumption corresponding to the prediction data of each key factor, includes:
and inputting the prediction data of each key factor into the second prediction model to obtain the prediction data of the power consumption corresponding to the prediction data of each key factor.
In the embodiment of the invention, training sample data can be divided into a training set and a test set, the training set is used for training the LSTM prediction model, the test set is used for carrying out reliability test on the LSTM prediction model, and the LSTM prediction model can be retrained if a test result does not meet the expected requirement. The LSTM prediction model established by screening the key factors has higher prediction precision, and the prediction data of each key factor is input into the LSTM prediction model, so that the prediction value of the power consumption in each prediction year can be obtained.
In step S103, the time to reach the power saturated load and the magnitude of the power saturated load are determined based on the prediction data of each key factor and the prediction data of the power consumption amount.
Optionally, as a specific implementation manner of the power saturated load prediction method provided in the first aspect of the embodiment of the present invention, determining, based on the prediction data of each key factor and the prediction data of the power consumption, the time to reach the power saturated load and the size of the power saturated load includes:
determining the time for each key factor to reach the corresponding first preset index based on the prediction data of each key factor;
determining the time when the electricity consumption reaches a second preset index based on the predicted data of the electricity consumption;
determining the time when each key factor reaches a corresponding first preset index and the power consumption reaches a second preset index as the time when the power saturation load is reached;
and determining the size of the power saturated load according to the time reaching the power saturated load and the prediction data of the power consumption.
In the embodiment of the present invention, the time of the power saturation load may be determined only by obtaining the prediction data of each key factor and the prediction data of the power consumption, and the determination criterion for the power saturation load may be selected according to an actual situation, for example, the time when a certain key factor reaches the corresponding first preset index or the time when certain key factors reach the corresponding first preset indexes may also be selected as the time when the power saturation load is reached in the time when the power consumption reaches the second preset index, which is not limited in the present application.
The power saturation load prediction method of the present invention is described below by way of an exemplary embodiment.
Selecting 7 factors influencing the power saturation load, namely GDP, frequent population number, average GDP, second industry ratio, third industry ratio, urbanization rate and resident consumption index of a certain area, acquiring historical values of the factors in 2019 of the area in 1995, and performing grey correlation analysis, wherein the grey correlation degree of the factors is shown in table 1:
TABLE 1 Grey correlation
Setting a threshold value gamma*And if the grey correlation degree of the GDP, the number of the permanent population, the average population GDP, the third industry proportion and the urbanization rate is greater than 0.7, determining that the GDP, the number of the permanent population, the average population GDP, the third industry proportion and the urbanization rate are key factors influencing the power saturation load.
And establishing a Logistic curve model through historical data of the GDP, the number of the constant population, the average-person GDP, the third industry proportion and the urbanization rate, and predicting the GDP, the number of the constant population, the average-person GDP, the third industry proportion and the urbanization rate in the coming years to obtain prediction data of each key factor.
Establishing an LSTM prediction model according to the historical values of the key factors in the years 1995-2019 and the historical values of the power consumption in the years 1995-2019, and inputting the predicted values of the key factors in the years in the future into the LSTM prediction model to obtain the predicted data of the power consumption in the years in the future. The curves for the LSTM prediction model are shown in fig. 2. According to fig. 2, in 2032 years, the increase rate of the power consumption is less than 3% for 5 years continuously, the increase rate of the number of the permanent population is less than 1%, the urbanization rate is more than 70%, and the proportion of the third industry is more than 65%, so 2032 years is determined as the time reaching the power saturation load, and the power saturation load is 51615 hundred million kilowatt-hours according to the prediction data of the power consumption.
According to the method, the key factors influencing the power saturation load are determined, the factors with high relevance degree with the power saturation load are screened out, and a first prediction model is established to determine the prediction data of each key factor; establishing a second prediction model according to the historical data of each key factor and the historical data of the power consumption corresponding to the historical data of each key factor, and determining the prediction data of the power consumption corresponding to the prediction data of each key factor according to the prediction data of each key factor and the second prediction model; and finally, determining the time for reaching the power saturated load and the size of the power saturated load based on the prediction data of each key factor and the prediction data of the power consumption. According to the method, the time for reaching the power saturated load and the size of the power saturated load are predicted by screening the key factors and establishing the power consumption prediction model integrating the key factors, so that the prediction accuracy of the power saturated load is effectively improved.
A second aspect of the embodiments of the present invention provides a power saturation load prediction apparatus, where the power saturation load prediction apparatus 3 includes:
a determination module 31 for determining at least one key factor affecting the power saturation load.
The first prediction module 32 is configured to obtain historical data of each key factor, establish a first prediction model of each key factor based on the historical data of each key factor, and determine prediction data of each key factor according to the first prediction model of each key factor.
