Method and device for determining load of transformer substation

Abstract

The invention discloses a transformer substation load determination method and device, and relates to the technical field of new energy and energy conservation. The method comprises the following steps: acquiring N load data sets and M influence factor data sets of a transformer substation in a first time period; obtaining a correlation degree between each load data set and each influence factor data set; inputting the N load data sets, the M influence factor data sets and the correlation degree between each load data set and each influence factor data set into a target model; and converting the data in the N load data sets, the data in the M influence factor data sets and the correlation degree into feature vectors through a target model, and determining N target load data sets of the transformer substation in the second time period according to the feature vectors. The technical problem of low accuracy of the main transformer total load prediction result caused by the fact that the power load prediction always focuses on the analysis of the total load and does not consider other influence factors in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of new energy and energy-saving technology, and in particular to a method and device for determining a substation load. Background Art

[0002] Power system load forecasting is an important part of ensuring stable power supply and plays a vital role in the safety and economic operation of the entire power system. Accurate load forecasting can provide decision support for the dispatching department, enabling it to make forward-looking power generation plans and optimize the allocation of power resources, thereby effectively reducing operating costs.

[0003] The accuracy of power load forecasting is restricted by multiple factors such as weather, holidays, special events, etc., and the load changes are relatively random. Due to the access of a large number of distributed photovoltaic and wind power, as well as the widespread popularity of electric vehicles, the randomness and volatility of the load have been further increased, increasing the difficulty of accurate load forecasting. Traditional power load forecasting research often focuses on the macro analysis of the overall load, while ignoring the micro-deconstruction of the power load components, especially the lack of consideration of highly random components such as air conditioning load, electric vehicle load, and new energy output, resulting in limited accuracy of the forecast results.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The present application provides a method and device for determining substation load, so as to at least solve the technical problem that the power load prediction in the prior art often focuses on the analysis of the overall load without considering other influencing factors, resulting in low accuracy of the total load prediction result of the main transformer.

[0006] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method and device for determining the load of a substation are provided, including: obtaining N load data sets and M influencing factor data sets of the substation in a first time period, wherein the N load data sets include at least a basic load data set, a load data set of power-consuming equipment, and a load data set of power supply equipment, and the M influencing factor data sets include at least a holiday data set and a climate data set, wherein the basic load data set is a collection of the load data of the substation itself; obtaining the correlation between each load data set and each influencing factor data set, wherein the correlation is used to quantify the correlation between each load data set and each influencing factor data set; inputting the N load data sets, the M influencing factor data sets, and the correlation between each load data set and each influencing factor data set into a target model; converting the data in the N load data sets, the data in the M influencing factor data sets, and the correlation into feature vectors through the target model, and determining N target load data sets of the substation in a second time period according to the feature vectors, wherein the N target load data sets correspond one-to-one to the N load data sets, and the second time period is after the first time period.

[0007] Optionally, the load data set of the electrical equipment includes at least one of the following load data sets: electric vehicle load data, used to characterize the load data generated by the electric vehicle powered by the substation in a first time period; target season equipment load data, used to characterize the load data generated by the target equipment powered by the substation in the target season included in the first time period, wherein the usage frequency of the target equipment in the target season is greater than the usage frequency in the non-target season.

[0008] Optionally, the load data set of the power supply equipment includes at least one of the following load data sets: photovoltaic power generation equipment load data, used to characterize the output load data of the photovoltaic power generation equipment connected to the substation in the first time period; wind power generation equipment load data, used to characterize the output load data of the wind power generation equipment connected to the substation in the first time period.

[0009] Optionally, the basic load data set is obtained through the following steps: obtaining a historical load data set of the substation; calculating the difference between the historical load data set and the load data set of the power equipment at the same time, and forming all the calculated differences into a first load data set; summing the load data of the first load data set and the load data set of the power supply equipment at the same time, and forming all the calculated summed results into the basic load data set.

[0010] Optionally, in the process of obtaining the correlation between each load data set and each influencing factor data set, the method for determining the substation load also includes: normalizing the i-th load data set among the N load data sets to obtain the i-th target load data set; normalizing the j-th influencing factor data set among the M influencing factor data sets to obtain the j-th target influencing factor data set; calculating the correlation coefficient between the i-th target load data set and the j-th target influencing factor data set, wherein the correlation coefficient is used to evaluate the similarity between the i-th target load data set and the j-th target influencing factor data set; and obtaining the correlation between the i-th target load data set and the j-th target influencing factor data set according to the correlation coefficient.

[0011] Optionally, calculating the correlation coefficient between the i-th target load data set and the j-th target influencing factor data set includes: dividing the first time period into K moments; calculating the absolute difference between the i-th target load data set and the j-th target influencing factor data set at the target moment, wherein the target moment is any moment among the K moments; calculating the absolute difference between the i-th target load data set and the j-th target influencing factor data set at each of the K moments to obtain K absolute differences; obtaining the minimum and maximum values ​​among the K absolute differences; and determining the correlation coefficient of the target moment based on the absolute difference, minimum value and maximum value of the target moment.

