CN111178618A - Intelligent power grid load prediction method - Google Patents

Intelligent power grid load prediction method Download PDF

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CN111178618A
CN111178618A CN201911352796.6A CN201911352796A CN111178618A CN 111178618 A CN111178618 A CN 111178618A CN 201911352796 A CN201911352796 A CN 201911352796A CN 111178618 A CN111178618 A CN 111178618A
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newton interpolation
load
adopting
park
data
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于波
吴明雷
王嘉庚
唐志津
卢欣
李民
朱伯苓
石枫
张超
王海巍
张智达
孟昭斌
杨延春
韩慎朝
吴亮
刘裕德
陈彬
曹晓男
隋淑慧
张凡
郭晓丹
孙学文
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State Grid Tianjin Energy Saving Service Co ltd
State Grid Tianjin Integration Energy Service Co ltd
State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
Electric Power Research Institute of State Grid Tianjin Electric Power Co Ltd
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State Grid Tianjin Energy Saving Service Co ltd
State Grid Tianjin Integration Energy Service Co ltd
State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
Electric Power Research Institute of State Grid Tianjin Electric Power Co Ltd
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Abstract

The invention relates to a load prediction method of an intelligent power grid, which is technically characterized by comprising the following steps: collecting 12-month electricity load data of a park in one year, and observing the trend condition of the park; selecting proper training data for training and predicting a Newton interpolation model; predicting the power load data of the missing month by adopting a Newton interpolation method; correcting a Newton interpolation result by adopting a post error estimation method; and calculating the relative error between the final Newton interpolation result and the true value, thereby completing the load prediction of the intelligent power grid. The method is reasonable in design, can accurately predict the short-term electric load condition of the park, is simple and easy to realize, does not need to fit a specific function form, can predict and obtain a better prediction effect by only a few data points, and can provide a reference basis for power grid scheduling formulation and power generation and supply plans.

