CN116383598B - Power consumer energy stability analysis method based on autoregressive algorithm - Google Patents
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Abstract
The invention relates to the technical field of power consumption stability analysis, in particular to an energy consumption stability analysis method for power users based on an autoregressive algorithm. The method comprises the steps of dividing the power users into areas, counting the data of the power users in the areas on the power energy consumption on different dates, establishing a regression model, comparing the regression models of the power users with the same use characteristics in each area, and analyzing and summarizing the relationship between the different dates and the power energy consumption in the regression model. In the invention, the power consumption of a range is predicted by dividing the power users in the range into a plurality of areas and then combining the small areas with the same power consumption trend according to the increase and decrease conditions of the power consumption displayed by the linear autoregressive model, so that the power users with the same power consumption trend are integrated, and the power consumption of a range is predicted by predicting the power consumption of the power users in a large area when the power consumption of the power users in a range is predicted.
Description
Technical Field
The invention relates to the technical field of power consumption stability analysis, in particular to an energy consumption stability analysis method for power users based on an autoregressive algorithm.
Background
The electric power is used as the most main energy source at present, is closely related to the life of human beings, and cannot be effectively stored at present, so when the electric power is distributed, the corresponding electric quantity supply is needed according to the electricity consumption condition of the area, the loss of electric energy is reduced, the redundant electric quantity is transmitted to other places, and the effective utilization of the electric energy is ensured.
The power consumption of the power users in the area is different, when the analysis of the power consumption of the area is carried out, the error of the analyzed data is overlarge, and then the power transmission errors are caused, so that the power waste is caused, meanwhile, in the area, the power consumption condition of the power users is related to the working time, such as residents with children learning nearby schools, when the residents encounter the weekends or holidays, the residents can leave the ground with the children, at the moment, the power consumption is different in peace and peace, when the statistics of the power is carried out, the excessive calculation of the power and the power consumption is caused, and therefore, a method is needed to stably analyze the power consumption of the power users in the area, so that the effective supply of the power is realized.
Disclosure of Invention
The invention aims to provide an energy consumption stability analysis method for power users based on an autoregressive algorithm so as to solve the problems in the background technology.
In order to achieve the above object, the present invention provides an energy consumption stability analysis method for electric power consumers based on an autoregressive algorithm, comprising the steps of:
s1, dividing a plurality of areas of power users, distinguishing the use characteristics of the power users in each divided area, and dividing the power users with the same use characteristics into the same cell;
s2, counting data of power consumption of power users in each district on different dates, and establishing a regression model for the counted data information by adopting an autoregressive algorithm;
s3, comparing regression models of the power users with the same use characteristics in each region, and inducing similar regression models together;
s4, analyzing the relation between different dates and power energy consumption in the generalized regression model, and judging the relation between the power energy consumption and the date types corresponding to the different dates according to the power energy consumption relation;
s5, analyzing the stability of the change between the power consumption of the small area and the date according to the power consumption relation of the small area, and integrating the power consumption data of the small areas with similar power consumption regression models in the areas to form large-area power consumption data;
s6, predicting the future power consumption of the large area according to the relationship between the judged power consumption and the date types corresponding to different dates.
As a further improvement of the present technical solution, the usage characteristics of the power consumer in S1 include the following types:
stable electrical characteristics of the resident where the resident can live stably;
industrial electrical characteristics, namely, electric energy consumed by industrial production;
time-saving electricity utilization characteristics of residents, such as residents with child school in the vicinity of a school, do not live there during a holiday, and there is time-saving electricity utilization.
As a further improvement of the present technical solution, in the step S1, when dividing the area, the power users within a range are divided into areas, and then the power users within the area are divided into small areas according to the power usage characteristics of the power users within the area, so that the adjacent power users with the same usage characteristics are divided into the same small areas, and the small areas are used as the minimum units of the power stability analysis.
As a further improvement of the technical scheme, in S2, in the data of the power consumption of the power consumer in each small area on different dates, the different dates include working days, rest days and holidays, and when the power consumption of the power consumer on different dates is counted, the corresponding date of the power consumption of the current date is marked.
As a further improvement of the present technical solution, the formula of the autoregressive algorithm in S2 is:
，
wherein ,is an autoregressive model value, a predicted value,/->Is a known value, i.e. a known power consumption value, a is a parameter, +.>To calculate the errorWhite noise of e,>t is the set of values for the date.
