1. The utility model provides a grey theory based power load prediction system and method, which is characterized in that, includes electric wire netting data acquisition module, data analysis module and data display module, electric wire netting data acquisition module and data analysis module communication connection, data analysis module and data display module communication connection, wherein:

the power grid data acquisition module is used for acquiring power load data of each user in a power grid in a set area and sending the power load data to the data analysis module; the system is used for inputting and setting influence factors influencing the power load prediction and sending the influence factors to the data analysis module;

the data analysis module is used for establishing a grey theoretical model for power load prediction, receiving the power load data and the influence factors sent by the power grid data acquisition module, inputting the power load data and the influence factors into the grey theoretical model, performing model calculation to obtain a predicted value, and performing accuracy analysis on the predicted value to obtain an analysis result; sending the predicted value and the analysis result to a data display module;

and the data display module is used for receiving the predicted value and the analysis result sent by the data analysis module and displaying the predicted value and the analysis result.

2. The grey theory-based power load prediction system and the grey theory-based power load prediction method according to claim 1, wherein the power grid data acquisition module comprises a data acquisition end, a data storage end, a data preprocessing end, a data sending end and an influence factor setting end.

3. The grey theory-based power load prediction system and the grey theory-based power load prediction method according to claim 2, wherein the data acquisition end is electrically connected with the data storage end, the data storage end is electrically connected with the data preprocessing end, and the data preprocessing end and the influencing factor setting end are both electrically connected with the data transmitting end.

4. The grey theory based power load forecasting system and the method thereof according to claim 1, wherein the data analysis module comprises a data receiving end, a model building end, a data analysis end, an accuracy analysis end and a data transmission end.

5. The grey theory-based power load forecasting system and the grey theory-based power load forecasting method as claimed in claim 4, wherein the data receiving terminal and the model building terminal are both electrically connected to a data analysis terminal, the data analysis terminal is electrically connected to an accuracy analysis terminal, and the accuracy analysis terminal is electrically connected to a data transmission terminal.

6. The grey theory-based power load forecasting system and the grey theory-based power load forecasting method according to claim 1, further comprising a remote monitoring platform, wherein the remote monitoring platform is electrically connected with the data display module and the power grid data acquisition module.

7. The grey theory-based power load prediction system and the grey theory-based power load prediction method according to claim 6, further comprising a power grid regulation and control platform, wherein the power grid regulation and control platform is electrically connected with the data display module.

8. A method for a grey theory based power load prediction system according to any of the claims 1-7, characterized in that it comprises the steps of:

s001, collecting power load data of each user in a power grid in a set area, storing the power load data into a power load database, and preprocessing the power load data;

s002, setting influence factors influencing power load prediction, and dividing the influence factors into a main influence factor A and a secondary influence factor B;

s003, inputting the preprocessed power load data and the main influence factors S as model variables into a grey theoretical model, calculating the grey theoretical model to obtain a predicted value, and performing accuracy analysis on the predicted value to obtain an analysis result;

s004, outputting the predicted value in the step S003 when the analysis result meets the accuracy setting range, and performing the step S005 when the analysis result does not meet the accuracy setting range;

and S005, inputting the preprocessed power load data, the main influence factor A and the secondary influence factor B serving as model variables into a grey theoretical model, and calculating the grey theoretical model to obtain a predicted value.

9. The gray theory-based power load prediction method according to claim 8, wherein in step S002, the influencing factors influencing the power load prediction include time data, meteorological data and environmental data.

10. The gray theory-based power load prediction method according to claim 8, further comprising the steps of: and after the predicted value is obtained through calculation, displaying the predicted value, sending the predicted value to a power grid regulation and control platform, and carrying out power grid regulation and control by the power grid regulation and control platform.

Background

The power load prediction is an important content, premise and basis of power system planning and power grid operation. Under the situation that energy conservation and environmental protection are vigorously advocated in China to save the existing energy consumption, the accuracy of power load prediction is related to the economic and efficient operation of the whole power grid enterprise and the safe operation of the whole power generation power grid, namely the current situation puts forward a higher standard requirement on the accuracy of power load prediction.

The conventional methods for short-term load prediction commonly used at present include a classical prediction method represented by a time series method and a regression analysis method, and an artificial intelligence method represented by an expert system method, a neural network and a fuzzy logic method. Because the power load change process is a highly complex nonlinear process, the traditional method is difficult to establish an effective mathematical model, so that the accuracy of a prediction result is not high

Therefore, in view of the above situation, there is an urgent need to develop a power load prediction system based on the gray theory and a method thereof to overcome the shortcomings in the current practical application.