[0012] Optionally, obtaining the correlation between the ith target load data set and the jth target influencing factor data set according to the correlation coefficient includes: calculating the average value of all correlation coefficients between the ith target load data set and the jth target influencing factor data set to obtain the correlation.

[0013] Optionally, the target model is trained through the following steps including: obtaining a training data set, wherein the training data set includes N load data sets and M influencing factor data sets of the substation in a first historical time period, and N load data sets of the substation in a second historical time period, wherein the second historical time period is after the first historical time period, and the interval length between the second historical time period and the first historical time period is the same as the interval length between the first time period and the second time period; using the N load data sets and M influencing factor data sets of the substation in the first historical time period as training samples; using the N load data sets of the substation in the second historical time period as training labels; iteratively training the neural network according to the training samples and training labels to obtain the target model.

[0014] According to another aspect of the present application, a device for determining the load of a substation is also provided, comprising: a first acquisition unit, which acquires N load data sets and M influencing factor data sets of the substation in a first time period, wherein the N load data sets include at least a basic load data set, a load data set of power-consuming equipment, and a load data set of power supply equipment, and the M influencing factor data sets include at least a holiday data set and a climate data set, wherein the basic load data set is a collection of the load data of the substation itself; a second acquisition unit, which acquires the correlation between each load data set and each influencing factor data set, wherein the correlation is used to quantify the correlation between each load data set and each influencing factor data set; a first input unit, which inputs the N load data sets, the M influencing factor data sets, and the correlation between each load data set and each influencing factor data set into a target model; a first determination unit, which converts the data in the N load data sets, the data in the M influencing factor data sets, and the correlation into feature vectors through the target model, and determines the N target load data sets of the substation in the second time period according to the feature vectors, wherein the N target load data sets correspond one-to-one to the N load data sets, and the second time period is after the first time period.

[0015] According to another aspect of the present application, an electronic device is also provided, comprising: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors are enabled to run the programs, wherein the program is configured to execute any one of the above-mentioned methods for determining the substation load when running. In the present application, firstly, N load data sets and M influencing factor data sets of the substation in the first time period are obtained, wherein the N load data sets include at least a basic load data set, a load data set of power-consuming equipment and a load data set of power supply equipment, and the M influencing factor data sets include at least a holiday data set and a climate data set, wherein the basic load data set is a collection of the substation's own load data, and then the correlation between each load data set and each influencing factor data set is obtained, wherein the correlation is used to quantify the correlation between each load data set and each influencing factor data set, and then the N load data sets, the M influencing factor data sets and the correlation between each load data set and each influencing factor data set are input into the target model, and finally, the data in the N load data sets, the data in the M influencing factor data sets and the correlation are converted into feature vectors through the target model, and the N target load data sets of the substation in the second time period are determined according to the feature vectors, wherein the N target load data sets correspond one-to-one to the N load data sets, and the second time period is after the first time period.

[0016] It can be seen from the above content that in the present application, by obtaining multiple load data sets and multiple influencing factor data sets at historical moments, and calculating the correlation between multiple load data sets and multiple influencing factor data sets, the degree of influence of different influencing factors on the load data set is determined, and the substation load at future moments is predicted based on the three dimensions of multiple load data sets, multiple influencing factor data sets and correlation, and finally the total load data of the substation at future moments is predicted. Compared with the prior art that only predicts the total load at future moments by analyzing the total load at historical moments, the present application takes into account the influence of various influencing factors on various types of loads on this basis, thereby achieving the technical effect of improving the accuracy of load prediction, and further solves the technical problem that the prior art often focuses on the analysis of the overall load for power load prediction without considering other influencing factors, resulting in low accuracy of the total load prediction result of the main transformer. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 is a flow chart of an optional method for determining substation load according to an embodiment of the present application;

[0019] Figure 2 is a schematic diagram of an optional method for determining a substation load according to an embodiment of the present application;

[0020] Figure 3 is a schematic diagram of an optional basic structure of an LSTM neural network according to an embodiment of the present application;

[0021] Figure 4 It is a schematic diagram of an optional device for determining substation load according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] It should also be noted that the information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) collected by this application are information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data are in compliance with the relevant laws, regulations and standards of the relevant regions, necessary confidentiality measures are taken, and public order and good customs are not violated, and corresponding operation portals are provided for users to choose to authorize or refuse. For example, an interface is set up between this system and relevant users or organizations. Before obtaining relevant information, it is necessary to send an acquisition request to the aforementioned user or organization through the interface, and obtain relevant information after receiving the consent information fed back by the aforementioned user or organization.

[0025] According to an embodiment of the present application, an embodiment of a method for determining a substation load is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0026] It should be noted that an intelligent prediction system can be used as the execution subject of the method for determining the substation load in the embodiment of the present application. It is understandable that the method for determining the substation load provided in the embodiment of the present application can also be executed by other systems or devices, and the embodiment of the present application does not specifically limit this.

