CN112365280A - Power demand prediction method and device - Google Patents
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Abstract
The invention discloses a method and a device for predicting power demand, wherein the method comprises the following steps: acquiring meteorological factors of a time period to be predicted in a target area; obtaining the dominant meteorological factors of the target area in the period to be predicted according to the meteorological factors of the target area in the period to be predicted and a pre-established dominant meteorological factor identification model; obtaining the power demand of the target area in the period to be predicted according to the dominant meteorological factors of the target area in the period to be predicted and a pre-established support vector machine power demand prediction model; the support vector machine power demand prediction model is pre-established according to a plurality of dominant meteorological characteristic samples and power load characteristic samples. The invention can improve the accuracy of power demand prediction.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method and a device for predicting power demand.
Background
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
The power load is one of important indexes of planning design and operation management of a power system, and the research on the characteristics and the change rule of the load is the first condition for achieving safe, stable, high-quality and economic operation of a power grid.
At present, various solutions, such as a regression model, a neural network method and the like, exist for the analysis and prediction of the power demand, but the prediction methods have the problem of low prediction accuracy.
Disclosure of Invention
The embodiment of the invention provides a power demand prediction method, which is used for improving the precision of power demand prediction and comprises the following steps:
acquiring meteorological factors of a time period to be predicted in a target area;
obtaining the dominant meteorological factors of the target area in the period to be predicted according to the meteorological factors of the target area in the period to be predicted and a pre-established dominant meteorological factor identification model;
obtaining the power demand of the target area in the period to be predicted according to the dominant meteorological factors of the target area in the period to be predicted and a pre-established support vector machine power demand prediction model; the support vector machine power demand prediction model is pre-established according to a plurality of dominant meteorological characteristic samples and power load characteristic samples.
The embodiment of the invention also provides a prediction device of power demand, which is used for improving the precision of power demand prediction and comprises the following components:
the acquiring unit is used for acquiring meteorological factors of a time period to be predicted in a target area;
the identification unit is used for acquiring the dominant meteorological factor of the target area in the period to be predicted according to the meteorological factor of the target area in the period to be predicted and a pre-established dominant meteorological factor identification model;
the prediction unit is used for obtaining the power demand of the target area in the period to be predicted according to the dominant meteorological factors of the target area in the period to be predicted and a pre-established support vector machine power demand prediction model; the support vector machine power demand prediction model is pre-established according to a plurality of dominant meteorological characteristic samples and power load characteristic samples.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the prediction method of the power demand when executing the computer program.
An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program for executing the above power demand prediction method is stored.
In the embodiment of the invention, compared with the technical scheme of predicting power demand analysis by using a regression model, a neural network and the like in the prior art, the power demand prediction scheme comprises the following steps: acquiring meteorological factors of a time period to be predicted in a target area; obtaining the dominant meteorological factors of the target area in the period to be predicted according to the meteorological factors of the target area in the period to be predicted and a pre-established dominant meteorological factor identification model; obtaining the power demand of the target area in the period to be predicted according to the dominant meteorological factors of the target area in the period to be predicted and a pre-established support vector machine power demand prediction model; the support vector machine power demand prediction model is pre-established according to the plurality of dominant meteorological feature samples and the power load feature samples, so that the dominant meteorological factors are accurately identified, the support vector machine is adopted to establish the power demand prediction model based on the analysis of the dominant meteorological factors, the accurate prediction of the power demand is further realized, and the prediction precision of the power demand is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
FIG. 1 is a flow chart illustrating a method for predicting power demand according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating the degree of weather effect on the type of summer industrial load in an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating the degree of weather impact on the type of summer commercial load in an embodiment of the present invention;
FIG. 4 is a diagram illustrating the degree of influence of weather on the load types of residents in summer according to the embodiment of the present invention;
FIG. 5 is a schematic diagram illustrating the degree of weather effect on the type of industrial load in winter according to an embodiment of the present invention;
FIG. 6 is a schematic diagram illustrating the degree of weather effect on the type of winter commercial load in an embodiment of the present invention;
FIG. 7 is a schematic diagram illustrating the degree of influence of the load type of the residents in winter on weather in the embodiment of the present invention;
FIG. 8 is a diagram illustrating a correspondence between power loads and temperatures according to an embodiment of the present invention;
FIG. 9 is a schematic diagram illustrating an analysis of a load prediction result according to an embodiment of the present invention;
fig. 10 is a schematic structural diagram of a power demand prediction apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
The inventor finds that: the analysis and prediction of current power demand has the following problems: firstly, factors influencing the power load are various, particularly, quite many factors cannot be accurately and quantitatively given, and the change characteristics of the power load are represented by time-varying property, space-varying property, randomness, complexity and diversity; secondly, in recent years, power shortage becomes a problem of social attention, with the continuous improvement of the living standard of people and the gradual adjustment of an industrial structure, the specific gravity of the electricity used by residents and the electricity used by a third industry is continuously increased, the two types of electricity are inseparable from meteorological conditions, and the specific gravity of the electricity used by residents in the total electricity demand is further increased, so that the relation between the change of the electric load and the meteorological conditions is more close, but the economic structure and the development level of each region are also different due to different climatic conditions of different power grids, and the relation between each electric grid load and the meteorological factors is different; finally, the relation between the power load and the meteorological factors in the region or the city is quite complex, the relation between the power load and the meteorological factors in different regions is different, and the dominant meteorological factors can change along with the change of the region.
