CN114819280A - Power distribution network load prediction method and system - Google Patents
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Abstract
The invention discloses a power distribution network load prediction method, which belongs to the technical field of power load prediction and comprises the following specific steps: the method comprises the following steps: acquiring historical power data; step two: determining load influence factors and selecting similar days; step three: determining electric vehicle load data; step four: extracting a training set; step five: constructing a regional short-term load prediction model; step six: load prediction; according to the method, other load influence related factors and the influence of electric vehicle load access on the load of the power distribution network are fully considered, and a regional short-term load prediction model is constructed for different load influence related factors in different regions and the electric vehicle load, so that the regional short-term power load prediction precision is favorably improved, and an important reference basis is favorably provided for reasonably controlling the starting and stopping of the generator set.
Description
Technical Field
The invention relates to the technical field of power load prediction, in particular to a power distribution network load prediction method and system.
Background
With the increasing and close relationship between electric energy and the life of residents, industrial and agricultural production and social development, whether safe, high-quality, economic and reliable electric energy can be met or not is more and more important for electricity consumers; the electric energy is difficult to store in large quantity, the use requirements of users can be met only through multiple links such as sending, transmitting, transforming, distributing and the like, and inevitable loss can be formed in each link; therefore, to effectively reduce loss and improve the utilization rate of primary energy, an important means for improving the stability and economy of a power grid is to make an accurate power generation plan and a reasonable scheduling scheme in advance, and accurate load prediction provides a reasonable reference basis for making a plan and putting forward a scheme; at present, according to a prediction time period, the method can be divided into four categories of ultra-short-term load prediction, medium-term load prediction and long-term load prediction; the short-term load prediction plays an important role in starting and stopping of a unit, coordination of water, fire and electricity, exchange power of a connecting line, economic load distribution, reservoir scheduling and equipment maintenance, so that the short-term load prediction becomes a key point of current research; for short-term prediction, the load change rule of a power grid needs to be fully researched, load change related factors are analyzed, but because the influence factors of power loads in different areas are greatly different, and the access of a large number of electric vehicle loads causes great obstruction to the short-term load prediction in the areas, how to improve the short-term power load prediction precision of the areas, reasonably control the start and stop of a generator set and improve the stability and economy of the power grid becomes more and more important; therefore, it becomes more important to invent a method and a system for predicting the load of the power distribution network;
through retrieval, the Chinese patent number CN107491812B discloses a short-term load prediction method considering real-time electricity price, and the method carries out short-term load prediction based on the relation between the electricity price and the load, and has good prediction performance;
through retrieval, the Chinese patent No. CN102930356A discloses a short-term load prediction method based on meteorological factor sensitivity, and the prediction precision of the short-term load of the power is improved by correcting meteorological factors;
from the prior applied patents, the existing short-term load prediction method mostly depends on specific load influence factors (electricity price, time and weather) to predict the short-term load, and although the prediction speed is high, other load influence related factors and the influence generated by the load access of the electric automobile are not considered, so that the prediction precision of the regional short-term power load cannot be improved, and an important reference basis cannot be provided for reasonably controlling the start and stop of a generator set; therefore, a power distribution network load prediction method and system are provided.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a method and a system for predicting the load of a power distribution network.
In order to achieve the purpose, the invention adopts the following technical scheme:
a power distribution network load prediction method comprises the following specific steps:
the method comprises the following steps: acquiring historical power data and acquiring historical power data of an area to be predicted; the historical power data comprises historical load impact data and historical load data;
step two: determining load influence factors and selecting similar days, determining main influence factors of the load of the area to be predicted according to the change condition of the historical load data, and extracting the similar historical days of the prediction days of the area to be predicted through a similarity measurement model according to the main influence factors;
step three: determining electric vehicle load data, acquiring electric vehicle related data, and determining electric vehicle daily load data of an area to be predicted according to the electric vehicle related data;
step four: extracting a training set, namely extracting the load influence data and the load data of the similar historical days and the daily load data of the electric vehicle as the training set, the test set and the verification set;
step five: constructing a regional short-term load prediction model, constructing a deep network model, training the training set as input data of the deep network model to form a regional short-term load prediction model, verifying through a verification test set, outputting the model if the regional short-term load prediction model error is within a threshold value, otherwise, skipping to the fourth step to re-extract the training set;
step six: and load prediction, namely acquiring and inputting relevant data of a prediction day of a region to be predicted, predicting based on the region short-term load prediction model to obtain a region short-term load prediction result, and outputting the region short-term load prediction result.