[0027] Figure 1 is a flow chart of an optional method for determining substation load according to an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps:

[0028] Step S101: Obtain N load data sets and M influencing factor data sets of a substation in a first time period.

[0029] In step S101 , the N load data sets include at least a basic load data set, a load data set of power-consuming equipment, and a load data set of power supply equipment, and the M influencing factor data sets include at least a holiday data set and a climate data set.

[0030] In step S101, the basic load data set is a collection of load data of the substation itself.

[0031] Optionally, the load data set of the electrical equipment includes but is not limited to an electric vehicle load data set and an air conditioning load data set.

[0032] Optionally, the load data set of the power supply equipment includes but is not limited to a photovoltaic output data set and a wind power output data set.

[0033] Optionally, the climate data set includes, but is not limited to, temperature, humidity, wind speed, and solar radiation intensity.

[0034] Optionally, data information such as holidays, temperature, humidity, wind speed, and solar radiation intensity can be collected from sources such as economic statistics departments and meteorological stations to form a holiday data set D time and climate data set D weather :

[0035] D time ={(hour,week,holiday),hour∈H,week∈W,holiday∈{0,1}}

[0036] D weather ={(temp,humidity,wind,solar direct ,solar scatter ),t∈T}

[0037] Among them, H is the hour number set 1-24, W is the week number set 1-7, holiday is the holiday mark, where 1 represents a holiday and 0 represents a non-holiday, temp, humidity, wind, solar direct and solar scatter They represent the temperature, humidity, wind speed, direct solar intensity and solar scattered intensity at time point t respectively.

[0038] Optionally, the N acquired load data sets are decomposed into multiple load data sets, including a basic load data set, an electric vehicle load data set, a photovoltaic and wind power output data set, and a target season equipment load data set, wherein the target season equipment load data includes summer air conditioning load data and winter heating load data.

[0039] Step S102: Obtain the correlation between each load data set and each influencing factor data set.

[0040] In step S102 , the correlation degree is used to quantify the correlation between each load data set and each influencing factor data set.

[0041] Optionally, a grey correlation analysis method is used to analyze the correlation between each influencing factor data set and each load data set, determine the degree of influence of different influencing factors on different loads, and calculate the correlation between each load data set and each influencing factor data set.

[0042] Alternatively, the grey correlation analysis method is a multivariate analysis method, which is often used to analyze the correlation and influence degree between multiple factors. It is mainly used to evaluate and sort the correlation degree between factors when the sample data is incomplete, the information is uncertain or the variability is large. Specifically, the grey correlation analysis method standardizes the data sequence and then calculates the correlation degree of each factor under different circumstances to find out the degree of influence of each factor on the result. Its basic idea is to transform the original data sequence into a grey series, and then use the regularity of these series to analyze the relationship between factors.

[0043] Step S103: inputting the N load data sets, the M influencing factor data sets, and the correlation between each load data set and each influencing factor data set into the target model.

[0044] Optionally, an LSTM (Long Short-Term Memory) model is used as a target model.

[0045] Step S104: converting the data in the N load data sets, the data in the M influencing factor data sets and the correlation degrees into feature vectors through the target model, and determining N target load data sets of the substation in the second time period according to the feature vectors.

[0046] In step S104, the N target load data sets correspond to the N load data sets one by one, and the second time period is after the first time period.

[0047] Optionally, the data in the N load data sets, the data in the M influencing factor data sets and the correlation are constructed into a feature vector as the input of the trained prediction model to predict the N target load data sets in the future time period (ie, the second time period).

[0048] Alternatively, for example, Figure 2 is a schematic diagram of an optional method for determining a substation load according to an embodiment of the present application, such as Figure 2As shown, firstly, multiple load data sets and influencing factor data sets are obtained, wherein the load data sets include the total load of the main substation, summer load, winter load, typical daily load in spring and autumn, photovoltaic and wind power output, and electric vehicle load; the influencing factor data sets include holidays and weather, etc. The obtained load data are decomposed into basic load, winter heating load, summer air conditioning load, electric vehicle load, photovoltaic and wind power output, and the key factors affecting the load are accurately identified based on grey correlation analysis to provide a scientific basis for the prediction model. Finally, the LSTM algorithm is used to predict various types of loads.

[0049] Optionally, for each type of load L k , where k represents the load type, such as basic load, summer air conditioning load, etc., and constructs its characteristic vector X k (t) As the input of the LSTM model, the feature vector includes historical load data and its related influencing factor data, which is specifically expressed as formula (1):

[0050] X k (t) = [L k (tn),…,L k (t-1),F1(t),F2(t),…,F m (t)] (1)

[0051] Among them, L k (tn) to L k (t-1) represents the historical load data, F1(t) to F m (t) represents the selected influencing factor data, such as temperature, humidity, time information, etc., n is the time window length of historical data, and m is the number of influencing factors.