Because the inventor finds the technical problems of the existing power demand prediction, the relation between the power load and the meteorological factors needs to be further researched, a prediction scheme based on the meteorological factors is established, and a basis is provided for analysis, prediction and operation scheduling of the power demand. Therefore, the inventor proposes a prediction scheme of the power demand.
The prediction scheme of the power demand provided by the embodiment of the invention mainly aims to solve the problem that the power demand prediction precision is insufficient due to the fact that the main meteorological influence factors are difficult to clearly identify because of numerous meteorological influence factors of the current power demand. Through the electric power demand prediction scheme provided by the embodiment of the invention, namely the electric power demand sensitivity analysis and prediction method based on meteorological factor characteristics, the dominant meteorological factors can be identified, the incidence relation between the meteorological factors and the electric power demand can be mined, and the prediction precision of the electric power demand can be improved.
The prediction scheme of the power demand provided by the embodiment of the invention is a power demand sensitivity analysis and prediction method based on meteorological factor characteristics, and mainly has the following functions:
(1) rapidly analyzing the incidence relation between meteorological factors and power requirements, and identifying dominant meteorological factors;
(2) and establishing a power demand prediction model (support vector machine power demand prediction model) based on meteorological factors by combining the identified dominant factors, and realizing power demand sensitivity analysis and prediction.
The method for analyzing and predicting the power demand sensitivity based on the meteorological factor characteristics, provided by the embodiment of the invention, classifies the information of large customers collected by a negative control system according to classification standards (in order to obtain a dominant meteorological characteristic sample and a power load characteristic sample), then analyzes the incidence relation between each meteorological factor index and the power demand by adopting a random forest algorithm, and establishes an influence meteorological factor index library and a dominant factor identification system; the method comprises the steps of analyzing load characteristics of different types of loads, such as industrial loads, residential loads and the like, extracting feature vectors, establishing an electric quantity demand prediction model (a support vector machine electric quantity demand prediction model) based on dominant meteorological factors by adopting a support vector machine technology, and predicting future electric quantity demand conditions.
Specifically, the method comprises the steps of firstly identifying leading factors of weather factors such as the highest temperature, the lowest temperature, the average temperature, the wind speed and the air pressure based on a random forest algorithm, then analyzing the influence of the change of the electric quantity demand and load characteristics, then carrying out normalization processing on a data set, dividing the data set into a training set, a testing set and a verification set, training to obtain a support vector machine electric power demand prediction model, and then predicting by using the support vector machine electric power demand prediction model to obtain an accurate electric power demand prediction result. The prediction scheme of the power demand will be described in detail below.
Fig. 1 is a schematic flow chart of a method for predicting power demand according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step 101: acquiring meteorological factors of a time period to be predicted in a target area;
step 102: obtaining the dominant meteorological factors of the target area in the period to be predicted according to the meteorological factors of the target area in the period to be predicted and a pre-established dominant meteorological factor identification model;
step 103: obtaining the power demand of the target area in the period to be predicted according to the dominant meteorological factors of the target area in the period to be predicted and a pre-established support vector machine power demand prediction model; the support vector machine power demand prediction model is pre-established according to a plurality of dominant meteorological characteristic samples and power load characteristic samples.