Further, the historical load influence data in the first step comprises specific influence factor data and random influence factor data; the specific influence factor data includes electricity price, time and weather; the random influence factor data is specifically determined according to the region to which the random influence factor data belongs.
Further, the specific process of determining the load influence factors in the step two is as follows:
s1: selecting a certain historical day of a certain load influence factor one by one, and extracting historical load data of the certain historical day;
s2: acquiring a history day which is the same as or similar to other influence factors of the history day and different from the certain load influence factor as a comparison day;
s3: and performing an addition operation on the historical load data of the comparison day and the historical load data of the certain historical day to obtain an operation result, if the operation result is 0.5-1.5, determining that the certain load influence factor is a main influence factor of the load of the area to be predicted, and otherwise, rejecting the certain load influence factor.
Further, the similar day selection in the second step is determined according to the main influence factors of the load of the area to be predicted, and the specific formula is as follows through calculation of a similarity measurement model:
in the formula: cos θ is the similarity, interval [0,1 ]; a is a load influence characteristic vector of a prediction day of an area to be predicted; b is a load influence characteristic vector of a historical day;
the similar day is selected from historical days with cos theta values above 0.8.
Further, the data related to the electric vehicle in the third step includes a daily electric vehicle flow and a daily electric vehicle charging number.
A power distribution network load prediction system comprises a data extraction module, a data preprocessing module, a deep learning module, a data acquisition module, a load prediction module and an output display module;
the data extraction module is used for acquiring historical electric power data and electric automobile related data in an electric power database and an electric automobile database, and extracting load influence data and load data of similar historical days and daily load data of the electric automobile based on the historical electric power data and the electric automobile related data;
the data preprocessing module is used for preprocessing the load influence data and the load data of the similar historical days and the daily load data of the electric automobile;
the deep learning module is used for taking the preprocessed load influence data and load data of similar historical days and daily load data of the electric vehicle as a training set, learning based on a deep learning network and generating a regional short-term load prediction model;
the data acquisition module is used for acquiring relevant data of a prediction day of an area to be predicted;
the load prediction module is used for inputting relevant data of the prediction day of the area to be predicted, predicting the data based on the area short-term load prediction model and generating an area short-term load prediction result;
and the output display module is used for outputting and displaying the regional short-term load prediction result so as to provide an important reference basis for reasonably controlling the start and stop of the generator set.
Compared with the prior art, the invention has the beneficial effects that:
the application discloses a power distribution network load prediction method and a power distribution network load prediction system, wherein corresponding load influence factors are determined according to the load influence degrees of different load influence factors in different regions, load influence data and load data of a similar historical day similar to a prediction day are selected through a similarity measurement model, meanwhile, daily load data of an electric vehicle are determined through the daily electric vehicle driving flow and the daily electric vehicle driving charging times, and finally, training and prediction are performed through a depth network model; according to the method and the device, other load influence relevant factors and the influence of electric vehicle load access on the load of the power distribution network are fully considered, so that the regional short-term power load prediction precision is favorably improved, and an important reference basis is favorably provided for reasonably controlling the starting and stopping of the generator set.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
Fig. 1 is an overall flowchart of a power distribution network load prediction method according to the present invention;
fig. 2 is an overall schematic diagram of a power distribution network load prediction system 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.
In the description of the present invention, it is to be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention.
Referring to fig. 1, the present embodiment discloses a power distribution network load prediction method, which includes the following specific steps:
the method comprises the following steps: acquiring historical power data and acquiring historical power data of an area to be predicted; the historical power data comprises historical load impact data and historical load data;
specifically, the historical load influence data includes specific influence factor data and random influence factor data; specific influencing factor data includes electricity price, time and weather; the random influence factor data is specifically determined according to the region to which the random influence factor data belongs, and the random influence factors include but are limited to policy factors, epidemic situation prevention and control factors, heating time and the like and are mainly determined according to the condition of a specific region.
