CN111008727A - Power distribution station load prediction method and device - Google Patents

Power distribution station load prediction method and device Download PDF

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CN111008727A
CN111008727A CN201911031980.0A CN201911031980A CN111008727A CN 111008727 A CN111008727 A CN 111008727A CN 201911031980 A CN201911031980 A CN 201911031980A CN 111008727 A CN111008727 A CN 111008727A
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庞杰锋
魏勇
李俊刚
史宏光
孟乐
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Xuji Group Co Ltd
XJ Electric Co Ltd
Xuchang XJ Software Technology Co Ltd
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Abstract

The invention relates to a power distribution station load prediction method and device, and belongs to the technical field of power systems. The method comprises the steps of firstly obtaining historical load data and relevant factor data of a set time period of a power distribution station area, then establishing a prediction model comprising the historical load data, the relevant factor data, a date type weight, a temperature factor influence weight and a power failure duration influence weight, and finally predicting a future load value according to the model. The invention not only considers the influence of temperature and date types, but also considers the influence of power-off duration, the established prediction model can more accurately describe the relation between the load and each factor, the prediction precision is improved, and the scheme does not need complex data processing and is simple and easy to realize.

Description

Power distribution station load prediction method and device
Technical Field
The invention relates to a power distribution station load prediction method and device, and belongs to the technical field of power systems.
Background
The power load prediction is a series of prediction work performed by taking a power load as an object, and is a scientific and reasonable conjecture on the future power load level, the occurrence time, the location and the like according to the development of past and present loads and the development and planning of past, present and future social economy. The current load curve prediction is mainly based on historical load data and environmental data, such as patent application publication No. CN110163429A, the patent application discloses a short-term load prediction method based on similar day optimization screening, the method comprises the steps of fitting corresponding load values of temperature values at different moments based on a minimum absolute shrinkage operator, dividing all 24-hour moments into key moments and non-key moments based on the fitting degree requirement of temperature on load, giving different weights of the key moments and the non-key moments, calculating a weighted Euclidean distance between historical temperature data and predicted day temperature data, constructing a similar day set, constructing a differential autoregressive moving average model based on load data and temperature factor data in the similar day set, and performing load prediction by combining the temperature data of the day of prediction and the load data of the previous two days. Although the scheme can realize load prediction, the implementation process of the scheme is complex, only historical load and temperature are considered, the consideration factors are incomplete, and the prediction result is inaccurate.
Disclosure of Invention
The invention aims to provide a power distribution station load prediction method and a power distribution station load prediction device, and aims to solve the problems of complexity and low accuracy of prediction results in the current load prediction process.
The invention provides a power distribution station load prediction method for solving the technical problem, which comprises the following steps:
1) acquiring historical load data and related factor data of a set time period of a power distribution station area, wherein the historical load data comprises absolute time of a historical load data measuring point, a measuring point date type and an actual load value, and the related factor data comprises an actual temperature value and power-off duration;
2) establishing a prediction model according to the acquired load historical data, wherein the prediction model comprises historical load data, related factor data, a date type weight, a temperature factor influence weight and a power-off duration influence weight;
3) and predicting the load data after the set time period according to the established prediction model prediction to obtain the predicted load value of each measuring point consistent with the historical load data measuring point.
The invention also provides a power distribution station load prediction device, which comprises a memory, a processor and a computer program stored on the memory and running on the processor, wherein the processor is coupled with the memory, and the processor executes the computer program to realize the power distribution station load prediction method.
The method comprises the steps of firstly obtaining historical load data and relevant factor data of a set time period of a power distribution station area, then establishing a prediction model comprising the historical load data, the relevant factor data, a date type weight, a temperature factor influence weight and a power failure duration influence weight, and finally predicting a future load value according to the model. The invention not only considers the influence of temperature and date types, but also considers the influence of power-off duration, the established prediction model can more accurately describe the relation between the load and each factor, the prediction precision is improved, and the scheme does not need complex data processing and is simple and easy to realize.