In the embodiment of the invention, compared with the technical scheme of predicting the power demand analysis by using a regression model, a neural network and the like in the prior art, the prediction scheme of the power demand realizes accurate identification of the dominant meteorological factors, and the prediction model of the power demand is established by using a support vector machine based on the analysis of the dominant meteorological factors, so that the accurate prediction of the power demand is realized, and the prediction precision of the power demand is improved.
The following describes in detail the individual steps involved in the embodiments of the present invention.
Firstly, a step of establishing a model in advance is introduced.
1. Identification of dominant weather factors
The current numerical weather data includes a plurality of weather indicators, wherein some of the weather indicators have a significant influence on the power demand, so the embodiment of the invention adopts a correlation analysis method to identify the dominant factors of the power load and the power demand. Correlation analysis method for calculating correlation coefficient by using Pearson coefficientThe formula calculates a correlation coefficient which can reflect the correlation degree between the meteorological influence factor M and the power load P, and the correlation coefficient rPMDimensionless, with values of [ -1,1 [)]Within the range. When r isPMWhen the P and the M are not related, the P and the M are not related; when r isPM>0, P increases with increasing M, which is called positive correlation between P and M; when r isPM<0, P decreases with increasing M, said P is negatively correlated with M; when r isPMP can be exactly represented by a linear function of the variable M, which is calculated by the formula:
wherein n represents the number of time series, PiRepresents a load value at the ith time;
the average load value is shown; miThe meteorological value of the ith moment is represented, and a plurality of single/comprehensive meteorological factors (factors) such as temperature, humidity and the like are generally taken;
the average meteorological factor value is shown.
In specific implementation, the above formula (1) may be a dominant meteorological factor identification model.
2. Dominant meteorological factor and load feature sample extraction and classification
The method comprises the steps of extracting features of power load data and dominant meteorological index data (dominant meteorological factor data) by adopting a random forest, forming a meteorological feature library and a load feature library, constructing a data feature sample library, storing sample data in a classified mode, and providing samples used for prediction (samples for predicting power demand: the dominant meteorological feature samples and the power load feature samples).
The RF adopts a back-put repeated Sampling (Bootstrap Sampling) mode, a plurality of samples are randomly extracted from an initial sample set B to generate a new sample subset, and then a forest set consisting of k decision trees is generated according to each subsample. The essence of the method is a combined decision tree algorithm, and a model with stronger generalization learning capability is obtained by arranging and combining a plurality of decision trees. Typically, each tree in a forest has the same distribution, and the error of the fit depends on the correlation between the learning abilities of each tree. During sampling, the remaining unpumped samples are out-of-bag samples and are defined as a set
Wherein C is and
are respectively B and
a subset of (2). Suppose Xn+pFor an n-dimensional dataset with p features, y is an n-dimensional label vector, and the RF algorithm calculates the importance of the features by rearranging the fit error before and after the features. When building Q trees, there are Q out-of-bag sample sets as test sets. The feature importance index S is calculated as follows:
wherein x isjCharacteristic parameters (which can be the characteristics of the dominant meteorological factor data and the power load data); y isiIs the fitting attribute of the ith off-bag data, I is the error expression function, hk(i) To fit the attribute parameters to the samples predicted from dataset B,
for a characteristic parameter xjAnd (5) obtaining a fitting attribute parameter after replacement.
In specific implementation, important features (key features) can be identified through the formula (2) and used as samples for subsequently establishing the support vector machine power demand prediction model, the support vector machine power demand prediction model with higher prediction accuracy can be obtained, and then a high-accuracy power demand prediction result is obtained through the high-accuracy model prediction.
3. And establishing a power demand prediction model of a Support Vector Machine (SVM).
The embodiment of the invention adopts a support vector machine, takes sample data in a data feature library as input quantity and output quantity, and continuously optimizes and trains the prediction model to obtain the optimal prediction model. The basic idea of using a support vector machine to classify samples is as follows: the linear inseparable disturbance signal input feature vector is mapped to a feature space with higher dimensionality, an optimal hyperplane (hyperplane) is established in the high-dimensional feature space, the distance between the hyperplane and the edge of a sample to be classified is made to be maximum, and the classification accuracy is guaranteed to the maximum extent under the condition that a training set is small.