Disclosure of Invention

The present invention is directed to a system and method for predicting a power load based on a gray theory, so as to solve the problems mentioned in the background art.

In order to achieve the purpose, the invention provides the following technical scheme:

the utility model provides a power load prediction system based on grey theory, includes electric wire netting data acquisition module, data analysis module and data display module, electric wire netting data acquisition module and data analysis module communication connection, data analysis module and data display module communication connection, wherein:

the power grid data acquisition module is used for acquiring power load data of each user in a power grid in a set area and sending the power load data to the data analysis module; the system is used for inputting and setting influence factors influencing the power load prediction and sending the influence factors to the data analysis module;

the data analysis module is used for establishing a grey theoretical model for power load prediction, receiving the power load data and the influence factors sent by the power grid data acquisition module, inputting the power load data and the influence factors into the grey theoretical model, performing model calculation to obtain a predicted value, and performing accuracy analysis on the predicted value to obtain an analysis result; sending the predicted value and the analysis result to a data display module;

and the data display module is used for receiving the predicted value and the analysis result sent by the data analysis module and displaying the predicted value and the analysis result.

As a further scheme of the invention: the power grid data acquisition module comprises a data acquisition end, a data storage end, a data preprocessing end, a data sending end and an influence factor setting end.

As a further scheme of the invention: the data acquisition end is electrically connected with the data storage end, the data storage end is electrically connected with the data preprocessing end, and the data preprocessing end and the influence factor setting end are both electrically connected with the data sending end.

As a further scheme of the invention: the data analysis module comprises a data receiving end, a model establishing end, a data analysis end, an accuracy analysis end and a data transmission end.

As a further scheme of the invention: the data receiving end and the model establishing end are both electrically connected with the data analysis end, the data analysis end is electrically connected with the accuracy analysis end, and the accuracy analysis end is electrically connected with the data transmission end.

As a further scheme of the invention: the system is characterized by further comprising a remote monitoring platform, wherein the remote monitoring platform is electrically connected with the data display module and the power grid data acquisition module.

As a further scheme of the invention: the power grid control system is characterized by further comprising a power grid control platform, and the power grid control platform is electrically connected with the data display module.

A power load prediction method based on a grey theory comprises the following steps:

s001, collecting power load data of each user in a power grid in a set area, storing the power load data into a power load database, and preprocessing the power load data;

s002, setting influence factors influencing power load prediction, and dividing the influence factors into a main influence factor A and a secondary influence factor B;

s003, inputting the preprocessed power load data and the main influence factors S as model variables into a grey theoretical model, calculating the grey theoretical model to obtain a predicted value, and performing accuracy analysis on the predicted value to obtain an analysis result;

s004, outputting the predicted value in the step S003 when the analysis result meets the accuracy setting range, and performing the step S005 when the analysis result does not meet the accuracy setting range;

s005, inputting the preprocessed power load data, the main influence factor A and the secondary influence factor B serving as model variables into a grey theoretical model, and calculating the grey theoretical model to obtain a predicted value

As a further scheme of the invention: in step S002, the influencing factors influencing the power load prediction include time data, meteorological data, and environmental data.

As a further scheme of the invention: also comprises the following steps: and after the predicted value is obtained through calculation, displaying the predicted value, sending the predicted value to a power grid regulation and control platform, and carrying out power grid regulation and control by the power grid regulation and control platform.

Compared with the prior art, the invention has the beneficial effects that: according to the method, the power grid data acquisition module is used for acquiring the power load data of each user in the power grid of the set area, the power load data is sent to the data analysis module, the data analysis module is used for establishing a grey theoretical model for power load prediction, the power load data and the influence factors sent by the power grid data acquisition module are received and input into the grey theoretical model for model calculation to obtain a predicted value, the predicted value is subjected to accuracy analysis to obtain an analysis result, and therefore the problem of accuracy deviation caused by data quality is reduced, and the historical load sequence prediction effect is improved.

Drawings

Fig. 1 is a block diagram showing a configuration of a power load prediction system based on the gray theory.

Fig. 2 is a block diagram of a structure of a power grid data acquisition module in a power load prediction system based on a grey theory.

