CN113837470A - Method for predicting electric energy consumption of smart power grid - Google Patents
Method for predicting electric energy consumption of smart power grid Download PDFInfo
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- CN113837470A CN113837470A CN202111125277.3A CN202111125277A CN113837470A CN 113837470 A CN113837470 A CN 113837470A CN 202111125277 A CN202111125277 A CN 202111125277A CN 113837470 A CN113837470 A CN 113837470A
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Abstract
The invention discloses a method for predicting the electric energy usage amount of a smart grid, and particularly relates to the field of electric energy, wherein the method comprises the following steps of S1: establishing a power grid electric energy use amount prediction index system; s2: collecting data; s3: acquiring the annual growth rate P of the GDP corresponding to the acquisition period region, and acquiring the annual average growth rate E of the total power consumption in the corresponding acquisition period region; s4: establishing a corresponding working condition relation table; s5: the working condition matching analysis is carried out, and the development expectation in the acquisition period region and the period to be predicted are compared according to the market change condition; s6: and the calculated electric energy is used for predicting and storing the electric energy into a database. The method comprehensively considers the result of the influence of the time difference and the regional change on the power consumption of the power grid, fully combines the change situation of the power market, better fits the real situation of the power market, avoids high deviation caused by the fact that a mathematical model method only predicts from a digital change rule, and improves the prediction precision of the power consumption in the whole society.
Description
Technical Field
The invention relates to the technical field of electric energy, in particular to a method for predicting electric energy consumption of an intelligent power grid.
Background
Ensuring a balance of supply and demand for the use of electrical energy is an important issue in electrical networks. When the supplied electric energy is less than the required electric energy, the interruption of partial electric power service can be caused, and even large-scale power failure can be caused; if the supplied power is greater than the demanded power, the excess power needs to be additionally transmitted or stored using a suitably sized energy storage device, and the transmission, storage, and maintenance caused by them can significantly increase the cost of power usage. In order to solve the problem, the electric energy consumption of the power grid must be rapidly and accurately predicted, so that the balance of supply and demand of the electric energy is ensured, and the economic benefit and the social benefit of the power network are improved.
The smart grid is a modern power network. Compared with the traditional power grid, the intelligent power grid is more reliable, safe and efficient, and the intelligent power grid can obtain more detailed power utilization data by using advanced sensing, measuring, communication and other technologies, so that the power grid can be helped to predict the power utilization more accurately.
At present, the existing electric energy usage prediction method in the power grid mainly has the following problems:
1. the prediction accuracy is not high. The time sequence formed by the change of the electric energy consumption along with the time in the power grid is influenced by multiple factors such as economic development, industrial structure, climate and the like, so that the prediction precision is easily influenced.
2. The adaptability or real-time performance is poor. Most of the existing electric energy usage amount prediction methods adopt mathematical models to model prediction, the modeling process is complex, and related parameters cannot be adjusted independently when some factors influencing the electric energy usage amount change.
However, the existing electric energy usage amount prediction method still has many defects in actual use, for example, most of the prediction methods adopt mathematical models to model prediction, the modeling process is complex, when some factors influencing the electric energy usage amount change, relevant parameters cannot be adjusted automatically, the adaptability and the real-time performance are poor, and the prediction accuracy is low.
Disclosure of Invention
In order to overcome the above defects in the prior art, an embodiment of the present invention provides a method for predicting electric energy usage of a smart grid, and the problem to be solved by the present invention is: the existing computer usage amount prediction method has poor adaptability and instantaneity and low prediction precision.
In order to achieve the purpose, the invention provides the following technical scheme: a method for predicting electric energy usage of a smart grid comprises the following steps:
s1: establishing a power grid electric energy use amount prediction index system;
s2: acquiring data, namely acquiring historical direct power consumption data corresponding to a known power consumption acquisition cycle, and acquiring corresponding regional power consumption data;
s3: acquiring the annual growth rate P of GDP corresponding to the acquisition period region, acquiring the annual average growth rate E of the total power consumption in the corresponding acquisition period region, calculating the elasticity coefficient E of power consumption, and storing the acquired data and the calculated data in a database;
s4: establishing a corresponding working condition relation table, establishing an electric energy consumption trend prediction index probability density distribution model according to historical electric energy quality index monitoring data of the smart grid, and then establishing a corresponding working condition relation table of the electric energy consumption prediction index probability density distribution model and numerical meteorological working condition data based on an electric energy elasticity coefficient method;
s5: the working condition matching analysis is carried out, and according to the market change condition and the development expectation in the acquisition period region and the period to be predicted, the corresponding calculation data are selected;
s6: and the calculated electric energy is used for predicting and storing the electric energy into a database.
In a preferred embodiment, the method of calculating the power consumption elastic coefficient E in step S3 is E ═ E/P.
In a preferred embodiment, the prediction model for the elastic coefficient of electrical energy method of step S4 is: a ═ a0(1+E)tWherein An is the annual consumption in the prediction period, and t is the time difference from the year of the prediction period to the year of the prediction period.
In a preferred embodiment, the time difference from the forecast period year to the forecast year in the grid electric energy usage forecast indicator system is in the range of 1-5 years.
By adopting the technical scheme, the smaller the time difference is, the closer the time difference is to the prediction period, so that the error between each coefficient index and the prediction result of the predicted annual power consumption can be reduced, and the accuracy of the prediction result is improved.
The invention has the technical effects and advantages that:
the prediction method comprehensively considers the results of the influences of time difference and regional change on the power consumption of the power grid, fully combines the change situation of the power market, better fits the real situation of the power market, avoids high deviation caused by a mathematical model method only from the prediction of a digital change rule, and improves the prediction precision of the power consumption in the whole society.