Support vector machines are derived from a binary problem for a binary sample set (x)i,yi)，x∈RdAnd e { -1,1}, wherein: x is the number ofiTo be divided into sample vectors, yiIndicating a sample label. And establishing a classification hyperplane w x + b as 0, wherein w is a normal vector of the hyperplane, b is a real number, and x is a d-dimensional input sample size. The training process of the support vector machine is a process for seeking the optimal hyperplane through iterative computation, namely, obtaining the values of w and b when the classification edge is maximized. Those training sample points on the hyperplane that are parallel to and closest to the classification plane are defined as Support Vectors (Support Vectors).
The problem of solving the optimal hyperplane can be expressed as:
s.t.y[(w×xi)+b]-1≥0,i＝1,2,...,n (4)
wherein y is a sample label.
Defining a lagrange function:
where a is the lagrangian coefficient (>0) and b is the bias. Solving the optimization problem under the constraint condition by using a dual principle, and further solving an optimal classification function formula as follows:
wherein, a*Represents the Lagrangian coefficient: (>0)，b*A threshold value representing a classification of the sample. x is the number ofiVarious meteorological factors are represented, and x represents sample characteristics. For the linear indifference case, a penalty factor u and a relaxation variable are introduced, and then the solution problem of the generalized optimal classification surface can be transformed into:
in the formula, ξ represents a deviation.
The support vector machine maps inseparable sample vectors in a low-dimensional space into a high-dimensional feature space through nonlinear mapping operation of a kernel function, and performs dot product operation (x) in an optimal classification surfaceiX) to kernel function K (x)iX) to obtain a discriminant function, wherein the kernel function is crucial to the construction of the support vector machine, and the relationship between the load and the influencing factors not only includes a linear relationship, but also has a complex nonlinear relationship, so that the embodiment of the invention adopts a radial basis kernel function, has excellent performance, can process the nonlinear problem more quickly and highly, and has the expression:
in the formula, γ represents a coefficient corresponding to a kernel function type, and can convert low-dimensional data into high-dimensional data and clearly divide the data.
Finally, forecasting the power demand according to a regression equation of the support vector machine, wherein the forecasting formula is (9):
where a is the Lagrangian coefficient, b represents the threshold for sample classification, i is the time,
the method comprises the following steps of taking a kernel function as a reference, taking x as a meteorological factor of a to-be-predicted time period of a target area, and taking m as the number of samples.
In specific implementation, according to the support vector machine principle, a regression equation as formula (9) is finally generated, the equation (9) can be a support vector machine power demand prediction model, the equation can obtain prediction of power demand according to meteorological factor x, namely the input of the support vector machine power demand prediction model is the dominant meteorological factor of a target region to-be-predicted time period, and the output of the model can be the power demand of the target region to-be-predicted time period.
In summary, in an embodiment, the method for predicting the power demand may further include: the method comprises the following steps of establishing a support vector machine power demand prediction model according to the following method:
acquiring sample data;
dividing sample data into a training set, a test set and a verification set;
training a support vector machine model by using a training set to obtain a support vector machine power demand prediction model;
testing the support vector machine power demand prediction model by using a test set to obtain a tested support vector machine power demand prediction model;
and verifying the tested support vector machine power demand prediction model by using a verification set to obtain the pre-established support vector machine power demand prediction model.
In specific implementation, the detailed implementation scheme of the pre-established support vector machine power demand prediction model further improves the power demand prediction precision.
In one embodiment, obtaining sample data may include:
obtaining historical dominant meteorological factor data and historical power load data corresponding to the historical dominant meteorological factor data according to historical meteorological factor data and historical power load data in a preset time period and a pre-established dominant meteorological factor identification model;
extracting historical dominant meteorological factor data and characteristics of historical power load data corresponding to the historical dominant meteorological factor data by adopting a random forest algorithm to form a dominant meteorological characteristic library and a power load characteristic library;
and obtaining sample data of power demand prediction according to the dominant meteorological feature library and the power load feature library.
In specific implementation, the historical dominant meteorological factor data are obtained by identifying the historical data, the features of the historical dominant meteorological factor data and the corresponding historical power load data are extracted by adopting a random forest algorithm, important features (key features) can be obtained, a dominant meteorological feature library and a power load feature library are further established, and finally, the precision of power demand prediction is further improved by the detailed implementation scheme of obtaining the sample data of power demand prediction.
