A method and system for power load forecasting based on big data

Abstract

The present invention relates to the technical field of power load forecasting, and specifically discloses a power load forecasting method and system based on big data. The method comprises obtaining a power grid model, configuring and numbering metering nodes based on the power grid model; obtaining power load based on the metering nodes, and synchronously calculating instantaneous load; counting instantaneous loads within a preset time period, comparing the counted instantaneous loads, and clustering the metering nodes; upon receiving a forecasting instruction, randomly selecting a metering node until the selected metering node meets a preset node condition, obtaining the instantaneous load of the selected metering node, and predicting the instantaneous load of all metering nodes based on the clustering result; synchronously verifying the error rate of the previous forecasting process, and updating the node condition based on the error rate. The present invention greatly reduces the resource pressure of obtaining data from the metering nodes while only partially reducing the accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of power load forecasting, and in particular to a power load forecasting method and system based on big data. Background Art

[0002] Power load forecasting has two meanings: it refers to various electrical equipment installed at user locations such as government agencies, enterprises, and residents, and it can also be used to describe the amount of power consumed by these devices. Power load forecasting is a series of forecasting tasks performed on power load. From the perspective of the forecast object, power load forecasting includes forecasts of future power demand (power), future power consumption (energy), and load curves.

[0003] The power load forecasting process requires the use of a large number of data collection devices. One prediction method is to directly obtain the collected data from these collection devices, process the collected data based on deep learning and other methods, and train a prediction model. In practical applications, it is only necessary to regularly collect data from all collection devices to predict the power load in the future. This is a global prediction solution that relies on the data of all metering nodes. In fact, when the number of metering nodes is large, the pressure of data acquisition and transmission is very high. How to further optimize and reduce the amount of metering data acquired during the prediction process is the technical problem that the technical solution of the present invention aims to solve. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for power load forecasting based on big data to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for predicting power load based on big data, the method comprising:

[0007] Obtain the power grid model, configure the metering nodes based on the power grid model and number them;

[0008] Obtain power load based on metering nodes and simultaneously calculate instantaneous load;

[0009] Count the instantaneous load within a preset time period, compare the counted instantaneous load, and cluster the metering nodes;

[0010] When a prediction instruction is received, a metering node is randomly selected until the selected metering node meets the preset node conditions, the instantaneous load of the selected metering node is obtained, and the instantaneous load of all metering nodes is predicted based on the clustering results;

[0011] Synchronously verifying the error rate of the previous prediction process, and updating the node condition based on the error rate;

[0012] The node condition is a quantity threshold. When the number of any type of metering nodes among the selected metering nodes is greater than the quantity threshold, the node condition is considered to be met.

[0013] As a further solution of the present invention: the step of obtaining the power load based on the metering node and synchronously calculating the instantaneous load includes:

[0014] For each metering node, the power load is obtained regularly according to the preset frequency;

[0015] Sorting the acquired power loads based on time, and performing discrete Fourier transform on the sorted power loads to determine frequency distribution characteristics;

[0016] Selecting a preset number of frequencies from the frequency distribution characteristics, and creating a frequency-based trigonometric function sum as a benchmark function for the fitting process;

[0017] The benchmark function is used to fit the sorted power load to obtain the fitting function of each metering node;

[0018] Derivative the fitting function to obtain a load derivative function, and determine the instantaneous load at each moment based on the load derivative function;

[0019] The benchmark function is: Where N is the preset number; a0, a n and b n are all randomly determined constants, w n It is the nth frequency value after sorting the N selected frequencies.

[0020] As a further solution of the present invention, the step of counting instantaneous loads within a preset time period, comparing the counted instantaneous loads, and clustering the metering nodes includes:

[0021] Receive the time period input by the management party and intercept the load derivative function within the time period;

[0022] For any two metering nodes, calculate the difference function of the load derivative function and calculate the integral of the difference function over the time period as the node distance;

[0023] The metering nodes are clustered based on the node distances.

[0024] As a further solution of the present invention: the step of clustering the metering nodes based on the node distance includes:

[0025] Receive the number of categories entered by the management party;

[0026] Randomly select the number of metering nodes in the metering nodes as the category center;

[0027] For any metering node, it is classified according to its distance from the center of each category;

[0028] After all metering nodes are classified once, the category center is updated;

[0029] The loop is executed until a preset exit condition is reached; the exit condition includes that the number of iterations reaches a threshold and the update amount of the category center is less than a preset update amount threshold;

[0030] For any metering node, the rule for classifying it according to its distance from the center of each category is: label i =argmin 1≤j≤k ||x i -μ j ||;label i is the classification result of the i-th metering node, x i represents the i-th metering node, μ j represents the jth category center, and k represents the total number of category centers;

[0031] The updated category center is the center of the same type of metering nodes in space.