Further, in order to improve the accuracy of the model, the prediction model is:
Figure BDA0002250418450000021
wherein Y (t) is a predicted load value,
Figure BDA0002250418450000022
to mean value of load values of the same measuring point in a set period of time, X1(t) is
Figure BDA0002250418450000023
Square root of difference, X, from the average load value of working days in a set period of time2(t) is
Figure BDA0002250418450000024
Square root of difference, X, from average load value on holidays in a set time period3(t) is
Figure BDA0002250418450000025
Square root value of difference from average load value in which temperature change does not exceed set threshold value in set time period, X4(t) is
Figure BDA0002250418450000026
Square root of difference from average load value in which no power-off occurs in set time period, a1Is a weight of a workday factor, a2Is a holiday factor weight, a3Is a weight of temperature factor, a4Is the power-off duration factor weight.
Further, in order to improve the accuracy of the model, the prediction model is:
Figure BDA0002250418450000031
wherein Y (t) is a predicted load value;
Figure BDA0002250418450000032
to set the mean value of the load values of the same measurement point in a time period, when the predicted day is the working day, X1(t) is
Figure BDA0002250418450000033
The square root value of the difference value with the average load value of the working days in the set time period; when the predicted day is a non-working day, X1(t) is
Figure BDA0002250418450000034
The square root value of the difference value with the average load value of the non-working days in the set time period; when the predicted day is a holiday, X2(t) is
Figure BDA0002250418450000035
The square root value of the difference value with the average load value of the holidays in the set time period; when the predicted day is a non-holiday, X2(t) is
Figure BDA0002250418450000036
A square root value of a difference value with an average load value of non-holidays in a set time period; x3(t) is
Figure BDA0002250418450000037
A square root value of a difference from an average load value in which a temperature change does not exceed a set threshold value in a set period of time; x4(t) is
Figure BDA0002250418450000038
A square root value of a difference value with an average load value in which power failure does not occur in a set time period; a is3Is the weight of the temperature factor; a is4Is the power-off duration factor weight.
Furthermore, the invention also provides a specific temperature factor weight determination mode, wherein the temperature factor weight is determined according to the temperature change in the set time period, when the temperature change in the set time period is not more than the set threshold, the temperature factor weight is 0.02, otherwise, the temperature factor weight is 0.025.
Furthermore, the invention also provides a specific power failure duration weight determination mode, wherein the power failure duration factor weight is determined according to whether power failure occurs in the set time period, when power failure occurs in the set time period, the power failure duration factor weight is 0.01, and if power failure does not occur in the set time period, the power failure duration factor weight is 0.02.
Further, the set time period is a week time.
Further, the measurement point date types include working days, non-working days, holidays and non-holidays.
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Fig. 1 is a schematic diagram of an implementation of the power distribution station load prediction method of the present invention.
Detailed Description
The following further describes embodiments of the present invention with reference to the drawings.
Embodiments of the prediction method
The prediction method comprises the steps of firstly, obtaining historical load data and relevant factor data of a set time period of a power distribution station area, wherein the historical load data comprises absolute time of a historical load data measuring point, a measuring point date type and an actual load value, and the relevant factor data comprises an actual temperature value and power-off duration; then establishing a prediction model according to the acquired load historical data, wherein the prediction model comprises historical load data, related factor data, a date type weight, a temperature factor influence weight and a power-off duration influence weight; and finally, predicting the load data after the set time period according to the established prediction model prediction to obtain the predicted load value of each measuring point consistent with the historical load data measuring point. The implementation principle of the method is shown in fig. 1, and the specific implementation process of the method of the present invention is described in detail below by taking data of a past week as an example.
1. Historical load data and relevant factor data of a set time period of a power distribution station area are obtained.
In the embodiment, historical load data and relevant factor data of the past 7 days a week are acquired, the historical load data comprises a plurality of measurement point data, in the embodiment, one measurement point is arranged every 15 minutes, 96 measurement points are arranged in total in one day, 672 measurement point data are arranged in total in seven days a week, and the setting of the measurement points can be flexibly changed according to actual requirements. The historical load data for each measurement point includes the serial number of the measurement point, the absolute time of the measurement point, the date type of the measurement point, and the actual load value of the measurement point, as shown in table 1.
TABLE 1
Figure BDA0002250418450000041