As a further improvement of the technical scheme, the step of establishing the regression model in the S2 is as follows:
(1) collecting the electricity consumption of each date of the small area according to the electricity consumption of the small area predicted as required, so that the electricity consumption of the corresponding date and the electricity consumption of the corresponding date have comparability, and dividing the electricity consumption of the corresponding date and the electricity consumption of the corresponding date into dependent variable and self-variable series; wherein, the independent variable is a specified date, for example, 1 month 2 is one independent variable, 1 month 3 is another independent variable, and the electric quantity consumed by the corresponding date is the dependent variable;
(2) calculating an autocorrelation coefficient of the specified date, determining an independent variable according to the size of the autocorrelation coefficient, selecting the autocorrelation coefficient of the autocorrelation coefficient, and establishing an autoregressive model according to the selected autocorrelation coefficient, wherein the autoregressive model is a linear model.
As a further improvement of the present technical solution, the formula for calculating the autocorrelation coefficient of the specified date self-variation sequence is:
，
wherein ,for autocorrelation coefficients, T is the set of values for the date, e.g., T includes the entire date of 2023, -/->、/>All are specified dates, & gt>、/>All of which are the power consumed by the specified date, e, < >>And E, r optionally the date in T, E represents mathematical expectations and D represents variance values.
As a further improvement of the technical scheme, the step of comparing the regression model in the step S3 is as follows:
the first step: identifying the growth and descent conditions of the linear regression model on different dates;
and a second step of: the regression models of the power consumption in the plurality of cells are compared according to the linear regression model,
and a third step of: the linear regression models with the same growing or descending trend are integrated together and marked as the same class of consumption users, and the power users with the same growing or descending trend of the linear regression models are marked so as to integrate the user information of the same power consumption in the later stage, thereby facilitating the prediction of the power stability in the later stage.
Compared with the prior art, the invention has the beneficial effects that:
1. in the power consumption stability analysis method for the power users based on the autoregressive algorithm, the power users in one range are divided into a plurality of areas, the power users with the same power consumption characteristics in each area are divided into one area, so that the power consumption in each area is the same, meanwhile, the statistics of the power consumption is carried out on the small areas in the plurality of areas, then, the establishment of a linear autoregressive model is carried out according to the statistical data, and then, the small areas with the same power consumption trend are combined to form a large area through the increase and decrease of the power consumption displayed by the linear autoregressive model, so that the power users with the same power consumption trend are integrated, and when the power consumption used by the power users in one range is predicted, the power consumption in one range is predicted through the prediction of the power users in the large area, the accuracy of the power consumption prediction in one range is improved, the stable analysis of the power consumption of the power users is further realized, and the power consumption of the power users is not wasted is ensured.
Drawings
Fig. 1 is an overall flow block diagram of embodiment 1 of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1:
the power consumption of the power users in the area is different, when the analysis of the power consumption of the area is carried out, the error of the analyzed data is overlarge, and then the power transmission errors are caused, so that the power waste is caused, meanwhile, in the area, the power consumption condition of the power users is related to the working time, such as residents with children learning nearby schools, when the residents encounter the weekends or holidays, the residents can leave the ground with the children, at the moment, the power consumption is different in peace and peace, when the statistics of the power is carried out, the excessive calculation of the power and the power consumption is caused, and therefore, a method is needed to stably analyze the power consumption of the power users in the area, so that the effective supply of the power is realized.
In order to complete the stable prediction of the electricity consumption of the power consumer in a range, so that the electricity supplied to the range is enough to be used by the power consumer without wasting the electricity, referring to fig. 1, the invention provides an energy consumption stability analysis method of the power consumer based on an autoregressive algorithm, comprising the following steps:
s1, dividing a plurality of areas of power users, distinguishing the use characteristics of the power users in each divided area, and dividing the power users with the same use characteristics into the same cell;
the usage characteristics of the power consumer in S1 include the following types:
the stable electricity utilization characteristics of residents can be realized, the residents can stably live in the place, the consumed electric quantity is stable, and the consumption of the electric quantity is not greatly increased or reduced;
industrial electrical characteristics, namely, the electric energy consumed by industrial production, are larger in consumed electric quantity, and the electric quantity consumed by factories is smoother, so that the electric energy cannot be greatly increased or reduced;
the electricity consumption characteristics of time-saving residents, such as residents with child school nearby schools, do not live there during a holiday, and electricity consumption is time-saving, so that the electricity consumed by the power users is stable during child school, and when the residents encounter a holiday, such as weekend or national specified holidays, the residents can leave the range with the child, and the electricity consumption condition can change.