Description

Intelligent power grid load prediction method
Technical Field
The invention belongs to the technical field of smart power grids, and particularly relates to a load prediction method for a smart power grid.
Background
The forecasting of the electric load of the park is an important link in the planning of an electric power system, and the problem of balance of supply and demand of electric energy caused by the fact that the electric energy cannot be stored in a large scale is solved, so that the quality of power supply is guaranteed.
With the development of an electric power system EMS, short-term load prediction becomes one of necessary links of the EMS nowadays, support is provided for guaranteeing safe and economic operation of the electric power system, and the method is mainly used for optimizing unit start-stop, hydropower plan, water, fire and electricity coordination and power exchange plan. By accurately predicting the short-term power load, a reference basis can be provided for the power grid scheduling and making a power generation and supply plan, the balance of supply and demand of electric energy in the power grid is guaranteed, and data support can be provided for the production, transmission, distribution and sale of estimated electric energy, so that a power system can make a more economic and reasonable power generation and utilization scheme, and the aims of energy management and control, energy conservation and emission reduction are achieved.
At present, regression analysis is mostly adopted to establish a mathematical model for forecasting the power load of a park, so that the fitting precision is low due to irregular data points, and the accurate management and control requirements of the intelligent power grid are difficult to meet.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method for predicting the load of an intelligent power grid, which has the advantages of reasonable design, accurate prediction result and high efficiency.
The technical problem to be solved by the invention is realized by adopting the following technical scheme:
a smart grid load prediction method comprises the following steps:
step 1, collecting power load data of a park in 12 months in one year, and observing the trend condition of the park;
2, selecting proper training data for training and predicting a Newton interpolation model;
step 3, predicting the power load data of the missing month by adopting a Newton interpolation method;
step 4, correcting a Newton interpolation result by adopting a post error estimation method;
and 5, calculating the relative error between the final Newton interpolation result and the true value, thereby completing the load prediction of the intelligent power grid.
The newton interpolation method is calculated according to the following formula:
f(x)＝f(x0)+(x-x0)f(x,x0)+…+(x-x0)…(x-xn-1)f(x,x0,…,xn-1)+(x-x0)…(x-xn-1)(x-xn)f(x,x0,…,xn)
in the formula, f (x) is the electricity consumption data of the missing month; f (x, x)0) Is a first order difference quotient, f (x, x)0,…,xn) Is the N-order difference quotient.
The quotient of each step of the Newton interpolation method is calculated by adopting the following formula:
f(x)＝f(x0)+(x-x0)f(x,x0)
f(x,x0)＝f(x,x1)+(x-x1)f(x,x0,x1)
f(x,x0,x1)＝f(x0,x1,x2)+(x-x2)f(x,x0,x1,x2)
…
f(x,x0,…,xn-1)＝f(x0,x1,…,xn)+(x-xn)f(x,x0,…,xn)
step 4, correcting the post error by adopting the following formula:
Figure BDA0002335066690000021
in the formula, y represents a modified Newton interpolation result; f (x)1) Representing the result of the first Newton interpolation; f (x)2) Representing the second Newton interpolation result; x is the number of1,x2,x3Here representing different months.
The step 5 adopts the following formula to calculate the relative error:
σ＝(y-yture)/yture*100％
in the formula, y represents a modified Newton interpolation result; y istureRepresenting the true value.
The invention has the advantages and positive effects that:
1. according to the method, on the basis of the collected historical load data, the short-term load trend is predicted by modeling analysis of the load by adopting a Newton interpolation method, a specific function form does not need to be fitted, the problem that the fitting precision of a regression analysis method is low due to irregular data points is solved, and the prediction result is more accurate.
2. The method adopts a Newton interpolation method, does not need to predict the electric load data for a long time, can simply calculate by only needing a plurality of data points, and greatly simplifies the requirement of data quantity.
3. The method can predict the power load data at any time in a certain time period, and the power load has periodicity, so that the prediction result of the method is also effective in the next period, and a reference basis is provided for the dispatching and making of a power generation and supply plan of a power grid in a park.
4. The intelligent park energy efficiency management and control system is reasonable in design, plays an important role in realizing resource regulation and control, guaranteeing balance of power supply and demand and guaranteeing power quality through short-term load prediction, can provide reference basis for energy efficiency management and control of the intelligent park, effectively realizes optimal allocation of resources and load balancing, and improves management energy efficiency.
Drawings
FIG. 1 is a prediction flow diagram of the present invention;
figure 2 is a plot of the electrical load trend for a campus.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
A method for predicting the load of a smart grid is disclosed, as shown in FIG. 1, and comprises the following steps:
step 1, collecting the electricity load data of the park in 12 months in one year, and observing the trend condition of the park.
In this step, as can be seen from fig. 2, the change of the power consumption with time has no obvious trend, so that the prediction of the power load cannot be performed by a simple regression analysis method, and the newton interpolation rule is a good choice.
The electricity consumption data of the 12 months in the park are shown in table 1.
TABLE 1 campus electricity load data for 24 hours (kWh)
Figure BDA0002335066690000022
Figure BDA0002335066690000031
And 2, selecting proper training data for training and predicting the Newton interpolation model.
In this step, for error estimation and correction, three sets of data are selected for model calculation, corresponding to 1,3,4 and 1,3,5 in months, respectively. The data for months 1,3,4 and 1,3,5 are used to predict the power load for month 2.
And 3, predicting the missing monthly electric load data by adopting a Newton interpolation method.
In this step, the newton interpolation model is calculated according to the following formula:
f(x)＝f(x0)+(x-x0)f(x,x0)+…+(x-x0)…(x-xn-1)f(x,x0,…,xn-1)+(x-x0)…(x-xn-1)(x-xn)f(x,x0,…,xn)
in the formula, f (x) is the electricity consumption data of the missing month; f (x, x)0) Is a first order difference quotient, like f (x, x)0,…,xn) Is the N-order difference quotient; the difference quotient f (x, x)0,…,xn) The calculation formula is as follows:
the difference quotient is calculated as follows:
f(x)＝f(x0)+(x-x0)f(x,x0)
f(x,x0)＝f(x,x1)+(x-x1)f(x,x0,x1)
f(x,x0,x1)＝f(x0,x1,x2)+(x-x2)f(x,x0,x1,x2)
…
f(x,x0,…,xn-1)＝f(x0,x1,…,xn)+(x-xn)f(x,x0,…,xn)
and calculating to obtain the difference quotient of each order shown in the tables 2 and 3 according to the calculation formula of the difference quotient and by combining the selected data.
TABLE 2.1,3,4 months power consumption difference quotient calculation results
Figure BDA0002335066690000032
TABLE 3.1,3,5 months power consumption difference quotient calculation results
Figure BDA0002335066690000033
If the result of the first Newton interpolation is recorded as f (2)1Then, according to table 2:
f(2)1＝9000+(2-1)*(-500)+(2-1)*(2-3)*(-333.3)＝8833.3
if the result of the second Newton interpolation is recorded as f (2)2Similarly, from table 3, we can obtain:
f(2)2＝9000+(2-1)*(-500)+(2-1)*(2-3)*0＝8500
and 4, step 4: and correcting the Newton interpolation result by adopting a post error estimation method, wherein the result corrected by the post error estimation method is as follows:
Figure BDA0002335066690000041
Figure BDA0002335066690000042
in the formula, y represents a modified Newton interpolation result; f (x)1) Representing the result of the first Newton interpolation; f (x)2) Representing the second Newton interpolation result; x is the number of1,x2,x3Here representing different months.
And 5: the relative error of the final newton interpolation result from the true value is calculated as follows:
σ＝(y-yture)/yture*100％＝(9499.9-10000)/10000*100％＝-5.001％
in the formula, y represents a modified Newton interpolation result; y istureRepresenting the true value.
Through the steps, the electricity utilization load of the designated month can be predicted, and the relative error is low.
It should be emphasized that the embodiments described herein are illustrative rather than restrictive, and thus the present invention is not limited to the embodiments described in the detailed description, but other embodiments derived from the technical solutions of the present invention by those skilled in the art are also within the scope of the present invention.