[0052] For each type of load L k , using its corresponding eigenvector X k (t) Train the LSTM model and perform load forecasting. The output of the LSTM model is the load forecast value for the next time t+1 The prediction process can be expressed as formula (2):

[0053]

[0054] Among them, LSTM k represents the LSTM model trained for the k-th class of loads.

[0055] Base load The predicted value is shown in formula (3):

[0056]

[0057] Summer air conditioning load The predicted value is shown in formula (4):

[0058]

[0059] Winter heating load L heating,winter The (t+1) predicted value is shown in formula (5):

[0060] L heating,winter (t+1)=LSTM heating,winter (X heating,winter (t)) (5)

[0061] Electric vehicle load The predicted value is shown in formula (6):

[0062]

[0063] Photovoltaic output and wind power output The predicted values ​​are shown in formulas (7) and (8) respectively:

[0064]

[0065] The LSTM prediction results of all types of loads and the prediction results of photovoltaic and wind power output are combined to obtain the predicted value of the total load of the substation The calculation formula is shown in (9):

[0066]

[0067] It can be seen from the contents of steps S101 to S104 that in the present application, firstly, N load data sets and M influencing factor data sets of the substation in the first time period are obtained, wherein the N load data sets include at least a basic load data set, a load data set of power-consuming equipment, and a load data set of power supply equipment, and the M influencing factor data sets include at least a holiday data set and a climate data set, wherein the basic load data set is a collection of the substation's own load data, and then the correlation between each load data set and each influencing factor data set is obtained, wherein the correlation is used to quantify the correlation between each load data set and each influencing factor data set, and then the N load data sets, the M influencing factor data sets, and the correlation between each load data set and each influencing factor data set are input into the target model, and finally, the data in the N load data sets, the data in the M influencing factor data sets, and the correlation are converted into feature vectors through the target model, and the N target load data sets of the substation in the second time period are determined according to the feature vectors, wherein the N target load data sets correspond one-to-one to the N load data sets, and the second time period is after the first time period.

[0068] It can be seen from the above content that in the present application, by obtaining multiple load data sets and multiple influencing factor data sets at historical moments, and calculating the correlation between multiple load data sets and multiple influencing factor data sets, the degree of influence of different influencing factors on the load data set is determined, and the substation load at future moments is predicted based on the three dimensions of multiple load data sets, multiple influencing factor data sets and correlation, and finally the total load data of the substation at future moments is predicted. Compared with the prior art that only predicts the total load at future moments by analyzing the total load at historical moments, the present application takes into account the influence of various influencing factors on various types of loads on this basis, thereby achieving the technical effect of improving the accuracy of load prediction, and further solves the technical problem that the prior art often focuses on the analysis of the overall load for power load prediction without considering other influencing factors, resulting in low accuracy of the total load prediction result of the main transformer.

[0069] In an optional embodiment, the load data set of the electrical equipment includes at least one of the following load data sets: electric vehicle load data, which is used to characterize the load data generated by the electric vehicle powered by the substation in the first time period; target season equipment load data, which is used to characterize the load data generated by the target equipment powered by the substation in the target season included in the first time period, wherein the usage frequency of the target equipment in the target season is greater than the usage frequency in the non-target season.

[0070] Optionally, the operation data of smart meters and charging piles are used to record the charging load of electric vehicles to form an electric vehicle load data set D EV :D EV ={L EV (t),t∈T}, where L EV (t) represents the electric vehicle load value at time point t.

[0071] Optionally, collect historical load data in spring and autumn, select days with strong representativeness and small fluctuations as typical days, and form a typical day set D typical :D typical ={L spring (t),L autumn (t),t∈T typical}, where L spring (t) and L autumn (t) represents the load value at time t on a typical day in spring and autumn, respectively, typical is the set of observation times for a typical day.

[0072] Optionally, the target seasonal equipment load data includes summer air conditioning load data and winter heating load data, which are special load types caused by seasonal changes. The difference between the summer total load and the spring typical daily load is used as the summer air conditioning load data, and the difference between the winter total load and the autumn typical daily load is used as the winter heating load data, wherein the summer air conditioning load data is shown by formula (10), and the winter heating load data is shown by formula (11):

[0073] L AC,summer =L total,summer -L typical,spring (10)

[0074] Among them, L AC,summer Indicates summer air conditioning load, L total,summer is the total load in summer, L typical,spring It is the typical daily load in spring and serves as the benchmark load in the non-air-conditioning season.

[0075] L heating,winter =L total,winter -L typical,autumn (11)

[0076] Among them, L heating,winter Indicates winter heating load, L total,winter is the total load in winter, L typical,autumn It is the typical daily load in autumn and also serves as the benchmark load in the non-heating season.

[0077] From the above content, it can be seen that the load data set of power-consuming equipment includes the electric vehicle load data set and the target season equipment load data set. By refining the load data set of power-consuming equipment, the correlation between each type of load data set and multiple influencing factors can be more accurately analyzed, and the impact of different influencing factors on different types of load data sets can be obtained, which can help the intelligent prediction system to more accurately understand and predict the load demand of the substation.