In S1, when dividing the area, dividing the power users in a range into areas, then dividing the power users in the areas into small ranges according to the power use characteristics of the power users in the areas, dividing the adjacent power users with the same use characteristics into the same small area, taking the small area as the minimum unit of power stability analysis, counting the power consumption by taking the small area as the minimum unit, and the power users in the small area have the same power consumption use characteristics, so that the stability of the power consumption is conveniently analyzed, and the difficulty of the power analysis of the power users in a range is reduced.
S2, counting data of power consumption of power users in each district on different dates, and establishing a regression model for the counted data information by adopting an autoregressive algorithm;
and S2, counting the data of the power consumption of the power users in each small area on different dates, wherein the different dates comprise working days, rest days and holiday days, and marking the corresponding date of the power consumption of the current day when the power consumption of the power users on the different dates is counted.
Wherein, the formula of the autoregressive algorithm in S2 is as follows:
，
wherein ,is an autoregressive model value, a predicted value,/->Is a known value, i.e. a known power consumption value, a is a parameter, +.>White noise of calculation error, e, is->T is the set of values for the date.
The method comprises the steps that the electric power consumption data of the counted electric power users on different dates are subjected to autoregressive calculation, so that an autoregressive model is built according to the autoregressive calculation result, and the electric quantity consumed by the electric power users in a range is predicted according to the autoregressive model;
the step of establishing a regression model according to the result of the autoregressive calculation is as follows:
(1) collecting the electricity consumption of each date of the small area according to the electricity consumption of the small area predicted as required, so that the electricity consumption of the corresponding date and the electricity consumption of the corresponding date have comparability, and dividing the electricity consumption of the corresponding date and the electricity consumption of the corresponding date into dependent variable and self-variable series; wherein, the independent variable is a specified date, for example, 1 month 2 is one independent variable, 1 month 3 is another independent variable, and the electric quantity consumed by the corresponding date is the dependent variable;
(2) calculating an autocorrelation coefficient of the specified date, determining an independent variable according to the size of the autocorrelation coefficient, selecting the autocorrelation coefficient of the autocorrelation coefficient, and establishing an autoregressive model according to the selected autocorrelation coefficient, wherein the autoregressive model is a linear model.
The formula for calculating the autocorrelation coefficient of the specified date self-variation sequence is:
，
wherein ,for autocorrelation coefficients, T is the set of values for the date, e.g., T includes the entire date of 2023, -/->、/>All are specified dates, & gt>、/>All of which are the power consumed by the specified date, e, < >>And E, r optionally the date in T, E represents mathematical expectations and D represents variance values.
And carrying out an autoregressive model according to the collected data of the electric quantity consumption of the electric power users, so that the electric power consumption of each small area of the electric power users on different dates forms a linear autoregressive model, and the electric power and the electric quantity can be stably analyzed according to the increasing or decreasing trend of the linear autoregressive model in the later period.
S3, comparing regression models of the power users with the same use characteristics in each region, and inducing similar regression models together;
the method for comparing the regression models in S3 comprises the following steps:
the first step: identifying the growth and descent conditions of the linear regression model on different dates;
and a second step of: the regression models of the power consumption in the plurality of cells are compared according to the linear regression model,
and a third step of: the linear regression models with the same growing or descending trend are integrated together and marked as the same class of consumption users, and the power users with the same growing or descending trend of the linear regression models are marked so as to integrate the user information of the same power consumption in the later stage, thereby facilitating the prediction of the power stability in the later stage.
S4, analyzing the relation between different dates and power energy consumption in the generalized regression model, and judging the relation between the power energy consumption and the date types corresponding to the different dates according to the power energy consumption relation;
and determining the increase and decrease of the linear autoregressive model of the power consumption on different dates, such as a rest day or a working day, and acquiring the overall power utilization condition of each small-area power consumer.
S5, analyzing the stability of the change between the power consumption of the small area and the date according to the power consumption relation of the small area, and integrating the power consumption data of the small areas with similar power consumption regression models in the areas to form large-area power consumption data;
the small areas with the same electricity consumption trend are integrated together to form a large area, the electricity consumption of the large area is stable, when the electricity consumption of the large area is analyzed, the electricity consumption of the large area is predicted by knowing the change condition of the trend of the linear autoregressive model, the stable analysis of the electricity consumption of the large area is realized, the electricity consumption analysis accuracy of the electricity users of the large area is improved, and the electricity consumption provided for the large area is ensured not to be wasted.
S6, predicting the future power consumption of the large area according to the relationship between the judged power consumption and the date types corresponding to different dates.