Step two: determining load influence factors and selecting similar days, determining main influence factors of the load of the area to be predicted according to the change condition of historical load data, and extracting the similar historical days of the prediction days of the area to be predicted through a similarity measurement model according to the main influence factors;
specifically, the specific process of determining the load influence factor is as follows: selecting a certain historical day of a certain load influence factor one by one, and extracting historical load data of the certain historical day; acquiring a history day which is the same as or similar to other influence factors of a certain history day and different from the certain load influence factor as a comparison day; and performing an addition operation on the historical load data of the comparison day and the historical load data of a certain historical day to obtain an operation result, if the operation result is 0.5-1.5, determining that a certain load influence factor is a main influence factor of the load of the area to be predicted, and otherwise, rejecting the certain load influence factor.
Specifically, the similar day is determined according to main influence factors of the load of the area to be predicted, and the similar day is calculated through a similarity measurement model, wherein a specific formula of the similar day is as follows:
in the formula: cos θ is the similarity, interval [0,1]](ii) a A is a load influence characteristic vector of a prediction day of an area to be predicted; b is a load influence characteristic vector of a historical day; if the cos θ value is 0.8 or more, the history date is regarded as the similar date.
Step three: determining electric vehicle load data, acquiring electric vehicle related data, and determining electric vehicle daily load data of an area to be predicted according to the electric vehicle related data;
specifically, the electric vehicle related data comprises daily running electric vehicle flow and daily running electric vehicle charging times; the electric automobile load data is calculated according to the daily running electric automobile flow and the daily running electric automobile charging times and based on the electric automobile power, so that the electric automobile load data is obtained.
Step four: extracting a training set, namely extracting load influence data and load data of similar historical days and daily load data of the electric vehicle as the training set, the test set and the verification set;
step five: constructing a regional short-term load prediction model, constructing a deep network model, training the training set as input data of the deep network model to form the regional short-term load prediction model, verifying through a verification test set, outputting the model if the regional short-term load prediction model error is within a threshold value, otherwise, jumping to the fourth step to re-extract the training set; specifically, the threshold is within ± 5%;
step six: and load prediction, namely acquiring and inputting relevant data of a prediction day of the region to be predicted, predicting based on a region short-term load prediction model to obtain a region short-term load prediction result, and outputting the region short-term load prediction result.
Referring to fig. 2, the embodiment discloses a power distribution network load prediction system, which includes a data extraction module, a data preprocessing module, a deep learning module, a data acquisition module, a load prediction module, and an output display module;
the data extraction module is used for acquiring historical electric power data and electric automobile related data in the electric power database and the electric automobile database, and extracting load influence data and load data of similar historical days and daily load data of the electric automobile based on the historical electric power data and the electric automobile related data;
the data preprocessing module is used for preprocessing the load influence data and the load data of the similar historical days and the daily load data of the electric automobile;
specifically, the preprocessing includes, but is not limited to, filling missing data, correcting noise data, smoothing data, normalizing data, and unifying data formats;
the deep learning module is used for taking the preprocessed load influence data and load data of similar historical days and daily load data of the electric vehicle as a training set, learning on the basis of a deep learning network and generating a regional short-term load prediction model;
the data acquisition module is used for acquiring relevant data of a prediction day of an area to be predicted;
specifically, the related data includes each load influence data of the prediction day of the area to be predicted;
the load prediction module is used for inputting relevant data of a prediction day of a region to be predicted, predicting the data based on a region short-term load prediction model and generating a region short-term load prediction result;
and the output display module is used for outputting and displaying the short-term load prediction result of the area so as to provide an important reference basis for reasonably controlling the start and stop of the generator set.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (6)
1. A power distribution network load prediction method is characterized by comprising the following specific steps:
the method comprises the following steps: acquiring historical power data and acquiring historical power data of an area to be predicted; the historical power data comprises historical load impact data and historical load data;
step two: determining load influence factors and selecting similar days, determining main influence factors of the load of the area to be predicted according to the change condition of the historical load data, and extracting the similar historical days of the prediction days of the area to be predicted through a similarity measurement model according to the main influence factors;
step three: determining electric vehicle load data, acquiring electric vehicle related data, and determining electric vehicle daily load data of an area to be predicted according to the electric vehicle related data;
step four: extracting a training set, namely extracting the load influence data and the load data of the similar historical days and the daily load data of the electric vehicle as the training set, the test set and the verification set;
step five: constructing a regional short-term load prediction model, constructing a deep network model, training the training set as input data of the deep network model to form a regional short-term load prediction model, verifying through a verification test set, outputting the model if the regional short-term load prediction model error is within a threshold value, otherwise, skipping to the fourth step to re-extract the training set;
step six: and load prediction, namely acquiring and inputting relevant data of a prediction day of a region to be predicted, predicting based on the region short-term load prediction model to obtain a region short-term load prediction result, and outputting the region short-term load prediction result.