Drawings
Fig. 1 is a schematic diagram illustrating a flow of predicting the amount of power used according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The method for predicting the electric energy consumption of the smart grid comprises the following steps:
s1: establishing a power grid electric energy use amount prediction index system;
s2: acquiring data, namely acquiring historical direct power consumption data corresponding to a known power consumption acquisition cycle, and acquiring corresponding regional power consumption data;
s3: acquiring the annual growth rate P of GDP corresponding to the acquisition period region, acquiring the annual average growth rate E of the total power consumption in the corresponding acquisition period region, calculating the elasticity coefficient E of power consumption, and storing the acquired data and the calculated data in a database;
s4: establishing a corresponding working condition relation table, establishing an electric energy consumption trend prediction index probability density distribution model according to historical electric energy quality index monitoring data of the smart grid, and then establishing a corresponding working condition relation table of the electric energy consumption prediction index probability density distribution model and numerical meteorological working condition data based on an electric energy elasticity coefficient method;
s5: the working condition matching analysis is carried out, and according to the market change condition and the development expectation in the acquisition period region and the period to be predicted, the corresponding calculation data are selected;
s6: and the calculated electric energy is used for predicting and storing the electric energy into a database.
As shown in fig. 1, the method of calculating the power consumption elastic coefficient E in step S3 is E ═ E/P.
As shown in fig. 1, the prediction model of the elastic coefficient of electrical energy method in step S4 is: a ═ a0(1+E)tWherein An is the annual consumption in the prediction period, and t is the time difference from the year of the prediction period to the year of the prediction period.
The time difference range from the year of the prediction period to the year of the prediction in the power grid electric energy usage prediction index system is 1-5 years, and the smaller the time difference is, the closer the time difference is to the prediction period, so that the error between each coefficient index and the prediction result of the predicted annual power consumption can be reduced, and the accuracy of the prediction result is improved.
As shown in fig. 1, the electric energy usage of the power grid in the prediction period can be calculated according to An (annual consumption).
As shown in fig. 1, the implementation scenario specifically includes: in actual use, the prediction method comprehensively considers the result of the influence of time difference and regional change on the power consumption of the power grid, fully combines the change condition of the power market, is more fit with the real condition of the power market, avoids high deviation caused by the fact that a mathematical model method only predicts from a digital change rule, and improves the prediction precision of the power consumption in the whole society. Meanwhile, the method is combined with daily work of market forecasters, is easy to master and operate, is simple and easy to understand in consideration of influence factors and variable calculation aspects, facilitates the actual operators to master, provides a beneficial guidance function for improving the work quality of electric power market analysis, and specifically solves the problem of low prediction precision in the prior art.
And finally: the above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that are within the spirit and principle of the present invention are intended to be included in the scope of the present invention.
Claims (4)
1. The method for predicting the electric energy consumption of the smart grid is characterized by comprising the following steps:
s1: establishing a power grid electric energy use amount prediction index system;
s2: acquiring data, namely acquiring historical direct power consumption data corresponding to a known power consumption acquisition cycle, and acquiring corresponding regional power consumption data;
s3: acquiring the annual growth rate P of GDP corresponding to the acquisition period region, acquiring the annual average growth rate E of the total power consumption in the corresponding acquisition period region, calculating the elasticity coefficient E of power consumption, and storing the acquired data and the calculated data in a database;
s4: establishing a corresponding working condition relation table, establishing an electric energy consumption trend prediction index probability density distribution model according to historical electric energy quality index monitoring data of the smart grid, and then establishing a corresponding working condition relation table of the electric energy consumption prediction index probability density distribution model and numerical meteorological working condition data based on an electric energy elasticity coefficient method;
s5: the working condition matching analysis is carried out, and according to the market change condition and the development expectation in the acquisition period region and the period to be predicted, the corresponding calculation data are selected;
s6: and the calculated electric energy is used for predicting and storing the electric energy into a database.
2. The smart grid electric energy usage prediction method according to claim 1, wherein the electric power consumption elasticity coefficient E in step S3 is calculated as E-E/P.
3. The smart grid electric energy usage amount prediction method according to claim 1, wherein the electric energy elastic coefficient method prediction model of step S4 is: a ═ a0(1+E)tWherein An is the annual consumption in the prediction periodAnd t is the time difference from the year of the prediction period to the year of the prediction.
4. The intelligent power grid electric energy usage prediction method according to claim 3, wherein the time difference range from the prediction cycle year to the prediction year in the power grid electric energy usage prediction index system is 1-5 years.
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Citations (5)
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| KR20160074325A (en) * | 2014-12-18 | 2016-06-28 | 주식회사 나우드림 | Electricity Demand Index (EDI) Forecasting System with respect to Weather Condition Change |
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- 2021-09-26 CN CN202111125277.3A patent/CN113837470A/en active Pending
Patent Citations (5)
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|---|---|---|---|---|
| CN103235981A (en) * | 2013-04-10 | 2013-08-07 | 东南大学 | Wind power quality trend predicting method |
| CN103745280A (en) * | 2014-01-26 | 2014-04-23 | 北京中电普华信息技术有限公司 | Prediction method, device and processor for electricity consumption |
| KR20160074325A (en) * | 2014-12-18 | 2016-06-28 | 주식회사 나우드림 | Electricity Demand Index (EDI) Forecasting System with respect to Weather Condition Change |
| CN107239850A (en) * | 2017-04-24 | 2017-10-10 | 华北电力大学 | A kind of long-medium term power load forecasting method based on system dynamics model |
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Non-Patent Citations (1)
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