The power consumption in each area is identical by dividing the power users in the area into a plurality of areas and dividing the power users with the same power use characteristics in each area into one cell, meanwhile, the power consumption in the areas is counted, then the linear autoregressive model is built according to the counted data, and then the areas with the same power consumption trend are combined to form a large area according to the increase and decrease of the power consumption displayed by the linear autoregressive model, so that the power users with the same power use trend are integrated, when the power consumption used by the power users in one area is predicted, the power consumption in one area is predicted by predicting the power consumption of the power users in the large area, the accuracy of the power consumption prediction in one area is improved, the stable analysis of the power consumption of the power users is further realized, and the transmitted power is ensured not to be wasted.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (1)
1. The power consumer energy stability analysis method based on the autoregressive algorithm is characterized by comprising the following steps of: the method comprises the following steps:
s1, dividing a plurality of areas of power users, distinguishing the use characteristics of the power users in each divided area, and dividing the power users with the same use characteristics into the same cell;
the usage characteristics of the power consumer in S1 include the following types:
stable electrical characteristics of the resident where the resident can live stably;
industrial electrical characteristics, namely, electric energy consumed by industrial production;
time-saving resident electricity utilization characteristics;
when dividing the area in the step S1, dividing the area of the power users in a range, and then dividing the small range according to the power use characteristics of the power users in the area, so that the adjacent power users with the same use characteristics are divided into the same small area, wherein the small area is used as the minimum unit of the power stability analysis;
s2, counting data of power consumption of power users in each district on different dates, and establishing a regression model for the counted data information by adopting an autoregressive algorithm;
in the step S2, statistics is performed on data of power consumption of the power users in each small area on different dates, wherein the different dates comprise working days, rest days and holiday days, and when the statistics is performed on the power consumption of the power users on different dates, the corresponding date of the power consumption of the current date is marked;
the formula of the autoregressive algorithm in the S2 is as follows:
，
wherein ,is an autoregressive model value, a predicted value,/->Is of known value, a is a parameter, < ->White noise, e, +.>T is a numerical value set of dates; the step of establishing a regression model in the step S2 is as follows:
(1) collecting the electricity consumption of each date of the small area according to the electricity consumption of the small area predicted as required, so that the electricity consumption of the corresponding date and the electricity consumption of the corresponding date have comparability, and dividing the electricity consumption of the corresponding date and the electricity consumption of the corresponding date into dependent variable and self-variable series;
(2) calculating an autocorrelation coefficient of the specified date, determining an independent variable according to the size of the autocorrelation coefficient, selecting the autocorrelation coefficient-larger autocorrelation coefficient, and establishing an autoregressive model according to the selected autocorrelation coefficient, wherein the autoregressive model is a linear model;
the formula for calculating the autocorrelation coefficient of the specified date self-variation sequence is:
，
wherein ,for the autocorrelation coefficient, T is the set of values for the date, < >>、/>All are specified dates, & gt>、/>All of which are the power consumed by the specified date, e, < >>And E, r optionally the date in T, E representing mathematical expectations, D representing variance values;
s3, comparing regression models of the power users with the same use characteristics in each region, and inducing similar regression models together;
the step of comparing the regression model in the step S3 is as follows:
the first step: identifying the growth and descent conditions of the linear regression model on different dates;
and a second step of: comparing regression models of electricity consumption in the plurality of cells according to the linear regression models;
and a third step of: integrating linear regression models with the same increasing or decreasing trend together and marking the linear regression models as similar consumption users;
s4, analyzing the relation between different dates and power consumption in the generalized regression model, and judging the relation between the power consumption and the date types corresponding to the different dates according to the power consumption relation, wherein the increase and decrease conditions of the linear autoregressive model of the power consumption at the different dates are determined, and the overall power utilization condition of each small-area power user is obtained;
s5, analyzing the stability of the change between the power consumption of the small area and the date according to the power consumption relation of the small area, and integrating the power consumption data of the small areas with similar power consumption regression models in the areas to form large-area power consumption data;
s6, predicting the future power consumption of the large area according to the relationship between the judged power consumption and the date types corresponding to different dates.
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| CN104254083A (en) * | 2013-06-28 | 2014-12-31 | 华为技术有限公司 | Method and device for predicting business hot spots |
| CN105678398A (en) * | 2015-12-24 | 2016-06-15 | 国家电网公司 | Power load forecasting method based on big data technology, and research and application system based on method |
| KR20190005456A (en) * | 2017-07-06 | 2019-01-16 | 두산중공업 주식회사 | Method and Apparatus for Predicting Power Demand and Apparatus and Method for Controlling Charge/Discharge of ESS based on It |
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