2. The method according to claim 1, wherein the historical load influence data comprises specific influence factor data and random influence factor data; the specific influence factor data includes electricity price, time and weather; the random influence factor data is specifically determined according to the region to which the random influence factor data belongs.
3. The method for predicting the load of the power distribution network according to claim 1, wherein the specific process for determining the load influence factors in the second step is as follows:
s1: selecting a certain historical day of a certain load influence factor one by one, and extracting historical load data of the certain historical day;
s2: acquiring a history day which is the same as or similar to other influence factors of the history day and different from the certain load influence factor as a comparison day;
s3: and performing an addition operation on the historical load data of the comparison day and the historical load data of the certain historical day to obtain an operation result, if the operation result is 0.5-1.5, determining that the certain load influence factor is a main influence factor of the load of the area to be predicted, and otherwise, rejecting the certain load influence factor.
4. The method for predicting the load of the power distribution network according to claim 1, wherein the similar day in the second step is determined according to main influence factors of the load of the area to be predicted, and the main influence factors are calculated through a similarity measurement model, and the specific formula is as follows:
in the formula: cos θ is the similarity, interval [0,1 ]; a is a load influence characteristic vector of a prediction day of an area to be predicted; b is a load influence characteristic vector of a historical day;
the similar day is selected from historical days with cos theta values above 0.8.
5. The method according to claim 1, wherein the electric vehicle related data in step three includes a daily electric vehicle flow and a daily electric vehicle charging number.
6. A power distribution network load prediction system is characterized by comprising a data extraction module, a data preprocessing module, a deep learning module, a data acquisition module, a load prediction module and an output display module;
the data extraction module is used for acquiring historical electric power data and electric automobile related data in an electric power database and an electric automobile database, and extracting load influence data and load data of similar historical days and daily load data of the electric automobile based on the historical electric power data and the electric automobile related data;
the data preprocessing module is used for preprocessing the load influence data and the load data of the similar historical days and the daily load data of the electric automobile;
the deep learning module is used for taking the preprocessed load influence data and load data of similar historical days and daily load data of the electric vehicle as a training set, learning based on a deep learning network and generating a regional short-term load prediction model;
the data acquisition module is used for acquiring relevant data of a prediction day of an area to be predicted;
the load prediction module is used for inputting relevant data of the prediction day of the area to be predicted, predicting the data based on the area short-term load prediction model and generating an area short-term load prediction result;
and the output display module is used for outputting and displaying the regional short-term load prediction result so as to provide an important reference basis for reasonably controlling the start and stop of the generator set.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116505663A (en) * | 2023-06-28 | 2023-07-28 | 佛山市华易科技有限公司 | Farm power consumption safety state monitoring and early warning system |
| CN118232331A (en) * | 2024-03-27 | 2024-06-21 | 华北电力大学 | Wave energy generation power prediction method, device, equipment and medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116505663A (en) * | 2023-06-28 | 2023-07-28 | 佛山市华易科技有限公司 | Farm power consumption safety state monitoring and early warning system |
| CN116505663B (en) * | 2023-06-28 | 2023-09-05 | 佛山市华易科技有限公司 | Farm power consumption safety state monitoring and early warning system |
| CN118232331A (en) * | 2024-03-27 | 2024-06-21 | 华北电力大学 | Wave energy generation power prediction method, device, equipment and medium |
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