Fig. 3 is a block diagram of a data analysis module in the power load prediction system based on the gray theory.

Fig. 4 is a flowchart of a power load prediction method based on the gray theory.

Detailed Description

The technical solution of the present patent will be described in further detail with reference to the following embodiments.

Reference will now be made in detail to embodiments of the present patent, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present patent and are not to be construed as limiting the present patent.

Example 1

Referring to fig. 1 to 3, in an embodiment of the present invention, a power load prediction system based on a gray theory includes a power grid data acquisition module 1, a data analysis module 2, and a data display module 3, where the power grid data acquisition module 1 is in communication connection with the data analysis module 2, and the data analysis module 2 is in communication connection with the data display module 3, where:

the power grid data acquisition module 1 is used for acquiring power consumption load data of each user in a power grid in a set area and sending the power consumption load data to the data analysis module 2; the system is used for inputting and setting influence factors influencing the power load prediction and sending the influence factors to the data analysis module 2;

the data analysis module 2 is used for establishing a grey theoretical model for power load prediction, receiving the power load data and the influence factors sent by the power grid data acquisition module 1, inputting the power load data and the influence factors into the grey theoretical model, performing model calculation to obtain a predicted value, and performing accuracy analysis on the predicted value to obtain an analysis result; sending the predicted value and the analysis result to a data display module 3;

and the data display module 3 is used for receiving the predicted value and the analysis result sent by the data analysis module 2 and displaying the predicted value and the analysis result.

It should be noted that, in the embodiment of the present invention, the method for establishing the gray theoretical model includes:

(1) the method comprises the following steps of taking power load data and influence factors as model variables n, and setting an original sequence of a model as follows:

x⁽⁰⁾(t)＝{x⁽⁰⁾(1)，x⁽⁰⁾(2)，...，x⁽⁰⁾(n)}；

(2) for x⁽⁰⁾Adding the accumulations to obtain:

x⁽¹⁾(t)＝{x⁽¹⁾(1)，x⁽¹⁾(2)，...，x⁽¹⁾(n)}；

(3) in z⁽¹⁾(t) is x⁽¹⁾(k) The close-proximity mean generation sequence of (1):

z⁽¹⁾(t)＝{z⁽¹⁾(1)，z⁽¹⁾(2)，...，z⁽¹⁾(n)}；

(4) and accumulating the gray differential equation and the generated sequence to obtain a prediction model:

X^{^(0)}(t+1)＝{X⁽⁰⁾(1)-b/a}e^-at+b/a。

in the embodiment of the invention, the power grid data acquisition module 1 comprises a data acquisition end 11, a data storage end 12, a data preprocessing end 13, a data transmitting end 14 and an influence factor setting end 15;

in the embodiment of the present invention, the data acquisition end 11 is electrically connected to the data storage end 12, the data storage end 12 is electrically connected to the data preprocessing end 13, and both the data preprocessing end 13 and the influencing factor setting end 15 are electrically connected to the data sending end 14;

in the embodiment of the present invention, the data analysis module 2 includes a data receiving end 21, a model establishing end 22, a data analysis end 23, an accuracy analysis end 24, and a data transmission end 25;

in the embodiment of the present invention, the data receiving terminal 21 and the model establishing terminal 22 are both electrically connected to the data analyzing terminal 23, the data analyzing terminal 23 is electrically connected to the precision analyzing terminal 24, and the precision analyzing terminal 24 is electrically connected to the data transmitting terminal 25.

In another embodiment of the present invention, the system further comprises a remote monitoring platform 4, wherein the remote monitoring platform 4 is electrically connected with the data display module 3 and the power grid data acquisition module 1;

in another embodiment of the present invention, the power grid monitoring system further comprises a power grid monitoring platform 5, wherein the power grid monitoring platform 5 is electrically connected to the data display module 3.