In one embodiment, the method for extracting the historical dominant meteorological factor data and the characteristics of the historical power load data corresponding to the historical dominant meteorological factor data by using the random forest algorithm to form a dominant meteorological characteristic library and a power load characteristic library may include:
extracting key features of the historical dominant meteorological factors and the corresponding historical power load data thereof as key feature vectors of the historical dominant meteorological factors and the corresponding historical power load data thereof by adopting a random forest algorithm according to the following formula:
during specific implementation, the importance index data is obtained according to the formula, so that key feature vectors are obtained, the weather feature library and the power load feature library are further led, and the accuracy of power demand prediction is further improved.
Second, the above step 101 is described.
In specific implementation, the target area may be any area where the power demand needs to be predicted, such as beijing chang ping; the period to be predicted may be any future period, for example, 3 months in the future.
Step 102 is introduced.
In specific implementation, the specific implementation of step 102 may refer to the implementation of identifying the dominant meteorological factor in the modeling process, which is not described herein again.
Step 103 is described.
In one embodiment, obtaining the power demand of the target area in the period to be predicted according to the dominant meteorological factors of the target area in the period to be predicted and a pre-established support vector machine power demand prediction model may include:
extracting the features of the dominant meteorological factors by adopting a random forest algorithm to obtain feature vectors of the dominant meteorological factors;
and inputting the characteristic vector of the dominant meteorological factor into a pre-established support vector machine power demand prediction model to obtain the power demand of the target area in the period to be predicted.
In specific implementation, the input of the support vector machine power demand prediction model is meteorological factors, and the output is a prediction result of the power demand.
In specific implementation, the pre-established support vector machine power demand prediction model can be regarded as two parts, wherein the first part is a feature extraction part, the first part firstly adopts a random forest algorithm to extract features of the dominant meteorological factors to obtain feature vectors of the dominant meteorological factors and obtain feature vectors of important dominant meteorological factors, and then the second part is a prediction part, and the second part inputs the feature vectors of the dominant meteorological factors into the pre-established support vector machine power demand prediction model to obtain a prediction result. This particular predictive embodiment further improves the accuracy of the prediction of the power demand.
In one embodiment, the method for extracting the features of the dominant meteorological factors by using a random forest algorithm to obtain the feature vector of the dominant meteorological factors comprises the following steps:
extracting key features of the dominant meteorological factors as key feature vectors (feature vectors of important dominant meteorological factors) by adopting a random forest algorithm according to the following formula:
in specific implementation, the important (key) characteristic vector of the dominant meteorological factors is obtained through the formula, and then subsequent power demand prediction is carried out, so that the precision of the power demand prediction is further improved.
Fifth, the step of further updating the model after the step 103 is described.
In one embodiment, the method for predicting the power demand may further include:
obtaining a current power demand prediction result;
and adjusting the power demand prediction model of the support vector machine according to the current power demand prediction result to obtain an updated power demand prediction model of the support vector machine.
During specific implementation, the current power demand prediction result is a real-time power demand prediction result, and according to the current power demand prediction result, the power demand prediction model of the support vector machine is adjusted to obtain a model with higher prediction precision, so that power demand prediction is performed, and the precision of power demand prediction is further improved.
In specific implementation, after the prediction model process and the prediction model are optimized, a post-evaluation system is utilized to feed back the prediction result in real time and dynamically adjust the model, so that the dynamic optimization model is realized, and the prediction precision is kept. The evaluation index has a root mean square error and an average relative error for evaluation, and the specific formula is as follows:
(1) root mean square error
Is the square root of the ratio of the sum of the squares of the deviations of the observations from the true values to the number m of observations.
Is used to measure the deviation between the observed value and the true value.
Wherein, PisRepresenting the actual load value, PiyRepresenting the predicted load value.
(2) Average relative error
The average relative error is an average value of the relative errors, and the average relative error is generally expressed by an absolute value, i.e., an absolute value of the average relative error.
Wherein n represents the predicted load number, PisRepresenting the actual load value, PiyRepresenting the predicted load value.
For convenience of understanding, the power demand prediction method provided by the embodiment of the invention is described in detail below with reference to fig. 2 to 9 as another example.
Examples are: the analysis is carried out on the three types of loads of industry, business and residents of the city.