The second prediction module 33 is configured to obtain historical data of the power consumption corresponding to the historical data of each key factor, establish a second prediction model based on the historical data of each key factor and the historical data of the power consumption, and determine prediction data of the power consumption corresponding to the prediction data of each key factor according to the prediction data of each key factor and the second prediction model.
And the saturated load calculation module 34 is used for determining the time for reaching the saturated power load and the size of the saturated power load based on the prediction data of the key factors and the prediction data of the power consumption.
Optionally, as a specific implementation manner of the power saturated load prediction apparatus provided in the second aspect of the embodiment of the present invention, determining key factors affecting the power saturated load may be detailed as follows:
acquiring a plurality of factors influencing the power saturation load;
performing grey correlation analysis on the multiple factors to obtain grey correlation degrees of the factors;
and determining the factors of which the grey correlation degree is not less than a preset threshold value as key factors influencing the power saturation load.
Optionally, as a specific implementation manner of the power saturated load prediction apparatus provided in the second aspect of the embodiment of the present invention, the historical data of the key factors is a historical value of the key factors in a historical year, and a first prediction model of each key factor is established based on the historical data of each key factor, which may be detailed as follows:
performing data fitting on the historical years and the historical values of the key factors to obtain a first prediction model of the key factors; the first prediction model is a Logistic curve model.
Optionally, as a specific implementation manner of the power saturated load prediction apparatus provided in the second aspect of the embodiment of the present invention, the prediction data of the key factors is a prediction value of the key factors in a prediction year, and the prediction data of each key factor is determined according to the first prediction model of each key factor, which may be detailed as follows:
and inputting the preset prediction years of all the key factors into the first prediction model to obtain the prediction values of all the key factors under the prediction years.
Optionally, as a specific implementation manner of the power saturated load prediction apparatus provided in the second aspect of the embodiment of the present invention, a second prediction model is established based on historical data of each key factor and historical data of power consumption, which may be detailed as follows:
carrying out normalization processing on historical data of each key factor and historical data of power consumption to obtain training sample data;
establishing a second prediction model based on the training sample data; wherein the second prediction model is an LSTM prediction model.
Optionally, as a specific implementation manner of the power saturated load prediction apparatus provided in the second aspect of the embodiment of the present invention, the prediction data of the power consumption corresponding to the prediction data of each key factor is determined according to the prediction data of each key factor and the second prediction model, and may be detailed as follows:
and inputting the prediction data of each key factor into the second prediction model to obtain the prediction data of the power consumption corresponding to the prediction data of each key factor.
Optionally, as a specific implementation manner of the power saturated load prediction apparatus provided in the second aspect of the embodiment of the present invention, the time to reach the power saturated load and the magnitude of the power saturated load are determined based on the prediction data of each key factor and the prediction data of the power consumption, which may be detailed as follows:
determining the time for each key factor to reach the corresponding first preset index based on the prediction data of each key factor;
determining the time when the electricity consumption reaches a second preset index based on the predicted data of the electricity consumption;
determining the time when each key factor reaches a corresponding first preset index and the power consumption reaches a second preset index as the time when the power saturation load is reached;
and determining the size of the power saturated load according to the time reaching the power saturated load and the prediction data of the power consumption.
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.
Fig. 4 is a schematic diagram of an electronic device provided in 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 the memory 41 and executable on the processor 40. The processor 40, when executing the computer program 42, implements the steps in each of the above-described embodiments of the power saturation load prediction method, such as the steps S101 to S104 shown in fig. 1. Alternatively, the processor 40, when executing the computer program 42, implements the functions of the modules in the above-described device embodiments, such as the functions of the modules 31 to 34 shown in fig. 3.
Illustratively, the computer program 42 may be partitioned into one or more modules/units, which are stored in the memory 41 and executed by the processor 40 to implement the present invention. One or more of the modules/units may be a series of computer program instruction segments capable of performing specific functions that describe the execution of the computer program 42 in the electronic device 4. For example, the computer program 42 may be divided into the determination module 31, the first prediction module 32, the second prediction module 33, and the saturation load calculation module 34, and each module has the following specific functions:
a determination module 31 for determining at least one key factor affecting the power saturation load.
The first prediction module 32 is configured to obtain historical data of each key factor, establish a first prediction model of each key factor based on the historical data of each key factor, and determine prediction data of each key factor according to the first prediction model of each key factor.
The second prediction module 33 is configured to obtain historical data of the power consumption corresponding to the historical data of each key factor, establish a second prediction model based on the historical data of each key factor and the historical data of the power consumption, and determine prediction data of the power consumption corresponding to the prediction data of each key factor according to the prediction data of each key factor and the second prediction model.