The relevant factor data includes actual temperature data and power-off duration data, wherein actual temperature values for each day of the past week and power-off durations for each day of the past week, maximum temperatures for each day, minimum temperatures for each day, and average temperatures, as shown in table 2. The power outage duration data includes the power outage duration of each day of the past week, as shown in table 3.
TABLE 2
Figure BDA0002250418450000051
TABLE 3
Figure BDA0002250418450000052
2. And establishing a prediction model.
Establishing a prediction model according to the acquired historical load data and the acquired related factor data, wherein the prediction model established in the embodiment is as follows:
Figure BDA0002250418450000053
wherein Y (t) is a predicted load value,
Figure BDA0002250418450000054
to mean value of load values at the same measurement point (same absolute time) in a set period of time, X1(t) is
Figure BDA0002250418450000055
Square root of difference, X, from the average load value of working days in a set period of time2(t) is
Figure BDA0002250418450000056
Square root of difference, X, from average load value on holidays in a set time period3(t) is
Figure BDA0002250418450000057
Square root value of difference from average load value in which temperature change does not exceed set threshold value in set time period, X4(t) is
Figure BDA0002250418450000058
Square root of difference from average load value in which no power-off occurs in set time period, a1The weight of the working day factor is 0.7 x 0.97 to 0.679 according to the probability of working day and non-working day, a2The default is 0.3 x 0.97 to 0.291, a, according to the holiday and non-holiday probabilities3The weight value is a temperature factor, and according to the average temperature change of the previous week, if the temperature change basically keeps unchanged, the default value is 0.02, and if the temperature continuously rises or continuously falls, the default value is 0.025; a is4A power-off duration factor weight value is obtained, according to the power-off event of the special event of the previous week, if the power-off event is not powered off in the previous week, a default correction coefficient value is 0.01, 0.01 is a small-probability event fluctuation probability and is used as a correction coefficient, and a normal condition a is realized1＝0.679，a2＝0.291，a3＝0.02，a4The coefficient may exceed 1 when the coefficient fluctuates, but the coefficients are small corrections and have no influence; if the power failure event occurs in the last week, the correlation of the default correction coefficient is increased, and the value is 0.02.
In the above model a1And a2A is a set value according to the probability of working days and holidays, and the type of the date of the predicted day is fixed, for example, if the predicted day is a specific day, whether the day is a working day or not and whether the day is a holiday or not are clear, and a is not necessarily used1And a2To describe its corresponding probability, the present invention provides another predictive model for this purpose:
Figure BDA0002250418450000061
compared with the previous model, X1(t) and X2(t) has a variable meaning, X is the day of work when the predicted day is the day of work1(t) is
Figure BDA0002250418450000062
The square root value of the difference value with the average load value of the working days in the set time period; when the predicted day is a non-working day, X1(t) is
Figure BDA0002250418450000063
The square root value of the difference value with the average load value of the non-working days in the set time period; when the predicted day is a holiday, X2(t) is
Figure BDA0002250418450000064
The square root value of the difference value with the average load value of the holidays in the set time period; when the predicted day is a non-holiday, X2(t) is
Figure BDA0002250418450000065
And the square root value of the difference with the average load value of the non-holidays in the set time period.
The two models are used for predicting the target measuring points, the load value of each measuring point on the prediction day or the prediction week can be measured by the established prediction model, and the absolute time of each measuring point on the prediction day is the same as the absolute time of each measuring point on each day in the historical load data.
3. And predicting according to the prediction model.
The load of one day or a plurality of days in the future can be predicted by utilizing the prediction model, and taking the day in the future as an example, the load prediction values of 96 measurement points in the day in the future can be obtained by utilizing the model.
Device embodiment
The power distribution station load prediction device comprises a memory, a processor and a computer program which is stored on the memory and runs on the processor, wherein the processor is coupled with the memory, and the power distribution station load prediction method is realized when the processor executes the computer program, wherein the specific realization process of the method is described in detail in the embodiment of the method, and is not described again here.
According to the method, the influences of the temperature and the date type and the influences of the power-off duration are considered, the relation between the load and each factor can be described more accurately by the established prediction model, the prediction precision is improved, and the scheme does not need complex data processing and is simple and easy to implement. Through the process, the load of the power distribution area is accurately predicted, the operation mode can be economically and reasonably adjusted, the reserve capacity of a higher-level power station is reduced, the maintenance plan is arranged, the operation cost is reduced, and the economic benefit is improved; meanwhile, the load prediction result provides a basis for power planning and a basis for capacity expansion and reconstruction of a power grid.