[0078] In an optional embodiment, the load data set of the power supply equipment includes at least one of the following load data sets: photovoltaic power generation equipment load data, which is used to characterize the output load data of the photovoltaic power generation equipment connected to the substation in the first time period; wind power generation equipment load data, which is used to characterize the output load data of the wind power generation equipment connected to the substation in the first time period.

[0079] Optionally, the historical output data of each photovoltaic power station and wind farm can be obtained through the operation data platform to form a photovoltaic output data set D PV :D PV = {P PV (t),t∈T} and wind power output dataset D win:D wind = {P wind (t),t∈T}, where P PV (t) and P wind (t) represent the photovoltaic and wind power output values ​​at time point t respectively.

[0080] From the above content, it can be seen that the power supply equipment contains multiple output load data sets. Taking multiple output load data sets as a type of load data set and studying the influence of multiple influencing factors on the output load data set can help the intelligent prediction system to more accurately predict the output load data set data.

[0081] In an optional embodiment, the intelligent prediction system obtains a historical load data set of the substation, then calculates the difference between the historical load data set and the load data of the power equipment at the same time, and combines all the calculated differences into a first load data set. Finally, the first load data set and the load data of the power supply equipment at the same time are summed up, and all the calculated summations are combined into a basic load data set.

[0082] Optionally, the time series data of the historical total load of the substation is obtained through the database interface of the power system operator to form a historical load data set D of the substation total :D total ={L total (t),t∈T}, where L total (t) represents the total load value of the main transformer at time point t, and T is the observation time set.

[0083] Alternatively, the base load refers to the load obtained by removing the target seasonal equipment load (such as summer air conditioning load and winter heating load) and the electric vehicle load, and adding the renewable energy output (photovoltaic and wind power). The calculation method is shown in (12):

[0084] L base =L total -(L AC,summer +L heating,winter )-L EV +(P PV +P wind )(12)

[0085] Among them, L base Indicates the base load, L total is the total historical load of the substation, L EV is the electric vehicle load, P PV and P wind They are the output of photovoltaic and wind power respectively.

[0086] From the above content, it can be seen that the intelligent prediction system calculates the basic load data set based on the historical total load set of the substation, the load data set of the power-consuming equipment, and the load data set of the power supply equipment. This can help manage the substation load, and subdividing the historical load data set of the substation into multiple sub-loads is conducive to studying the degree of influence of each type of sub-load on the factors affecting it.

[0087] In an optional embodiment, the intelligent prediction system normalizes the i-th load data set among N load data sets to obtain the i-th target load data set, normalizes the j-th influencing factor data set among M influencing factor data sets to obtain the j-th target influencing factor data set, calculates the correlation coefficient between the i-th target load data set and the j-th target influencing factor data set, wherein the correlation coefficient is used to evaluate the similarity between the i-th target load data set and the j-th target influencing factor data set, and finally obtains the correlation between the i-th target load data set and the j-th target influencing factor data set according to the correlation coefficient.

[0088] Optionally, the grey correlation analysis method is used to analyze the correlation between influencing factors such as holidays and climate and various loads, so as to determine the degree of influence of different influencing factors on different loads. First, the N load data sets and the M influencing factor data sets are normalized to ensure that the data are compared at the same level. The normalization formula is shown in (13):

[0089]

[0090] Where X is the load data set or influencing factor data set, X * is the standardized load data set or influencing factor data set, X min and X max are the minimum and maximum values ​​in the load data set or influencing factor data set, respectively.

[0091] From the above content, it can be seen that this process combines data normalization and correlation coefficient calculation to evaluate and analyze the complex correlation between multiple load data sets and multiple influencing factor data sets, thereby providing more accurate prediction capabilities and decision support for the intelligent prediction system.

[0092] In an optional embodiment, the intelligent prediction system divides the first time period into K moments, calculates the absolute difference between the i-th target load data set and the j-th target influencing factor data set at the target moment, wherein the target moment is any moment among the K moments, and then calculates the absolute difference between the i-th target load data set and the j-th target influencing factor data set at each of the K moments to obtain K absolute differences, obtains the minimum and maximum values ​​among the K absolute differences, and then determines the correlation coefficient of the target moment based on the absolute difference, minimum value and maximum value of the target moment.

[0093] Optionally, for each load data set D k And the influencing factor dataset D f , calculate the correlation coefficient ξ between them k,f , to evaluate the similarity between them, and calculate the correlation coefficient according to formula (14):

[0094]

[0095] Among them, Δ k,f (t) = |X k (t)-X f (t)|,X k (t) is the load data set D k The value at time t, X f (t) is the influencing factor dataset D f The value at time point t, Δ k,f (t) represents D k and D f The absolute difference at time point t, |Δ k,f | indicates D k and D f The set of absolute differences at each moment in history, minmin|Δ k,f | represents the minimum value in this absolute difference set, maxmax|Δ k,f | represents the maximum value in this absolute difference set, ρ is the resolution coefficient, which is usually taken as 0.5.