Example 2

Referring to fig. 1 to 3, in an embodiment of the present invention, a power load prediction system based on a gray theory includes a power grid data acquisition module 1, a data analysis module 2, and a data display module 3, where the power grid data acquisition module 1 is in communication connection with the data analysis module 2, and the data analysis module 2 is in communication connection with the data display module 3, where:

the power grid data acquisition module 1 is used for acquiring power consumption load data of each user in a power grid in a set area and sending the power consumption load data to the data analysis module 2; the system is used for inputting and setting influence factors influencing the power load prediction and sending the influence factors to the data analysis module 2;

the data analysis module 2 is used for establishing a grey theoretical model for power load prediction, receiving the power load data and the influence factors sent by the power grid data acquisition module 1, inputting the power load data and the influence factors into the grey theoretical model, performing model calculation to obtain a predicted value, and performing accuracy analysis on the predicted value to obtain an analysis result; sending the predicted value and the analysis result to a data display module 3;

and the data display module 3 is used for receiving the predicted value and the analysis result sent by the data analysis module 2 and displaying the predicted value and the analysis result.

It should be noted that, in the embodiment of the present invention, the method for establishing the gray theoretical model includes:

(1) the method comprises the following steps of taking power load data and influence factors as model variables n, and setting an original sequence of a model as follows:

x⁽⁰⁾(t)＝{x⁽⁰⁾(1)，x⁽⁰⁾(2)，...，x⁽⁰⁾(n)}；

(2) for x⁽⁰⁾Adding the accumulations to obtain:

x⁽¹⁾(t)＝{x⁽¹⁾(1)，x⁽¹⁾(2)，...，x⁽¹⁾(n)}；

(3) in z⁽¹⁾(t) is x⁽¹⁾(k) The close-proximity mean generation sequence of (1):

z⁽¹⁾(t)＝{z⁽¹⁾(1)，z⁽¹⁾(2)，...，z⁽¹⁾(n)}；

(4) and accumulating the gray differential equation and the generated sequence to obtain a prediction model:

X^∧(0)(t+1)＝{X⁽⁰⁾(1)-b/a}e^-at+b/a。

in the embodiment of the invention, the power grid data acquisition module 1 comprises a data acquisition end 11, a data storage end 12, a data preprocessing end 13, a data transmitting end 14 and an influence factor setting end 15;

in the embodiment of the present invention, the data acquisition end 11 is electrically connected to the data storage end 12, the data storage end 12 is electrically connected to the data preprocessing end 13, and both the data preprocessing end 13 and the influencing factor setting end 15 are electrically connected to the data sending end 14;

in the embodiment of the present invention, the data analysis module 2 includes a data receiving end 21, a model establishing end 22, a data analysis end 23, an accuracy analysis end 24, and a data transmission end 25;

in the embodiment of the present invention, the data receiving terminal 21 and the model establishing terminal 22 are both electrically connected to the data analyzing terminal 23, the data analyzing terminal 23 is electrically connected to the precision analyzing terminal 24, and the precision analyzing terminal 24 is electrically connected to the data transmitting terminal 25.

In another embodiment of the present invention, the system further comprises a remote monitoring platform 4, wherein the remote monitoring platform 4 is electrically connected with the data display module 3 and the power grid data acquisition module 1;

in another embodiment of the present invention, the power grid monitoring system further comprises a power grid monitoring platform 5, wherein the power grid monitoring platform 5 is electrically connected to the data display module 3.

Referring to fig. 4, a method for predicting a power load based on a gray theory includes the following steps:

s001, collecting power load data of each user in a power grid in a set area, storing the power load data into a power load database, and preprocessing the power load data;

s002, setting influence factors influencing power load prediction, and dividing the influence factors into a main influence factor A and a secondary influence factor B;

s003, inputting the preprocessed power load data and the main influence factors S as model variables into a grey theoretical model, calculating the grey theoretical model to obtain a predicted value, and performing accuracy analysis on the predicted value to obtain an analysis result;

s004, outputting the predicted value in the step S003 when the analysis result meets the accuracy setting range, and performing the step S005 when the analysis result does not meet the accuracy setting range;

and S005, inputting the preprocessed power load data, the main influence factor A and the secondary influence factor B serving as model variables into a grey theoretical model, and calculating the grey theoretical model to obtain a predicted value.

In step S002 of the embodiment of the present invention, the influencing factors influencing the power load prediction include time data, meteorological data, and environmental data;

in another embodiment of the present invention, the method further comprises the steps of: and after the predicted value is obtained through calculation, displaying the predicted value, sending the predicted value to the power grid regulation and control platform 5, and carrying out power grid regulation and control by the power grid regulation and control platform 5.

The above is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, it is possible to make several variations and modifications without departing from the concept of the present invention, and these should be considered as the protection scope of the present invention, which will not affect the effect of the implementation of the present invention and the utility of the patent.