Claims (8)

1. A power distribution station load prediction method is characterized by comprising the following steps;
1) acquiring historical load data and related factor data of a set time period of a power distribution station area, wherein the historical load data comprises absolute time of a historical load data measuring point, a measuring point date type and an actual load value, and the related factor data comprises an actual temperature value and power-off duration;
2) establishing a prediction model according to the acquired load historical data, wherein the prediction model comprises historical load data, related factor data, a date type weight, a temperature factor influence weight and a power-off duration influence weight;
3) and predicting the load data after the set time period according to the established prediction model prediction to obtain the predicted load value of each measuring point consistent with the historical load data measuring point.
2. A distribution grid load prediction method as claimed in claim 1, wherein the prediction model is:
Figure FDA0002250418440000011
wherein Y (t) is a predicted load value,
Figure FDA0002250418440000012
to mean value of load values of the same measuring point in a set period of time, X1(t) is
Figure FDA0002250418440000013
Square root of difference, X, from the average load value of working days in a set period of time2(t) is
Figure FDA0002250418440000014
Square root of difference, X, from average load value on holidays in a set time period3(t) is
Figure FDA0002250418440000015
Square root value of difference from average load value in which temperature change does not exceed set threshold value in set time period, X4(t) is
Figure FDA0002250418440000016
Square root of difference from average load value in which no power-off occurs in set time period, a1Is a weight of a workday factor, a2Is a holiday factor weight, a3Is a weight of temperature factor, a4Is the power-off duration factor weight.
3. A distribution grid load prediction method as claimed in claim 1, wherein the prediction model is:
Figure FDA0002250418440000017
wherein Y (t) is a predicted load value;
Figure FDA0002250418440000018
the average value of the load values of the same measuring point in a set time period is obtained when the prediction day isAt working day, X1(t) is
Figure FDA0002250418440000019
The square root value of the difference value with the average load value of the working days in the set time period; when the predicted day is a non-working day, X1(t) is
Figure FDA00022504184400000110
The square root value of the difference value with the average load value of the non-working days in the set time period; when the predicted day is a holiday, X2(t) is
Figure FDA00022504184400000111
The square root value of the difference value with the average load value of the holidays in the set time period; when the predicted day is a non-holiday, X2(t) is
Figure FDA0002250418440000021
A square root value of a difference value with an average load value of non-holidays in a set time period; x3(t) is
Figure FDA0002250418440000022
A square root value of a difference from an average load value in which a temperature change does not exceed a set threshold value in a set period of time; x4(t) is
Figure FDA0002250418440000023
A square root value of a difference value with an average load value in which power failure does not occur in a set time period; a is3Is the weight of the temperature factor; a is4Is the power-off duration factor weight.
4. The distribution substation load prediction method according to claim 2 or 3, wherein the temperature factor weight is determined according to the temperature change in a set time period, when the temperature change in the set time period is not greater than a set threshold, the temperature factor weight is 0.02, otherwise, the temperature factor weight is 0.025.
5. The distribution substation load prediction method according to claim 2 or 3, wherein the power outage duration factor weight is determined according to whether power outage occurs within a set time period, when power outage occurs within the set time period, the power outage duration factor weight is 0.01, and if power outage does not occur within the set time period, the power outage duration factor weight is 0.02.
6. The distribution substation load prediction method of claim 1, wherein the set time period is a week time.
7. The distribution grid load prediction method of claim 1, wherein the measurement point date types include weekday, non-weekday, holiday, and non-holiday.
8. A distribution substation load prediction device, characterized in that the load prediction device comprises a memory and a processor, and a computer program stored on the memory and running on the processor, the processor being coupled to the memory, the processor implementing the distribution substation load prediction method according to any of claims 1-7 when executing the computer program.
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