[0096] From the above content, it can be seen that by comparing the degree of difference between the target load data set and the influencing factor data set at different times, and combining the minimum difference and the maximum difference to determine the correlation coefficient, the degree of their correlation at a specific time can be evaluated. Such analysis can help the prediction system understand and quantify the relationship between the load data set and the influencing factor data set, and provide a more accurate basis for prediction and decision-making.

[0097] In an optional embodiment, the intelligent prediction system obtains the correlation degree by calculating the average value of all correlation coefficients between the i-th target load data set and the j-th target influencing factor data set.

[0098] Optionally, based on the correlation coefficient, the grey correlation degree γ between each type of load and each influencing factor is calculated k,f , to quantify the overall degree of correlation between them. The degree of correlation is calculated as shown in (15):

[0099]

[0100] Where N is the length of the time series.

[0101] Optionally, by comparing the grey correlation of different influencing factor data sets to various load data sets, the influencing factors with higher grey correlation will be regarded as important factors corresponding to the load change, and the top three influencing factors with the highest degree of influence on the load will be given priority in model prediction for model construction.

[0102] From the above content, it can be seen that the influence degree of each type of load and each influencing factor is determined by calculating the correlation degree, thereby determining the key factors affecting the load, providing a reference for load prediction.

[0103] In an optional embodiment, the intelligent prediction system obtains a training data set, wherein the training data set includes N load data sets and M influencing factor data sets of the substation in a first historical time period, and N load data sets of the substation in a second historical time period, wherein the second historical time period is after the first historical time period, and the interval length between the second historical time period and the first historical time period is the same as the interval length between the first time period and the second time period, and the N load data sets and M influencing factor data sets of the substation in the first historical time period are used as training samples, and the N load data sets of the substation in the second historical time period are used as training labels, and finally the neural network is iteratively trained according to the training samples and training labels to obtain the target model.

[0104] Optionally, various types of loads are predicted based on the LSTM model, and the prediction results are superimposed to obtain the total load at the next moment.

[0105] Optionally, the LSTM network consists of multiple LSTM units, each of which includes three gates: input gate, forget gate, and output gate. These gates capture and remember long-term dependencies in time series data by controlling the flow of information. Figure 3 is a schematic diagram of an optional basic structure of an LSTM neural network according to an embodiment of the present application, such as Figure 3 As shown in Figure 1, the memory unit and the hidden state memorize the historical information of the sequence data. The information in the memory unit is controlled by three gating units. The forget gate is based on h t-1 and x t Delete the information in the memory unit, as shown in formula (16):

[0106] f t =σ(W f ·[h t-1 ,x t ]+b f )(16)

[0107] Among them, σ is the Sigmoid activation function, W f is the weight matrix of the forget gate, b f is the bias term of the forget gate.

[0108] The input gate is based on h t-1 and x t Add new information to the memory unit, as shown in equations (17) and (18):

[0109] i t =σ(W i ·[h t-1 ,x t ]+b i ) (17)

[0110]

[0111] Among them, i t is the activation value of the input gate, that is, the information to be remembered, is a candidate memory unit, used to update the memory unit, W i , W C is the weight matrix of the input gate, b i 、b C is the bias term of the input gate.

[0112] Combine the information of the forget gate and the input gate to update the current memory unit C t , as shown in formula (19):

[0113]

[0114] Among them, C t-1 is the state of the memory unit at the previous moment.

[0115] The output gate is based on h t-1 and x t Determine the output information of the current memory unit, as shown in formula (20):

[0116] o t =σ(W o ·[h t-1 ,x t ]+b o ) (20)

[0117] Among them, W o is the weight matrix of the output gate, b o is the bias term of the output gate.

[0118] Combine the information of the current memory unit and the output gate to generate the hidden state h at the current moment t , as shown in formula (21):

[0119] h t =o t tanh(C t ) (twenty one)

[0120] Optionally, historical data is obtained as input for model training. When the model has been trained for multiple iterations and has achieved satisfactory performance indicators on the validation set, the training is considered complete. At this point, the obtained neural network model can be used to predict the load data of the substation in the future time period.

[0121] From the above content, it can be seen that through the above steps, the intelligent prediction system uses historical data to carry out a supervised learning training process, thereby obtaining a target model that can predict future load data.

[0122] According to an embodiment of the present application, an embodiment of a device for determining a substation load is also provided. Figure 4 is a schematic diagram of an optional device for determining substation load according to an embodiment of the present application, such as Figure 4 As shown, the device for determining the substation load includes: a first acquisition unit 401 , a second acquisition unit 402 , a first input unit 403 , and a first determination unit 404 .