1. Weather influence analysis of each season load
And respectively calculating the correlation degree of the weather information of the resident, commercial and industrial loads in winter and summer by using a random forest algorithm. As can be seen from fig. 2 to 7, the correlation between the load change and the temperature information is the largest, a certain correlation also exists with the relative humidity, and the influence of the air pressure and the wind speed on various loads is small. Meanwhile, the load of residents is more sensitive to weather changes, and the highest temperature in summer and the lowest temperature in winter both have great influence on the electricity consumption of residents. Due to the strong regularity of commercial and industrial loads, which are more susceptible to temperature, and the relatively small exposure to weather than residential loads, production schedules have some correlation with average temperature.
The relationship between the power load and the temperature is further analyzed, as shown in fig. 8. A linear relation exists between the temperature and the power load, the temperature is comfortable in the area generally, and partial time periods with lower temperature exist. When the temperature is lower, the air conditioner or other heating equipment is used, so that the power load demand is increased, and the accuracy of the random forest algorithm on the correlation analysis of the meteorological features is proved through the analysis side of the relationship between the air conditioner and the heating equipment.
2. Weather influence analysis of each season load
The load is predicted separately based on the analysis of weather characteristics as can be seen in fig. 9, where the test data range analysis selects the daily load for analysis within a month. As shown in fig. 9, through experimental comparison analysis, the prediction effect of the RF-SVM model according to the embodiment of the present invention is better than that of the neural network model. The reason is that after the meteorological features are analyzed through RF and correlated, features which have larger influence on future trend prediction can be selected, so that the model has good generalization capability and prediction effect.
In summary, the power demand prediction method provided by the embodiment of the present invention realizes:
(1) the method realizes intelligent classification of information of large customers by means of classification standards and a power big data technology;
(2) when various influence factors exist, the method can adopt a random forest algorithm and adopts a mode of returning repeated sampling to extract load data and characteristic vectors of various meteorological index data, so that leading factors are identified and an influence factor index database is established;
(3) according to the method, a prediction model of the power demand can be established by adopting a support vector machine based on analysis of influence factors, so that accurate prediction of the power demand is realized, and the prediction precision is improved.
The embodiment of the invention also provides a device for predicting the power demand, which is described in the following embodiment. Because the principle of solving the problems of the device is similar to the method for predicting the power demand, the implementation of the device can refer to the implementation of the method, and repeated details are not repeated.
Fig. 10 is a schematic structural diagram of an apparatus for predicting power demand according to an embodiment of the present invention, as shown in fig. 10, the apparatus includes:
the acquiring unit 01 is used for acquiring meteorological factors of a time period to be predicted in a target area;
the identification unit 02 is used for acquiring the dominant meteorological factor of the target area in the period to be predicted according to the meteorological factor of the target area in the period to be predicted and a pre-established dominant meteorological factor identification model;
the prediction unit 03 is used for obtaining the power demand of the target area in the period to be predicted according to the dominant meteorological factors of the target area in the period to be predicted and a pre-established support vector machine power demand prediction model; the support vector machine power demand prediction model is pre-established according to a plurality of dominant meteorological characteristic samples and power load characteristic samples.
In one embodiment, the prediction device of the power demand may further include: the establishing unit is used for establishing a support vector machine power demand prediction model according to the following method:
acquiring sample data;
dividing sample data into a training set, a test set and a verification set;
training a support vector machine model by using a training set to obtain a support vector machine power demand prediction model;
testing the support vector machine power demand prediction model by using a test set to obtain a tested support vector machine power demand prediction model;
and verifying the tested support vector machine power demand prediction model by using a verification set to obtain the pre-established support vector machine power demand prediction model.
In one embodiment, obtaining sample data may include:
obtaining historical dominant meteorological factor data and historical power load data corresponding to the historical dominant meteorological factor data according to historical meteorological factor data and historical power load data in a preset time period and a pre-established dominant meteorological factor identification model;
extracting historical dominant meteorological factor data and characteristics of historical power load data corresponding to the historical dominant meteorological factor data by adopting a random forest algorithm to form a dominant meteorological characteristic library and a power load characteristic library;
and obtaining sample data of power demand prediction according to the dominant meteorological feature library and the power load feature library.
In one embodiment, the obtaining unit may be further configured to obtain a current power demand prediction result;
the prediction means of the power demand may further include: and the adjusting unit is used for adjusting the support vector machine power demand prediction model according to the current power demand prediction result to obtain an updated support vector machine power demand prediction model.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the prediction method of the power demand when executing the computer program.
An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program for executing the above power demand prediction method is stored.