Claims (5)

1. A method for predicting the load of a smart grid is characterized by comprising the following steps:
step 1, collecting power load data of a park in 12 months in one year, and observing the trend condition of the park;
2, selecting proper training data for training and predicting a Newton interpolation model;
step 3, predicting the power load data of the missing month by adopting a Newton interpolation method;
step 4, correcting a Newton interpolation result by adopting a post error estimation method;
and 5, calculating the relative error between the final Newton interpolation result and the true value, thereby completing the load prediction of the intelligent power grid.
2. The smart grid load prediction method according to claim 1, wherein: the newton interpolation method is calculated according to the following formula:
f(x)＝f(x0)+(x-x0)f(x,x0)+…+(x-x0)…(x-xn-1)f(x,x0,…,xn-1)+(x-x0)…(x-xn-1)(x-xn)f(x,x0,…,xn)
in the formula, f (x) is the electricity consumption data of the missing month; f (x, x)0) Is a first order difference quotient, f (x, x)0,…,xn) Is the N-order difference quotient.
3. The smart grid load prediction method according to claim 2, wherein: the quotient of each step of the Newton interpolation method is calculated by adopting the following formula:
f(x)＝f(x0)+(x-x0)f(x,x0)
f(x,x0)＝f(x,x1)+(x-x1)f(x,x0,x1)
f(x,x0,x1)＝f(x0,x1,x2)+(x-x2)f(x,x0,x1,x2)
…
f(x,x0,…,xn-1)＝f(x0,x1,…,xn)+(x-xn)f(x,x0,…,xn) 。
4. the smart grid load prediction method according to claim 1, wherein: step 4, correcting the post error by adopting the following formula:
Figure FDA0002335066680000011
in the formula, y represents a modified Newton interpolation result; f (x)1) Representing the result of the first Newton interpolation; f (x)2) Representing the second Newton interpolation result; x is the number of1,x2,x3Here representing different months.
5. The smart grid load prediction method according to claim 1, wherein: the step 5 adopts the following formula to calculate the relative error:
σ＝(y-yture)/yture*100％
in the formula, y represents a modified Newton interpolation result; y istureRepresenting the true value.
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CN112668765A (en) * 2020-12-23 2021-04-16 韶关东阳光科技研发有限公司 Method and device for load prediction and energy management of power consumers of aluminum processing enterprises
CN113864372A (en) * 2021-11-01 2021-12-31 中国第一汽车股份有限公司 Transmission predictive thermal protection method and automobile

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CN103236025A (en) * 2013-04-25 2013-08-07 国家电网公司 Data consolidation processing method based on power user data
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112668765A (en) * 2020-12-23 2021-04-16 韶关东阳光科技研发有限公司 Method and device for load prediction and energy management of power consumers of aluminum processing enterprises
CN113864372A (en) * 2021-11-01 2021-12-31 中国第一汽车股份有限公司 Transmission predictive thermal protection method and automobile

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