[0123] Optionally, a first acquisition unit 401 is used to acquire N load data sets and M influencing factor data sets of the substation in a first time period, wherein the N load data sets include at least a basic load data set, a load data set of power-consuming equipment, and a load data set of power supply equipment, and the M influencing factor data sets include at least a holiday data set and a climate data set, wherein the basic load data set is a collection of the substation's own load data; a second acquisition unit 402 is used to acquire the correlation between each load data set and each influencing factor data set, wherein the correlation is used to quantify the correlation between each load data set and each influencing factor data set; a first input unit 403 is used to input the N load data sets, the M influencing factor data sets, and the correlation between each load data set and each influencing factor data set into a target model; a first determination unit 404 is used to convert the data in the N load data sets, the data in the M influencing factor data sets, and the correlation into feature vectors through the target model, and determine the N target load data sets of the substation in the second time period according to the feature vectors, wherein the N target load data sets correspond one-to-one to the N load data sets, and the second time period is after the first time period.

[0124] Optionally, the first acquisition unit 401 includes: a first data subunit and a second data subunit. The first data subunit, electric vehicle load data, is used to characterize the load data generated by the electric vehicle powered by the substation in the first time period; the second data subunit, target season equipment load data, is used to characterize the load data generated by the target equipment powered by the substation in the target season included in the first time period, wherein the use frequency of the target equipment in the target season is greater than the use frequency in the non-target season.

[0125] Optionally, the first acquisition unit 401 includes: a third data subunit and a fourth data subunit. The third data subunit is photovoltaic power generation equipment load data, which is used to characterize the output load data of the photovoltaic power generation equipment connected to the substation in the first time period; the fourth data subunit is wind power generation equipment load data, which is used to characterize the output load data of the wind power generation equipment connected to the substation in the first time period.

[0126] Optionally, the first acquisition unit 401 includes: a first acquisition subunit, a first calculation subunit, and a second calculation subunit. The first acquisition subunit is used to acquire the historical load data set of the substation; the first calculation subunit is used to calculate the difference between the historical load data set and the load data set of the power equipment at the same time, and all the calculated differences form the first load data set; the second calculation subunit is used to sum the first load data set and the load data set of the power supply equipment at the same time, and all the calculated summation results form the basic load data set.

[0127] Optionally, the second acquisition unit 402 includes: a first processing subunit, a second processing subunit, a third calculation subunit, and a first determination subunit. The first processing subunit is used to normalize the i-th load data set among the N load data sets to obtain the i-th target load data set; the second processing subunit is used to normalize the j-th influencing factor data set among the M influencing factor data sets to obtain the j-th target influencing factor data set; the third calculation subunit is used to calculate the correlation coefficient between the i-th target load data set and the j-th target influencing factor data set, wherein the correlation coefficient is used to evaluate the similarity between the i-th target load data set and the j-th target influencing factor data set; the first determination subunit is used to obtain the correlation between the i-th target load data set and the j-th target influencing factor data set according to the correlation coefficient.

[0128] Optionally, the third calculation subunit includes: a first processing module, a first calculation module, a second calculation module, a first acquisition module, and a first determination module. The first processing module is used to divide the first time period into K moments; the first calculation module is used to calculate the absolute difference between the i-th target load data set and the j-th target influencing factor data set at the target moment, wherein the target moment is any moment among the K moments; the second calculation module is used to calculate the absolute difference between the i-th target load data set and the j-th target influencing factor data set at each of the K moments to obtain K absolute differences; the first acquisition module is used to obtain the minimum and maximum values ​​among the K absolute differences; the first determination module is used to determine the correlation coefficient of the target moment according to the absolute difference, minimum and maximum values ​​of the target moment.

[0129] Optionally, the first determination subunit includes: a third calculation module, used to calculate the average value of all correlation coefficients between the i-th target load data set and the j-th target influencing factor data set to obtain the correlation degree.

[0130] Optionally, the first input unit 403 includes: a second acquisition subunit, a third processing subunit, a fourth processing subunit, and a second determination subunit. The second acquisition subunit is used to acquire a training data set, wherein the training data set includes N load data sets and M influencing factor data sets of the substation in the first historical time period, and N load data sets of the substation in the second historical time period, wherein the second historical time period is after the first historical time period, and the interval between the second historical time period and the first historical time period is the same as the interval between the first time period and the second time period; the third processing subunit is used to use the N load data sets and M influencing factor data sets of the substation in the first historical time period as training samples; the fourth processing subunit is used to use the N load data sets of the substation in the second historical time period as training labels; the second determination subunit is used to iteratively train the neural network according to the training samples and training labels to obtain the target model.

[0131] According to an embodiment of the present application, an electronic device is also provided, wherein the electronic device includes one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors are enabled to implement the method for running the program, wherein the program is configured to execute any of the above-mentioned methods for determining the load of a substation when running. The serial numbers of the above-mentioned embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

[0132] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0133] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0134] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0135] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0136] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0137] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for determining substation load, It is characterized in that include: Obtaining N load data sets and M influencing factor data sets of the substation in the first time period, wherein the N load data sets at least include a basic load data set, a load data set of power-consuming equipment, and a load data set of power supply equipment, and the M influencing factor data sets at least include a holiday data set and a climate data set, wherein the basic load data set is a collection of the substation's own load data; Acquire a correlation degree between each load data set and each influencing factor data set, wherein the correlation degree is used to quantify the correlation between each load data set and each influencing factor data set; Inputting the N load data sets, the M influencing factor data sets, and the correlation between each load data set and each influencing factor data set into a target model; The data in the N load data sets, the data in the M influencing factor data sets and the correlation degrees are converted into feature vectors through the target model, and the N target load data sets of the substation in the second time period are determined according to the feature vectors, wherein the N target load data sets correspond one-to-one to the N load data sets, and the second time period is after the first time period.