In the embodiment of the invention, compared with the technical scheme of predicting the power demand analysis by using a regression model, a neural network and the like in the prior art, the prediction scheme of the power demand realizes accurate identification of the dominant meteorological factors, and the prediction model of the power demand is established by using a support vector machine based on the analysis of the dominant meteorological factors, so that the accurate prediction of the power demand is realized, and the prediction precision of the power demand is improved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method of predicting a demand for electric power, comprising:
acquiring meteorological factors of a time period to be predicted in a target area;
obtaining the dominant meteorological factors of the target area in the period to be predicted according to the meteorological factors of the target area in the period to be predicted and a pre-established dominant meteorological factor identification model;
obtaining the power demand of the target area in the period to be predicted according to the dominant meteorological factors of the target area in the period to be predicted and a pre-established support vector machine power demand prediction model; the support vector machine power demand prediction model is pre-established according to a plurality of dominant meteorological characteristic samples and power load characteristic samples.
2. The method of forecasting power demand as set forth in claim 1, further including: the method comprises the following steps of establishing a support vector machine power demand prediction model according to the following method:
acquiring sample data;
dividing sample data into a training set, a test set and a verification set;
training a support vector machine model by using a training set to obtain a support vector machine power demand prediction model;
testing the support vector machine power demand prediction model by using a test set to obtain a tested support vector machine power demand prediction model;
and verifying the tested support vector machine power demand prediction model by using a verification set to obtain the pre-established support vector machine power demand prediction model.
3. The method of forecasting power demand according to claim 2, wherein obtaining sample data comprises:
obtaining historical dominant meteorological factor data and historical power load data corresponding to the historical dominant meteorological factor data according to historical meteorological factor data and historical power load data in a preset time period and a pre-established dominant meteorological factor identification model;
extracting historical dominant meteorological factor data and characteristics of historical power load data corresponding to the historical dominant meteorological factor data by adopting a random forest algorithm to form a dominant meteorological characteristic library and a power load characteristic library;
and obtaining sample data of power demand prediction according to the dominant meteorological feature library and the power load feature library.
4. The method of forecasting power demand as set forth in claim 1, further including:
obtaining a current power demand prediction result;
and adjusting the power demand prediction model of the support vector machine according to the current power demand prediction result to obtain an updated power demand prediction model of the support vector machine.
5. An apparatus for predicting a demand for electric power, comprising:
the acquiring unit is used for acquiring meteorological factors of a time period to be predicted in a target area;
the identification unit is used for acquiring the dominant meteorological factor of the target area in the period to be predicted according to the meteorological factor of the target area in the period to be predicted and a pre-established dominant meteorological factor identification model;
the prediction unit is used for obtaining the power demand of the target area in the period to be predicted according to the dominant meteorological factors of the target area in the period to be predicted and a pre-established support vector machine power demand prediction model; the support vector machine power demand prediction model is pre-established according to a plurality of dominant meteorological characteristic samples and power load characteristic samples.
6. The power demand prediction apparatus of claim 5, further comprising: the establishing unit is used for establishing a support vector machine power demand prediction model according to the following method:
acquiring sample data;
dividing sample data into a training set, a test set and a verification set;
training a support vector machine model by using a training set to obtain a support vector machine power demand prediction model;
testing the support vector machine power demand prediction model by using a test set to obtain a tested support vector machine power demand prediction model;
and verifying the tested support vector machine power demand prediction model by using a verification set to obtain the pre-established support vector machine power demand prediction model.
7. The apparatus for forecasting electric power demand according to claim 6, wherein the obtaining of sample data comprises:
obtaining historical dominant meteorological factor data and historical power load data corresponding to the historical dominant meteorological factor data according to historical meteorological factor data and historical power load data in a preset time period and a pre-established dominant meteorological factor identification model;
extracting historical dominant meteorological factor data and characteristics of historical power load data corresponding to the historical dominant meteorological factor data by adopting a random forest algorithm to form a dominant meteorological characteristic library and a power load characteristic library;
and obtaining sample data of power demand prediction according to the dominant meteorological feature library and the power load feature library.
8. The prediction apparatus of power demand according to claim 5, wherein the obtaining unit is further configured to obtain a current power demand prediction result;
the power demand prediction apparatus further includes: and the adjusting unit is used for adjusting the support vector machine power demand prediction model according to the current power demand prediction result to obtain an updated support vector machine power demand prediction model.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 4.
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