And the saturated load calculation module 34 is used for determining the time for reaching the saturated power load and the size of the saturated power load based on the prediction data of the key factors and the prediction data of the power consumption.
The electronic device 4 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The electronic device 4 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 the electronic device 4 and does not constitute a limitation of the electronic device 4 and may include more or fewer components than shown, or combine certain components, or different components, e.g., the electronic device 4 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), an off-the-shelf 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 storage 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 provided on the electronic device 4, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the memory 41 may also include both an internal storage unit of the electronic device 4 and an external storage device. The memory 41 is used for storing computer programs and other programs and data required by the electronic device 4. 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, so as to perform all or part of the functions described above. 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, a module or a unit may be divided into only one logic function, and may be implemented in other ways, 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.
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 in the method according to the embodiments of the present invention may also be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of the embodiments of the method. 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 computer program code, recording medium, U.S. 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 media, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, in accordance with legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunications signals.
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 for predicting a power saturation load, comprising:
determining at least one key factor affecting the power saturation load; acquiring historical data of each key factor, establishing a first prediction model of each key factor based on the historical data of each key factor, and determining the prediction data of each key factor according to the first prediction model of each key factor;
acquiring historical data of power consumption corresponding to the historical data of each key factor, establishing a second prediction model based on the historical data of each key factor and the historical data of the power consumption, and determining the prediction data of the power consumption corresponding to the prediction data of each key factor according to the prediction data of each key factor and the second prediction model;
and determining the time for reaching the power saturated load and the size of the power saturated load based on the prediction data of the key factors and the prediction data of the power consumption.
2. The method of power saturated load prediction according to claim 1, wherein the method of determining key factors affecting power saturated load comprises:
acquiring a plurality of factors influencing the power saturation load;
performing grey correlation analysis on the factors to obtain grey correlation degrees of the factors;
and determining the factors of which the grey correlation degree is not less than a preset threshold value as key factors influencing the power saturation load.
3. The power saturation load prediction method according to claim 1, wherein the historical data of the key factors is historical values of the key factors in historical years, and the establishing of the first prediction model of each key factor based on the historical data of each key factor includes:
performing data fitting on the historical years and the historical values of the key factors to obtain a first prediction model of the key factors; wherein the first prediction model is a Logistic curve model.
4. The power saturation load prediction method according to claim 1 or 3, wherein the prediction data of the key factors is a prediction value of the key factors under a prediction year, and the determining the prediction data of each key factor according to the first prediction model of each key factor includes:
and inputting the preset prediction years of all the key factors into the first prediction model to obtain the prediction values of all the key factors under the prediction years.
5. The method for predicting saturated power load according to claim 1, wherein the establishing of the second prediction model based on the historical data of the key factors and the historical data of the power consumption comprises:
carrying out normalization processing on historical data of each key factor and historical data of the electricity consumption to obtain training sample data;
establishing a second prediction model based on the training sample data; wherein the second prediction model is an LSTM prediction model.
6. The power saturation load prediction method according to claim 1 or 5, wherein the determining the prediction data of the power consumption corresponding to the prediction data of each key factor according to the prediction data of each key factor and the second prediction model includes:
and inputting the prediction data of each key factor into the second prediction model to obtain the prediction data of the power consumption corresponding to the prediction data of each key factor.
7. The power saturated load prediction method of claim 1, wherein the determining the time to reach the power saturated load and the magnitude of the power saturated load based on the prediction data of the respective key factors and the prediction data of the power consumption amount comprises:
determining the time for each key factor to reach the corresponding first preset index based on the prediction data of each key factor;
determining the time when the electricity consumption reaches a second preset index based on the predicted data of the electricity consumption;
determining the time when each key factor reaches a corresponding first preset index and the power consumption reaches a second preset index as the time when the power saturation load is reached;
and determining the size of the power saturated load according to the time reaching the power saturated load and the prediction data of the power consumption.
8. An electric saturation load prediction apparatus, comprising:
the determining module is used for determining at least one key factor influencing the power saturation load;
the first prediction module is used for acquiring historical data of each key factor, establishing a first prediction model of each key factor based on the historical data of each key factor, and determining the prediction data of each key factor according to the first prediction model of each key factor;
the second prediction module is used for acquiring historical data of power consumption corresponding to the historical data of each key factor, establishing a second prediction model based on the historical data of each key factor and the historical data of the power consumption, and determining the prediction data of the power consumption corresponding to the prediction data of each key factor according to the prediction data of each key factor and the second prediction model;
and the saturated load calculation module is used for determining the time for reaching the saturated power load and the size of the saturated power load based on the prediction data of the key factors and the prediction data of the power consumption.
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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Application publication date: 20210122 |