2. The method for determining the substation load according to claim 1, It is characterized in that The load data set of the electrical equipment includes at least one of the following load data sets: Electric vehicle load data, used to characterize load data generated by the electric vehicle powered by the substation during the first time period; The target season equipment load data is used to characterize the load data generated by the target equipment powered by the substation in the target season included in the first time period, wherein the usage frequency of the target equipment in the target season is greater than the usage frequency in the non-target season.

3. The method for determining the substation load according to claim 1, It is characterized in that The load data set of the power supply equipment includes at least one of the following load data sets: Photovoltaic power generation equipment load data, used to characterize the output load data of the photovoltaic power generation equipment connected to the substation in the first time period; The wind power generation equipment load data is used to represent the output load data of the wind power generation equipment connected to the substation in the first time period.

4. The method for determining the substation load according to claim 1, It is characterized in that The basic load data set is obtained by the following steps: Obtain historical load data sets for substations; Calculating the difference between the historical load data set and the load data corresponding to the same time in the load data set of the electrical equipment, and forming a first load data set with all the calculated differences; The first load data set and the load data set of the power supply equipment corresponding to the same time are summed up, and all the summed results are combined into the basic load data set.

5. The method for determining the substation load according to claim 1, It is characterized in that In the process of obtaining the correlation between each load data set and each influencing factor data set, the method for determining the substation load further includes: Normalizing the i-th load data set among the N load data sets to obtain the i-th target load data set; Normalizing the j-th influencing factor data set among the M influencing factor data sets to obtain the j-th target influencing factor data set; Calculating a correlation coefficient between the i-th target load data set and the j-th target influencing factor data set, wherein the correlation coefficient is used to evaluate the similarity between the i-th target load data set and the j-th target influencing factor data set; The correlation degree between the i-th target load data set and the j-th target influencing factor data set is obtained according to the correlation coefficient.

6. The method for determining the substation load according to claim 5, It is characterized in that Calculate the correlation coefficient between the i-th target load data set and the j-th target influencing factor data set, including: Divide the first time period into K moments; Calculating the absolute difference between the i-th target load data set and the j-th target influencing factor data set at a target time, wherein the target time is any one of the K time points; Calculate the absolute difference between the i-th target load data set and the j-th target influencing factor data set at each of the K moments to obtain K absolute differences; Obtaining the minimum and maximum values ​​of the K absolute differences; The correlation coefficient of the target time is determined according to the absolute difference of the target time, the minimum value and the maximum value.

7. The method for determining the substation load according to claim 6, It is characterized in that Obtaining the correlation between the i-th target load data set and the j-th target influencing factor data set according to the correlation coefficient includes: The average value of all correlation coefficients between the i-th target load data set and the j-th target influencing factor data set is calculated to obtain the correlation degree.

8. The method for determining the substation load according to claim 1, It is characterized in that The target model is trained by the following steps: Acquire a training data set, wherein the training data set includes N load data sets and M influencing factor data sets of the substation in a first historical time period, and N load data sets of the substation in a second historical time period, wherein the second historical time period is after the first historical time period, and the interval between the second historical time period and the first historical time period is the same as the interval between the first time period and the second time period; Taking N load data sets and M influencing factor data sets of the substation in the first historical time period as training samples; Using N load data sets of the substation in the second historical time period as training labels; The neural network is iteratively trained according to the training samples and the training labels to obtain the target model.

9. A device for determining substation load, It is characterized in that include: A first acquisition unit is configured to acquire N load data sets and M influencing factor data sets of the substation in a first time period, wherein the N load data sets at least include a basic load data set, a load data set of power-consuming equipment, and a load data set of power supply equipment, and the M influencing factor data sets at least include a holiday data set and a climate data set, wherein the basic load data set is a collection of the substation's own load data; A second acquisition unit is configured to acquire a correlation between each load data set and each influencing factor data set, wherein the correlation is used to quantify the correlation between each load data set and each influencing factor data set; A first input unit, inputting the N load data sets, the M influencing factor data sets, and the correlation between each load data set and each influencing factor data set into a target model; The first determination unit converts the data in the N load data sets, the data in the M influencing factor data sets and the correlation degree into feature vectors through the target model, and determines the N target load data sets of the substation in the second time period according to the feature vectors, wherein the N target load data sets correspond one-to-one to the N load data sets, and the second time period is after the first time period.

10. An electronic device, It is characterized in that The electronic device includes one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors are implemented to run the programs, wherein the program is configured to execute the method for determining the substation load described in any one of claims 1 to 8 when running.

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