[0032] As a further solution of the present invention, when a prediction instruction is received, randomly selecting a metering node until the selected metering node meets a preset node condition, obtaining the instantaneous load of the selected metering node, and predicting the instantaneous load of all metering nodes based on the clustering result includes the following steps:

[0033] When a prediction instruction is received, a metering node is randomly selected and the category to which it belongs is obtained;

[0034] Calculate the number of metering nodes of each category, and continue selecting when the number does not exceed a preset number threshold; and stop selecting when the number exceeds the preset number threshold;

[0035] Obtain the instantaneous load of the selected metering node and calculate its ratio to the instantaneous load at the previous moment;

[0036] For other metering nodes of the same category, the product of the instantaneous load at the previous moment and the ratio is calculated as the predicted instantaneous load.

[0037] As a further solution of the present invention, the step of synchronously verifying the error rate of the previous prediction process and updating the node condition based on the error rate includes:

[0038] Read the calculated ratio, and calculate the absolute value of the difference between the ratio and 1 as the error rate;

[0039] The scaling ratio is determined according to the direct proportion of the error rate, and the scaling ratio is multiplied by the quantity threshold and rounded up to an integer, which is used as the updated node condition.

[0040] The technical solution of the present invention also provides a power load forecasting system based on big data, the system comprising:

[0041] Node configuration module, used to obtain the power grid model, configure metering nodes based on the power grid model and number them;

[0042] The instantaneous load acquisition module is used to obtain the power load based on the metering node and synchronously calculate the instantaneous load;

[0043] The node clustering module is used to count the instantaneous load within a preset time period, compare the counted instantaneous load, and cluster the metering nodes;

[0044] The node selection application module is used to randomly select a metering node when receiving a prediction instruction until the selected metering node meets the preset node conditions, obtain the instantaneous load of the selected metering node, and predict the instantaneous load of all metering nodes based on the clustering results;

[0045] A node condition updating module, configured to synchronously verify the error rate of the previous prediction process and update the node condition based on the error rate;

[0046] The node condition is a quantity threshold. When the number of any type of metering nodes among the selected metering nodes is greater than the quantity threshold, the node condition is considered to be met.

[0047] As a further solution of the present invention: the instantaneous load acquisition module includes:

[0048] The power load acquisition unit is used to obtain the power load of each metering node according to a preset frequency timing;

[0049] A spectrum identification unit is used to sort the acquired power loads based on time, and perform discrete Fourier transform on the sorted power loads to determine frequency distribution characteristics;

[0050] A reference function determination unit is used to select a preset number of frequencies from the frequency distribution characteristics and create a frequency-based trigonometric function sum as a reference function for the fitting process;

[0051] The power load fitting unit is used to fit the sorted power loads using the benchmark function to obtain a fitting function for each metering node;

[0052] a fitting function derivation unit, configured to derive the fitting function to obtain a load derivative function, and determine the instantaneous load at each moment based on the load derivative function;

[0053] The benchmark function is: Where N is the preset number; a0, a n and b n are all randomly determined constants, w n It is the nth frequency value after sorting the N selected frequencies.

[0054] As a further solution of the present invention: the node clustering module includes:

[0055] A derivative function interception unit is used to receive a time period input by a management party and intercept the load derivative function within the time period;

[0056] The node distance calculation unit is used to calculate the difference function of the load derivative function of any two metering nodes, and calculate the integral of the difference function over the time period as the node distance;

[0057] A clustering execution unit is configured to cluster the metering nodes based on the node distances.

[0058] As a further solution of the present invention: the node selection application module includes:

[0059] A category query unit is used to randomly select a metering node and obtain the category to which it belongs when receiving a prediction instruction;

[0060] a quantity determination unit, configured to calculate the number of metering nodes of each category, and continue selecting when the number does not exceed a preset number threshold, and stop selecting when the number exceeds the preset number threshold;

[0061] A ratio calculation unit, used to obtain the instantaneous load of the selected metering node and calculate the ratio of the instantaneous load to the instantaneous load at the previous moment;

[0062] The comparison prediction unit is used to calculate the product of the instantaneous load at the previous moment and the ratio for other metering nodes of the same category as the predicted instantaneous load.

[0063] Compared with the prior art, the present invention has the following beneficial effects: when acquiring data from metering nodes, the present invention clusters the metering nodes according to the data of the metering nodes. In the actual use stage, only the collected data of a limited number of metering nodes is acquired, and the data of other metering nodes is predicted based on the collected data. Then, the prediction model obtained by deep learning is applied, which greatly reduces the resource pressure of acquiring data from the metering nodes while only partially reducing the accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention.

[0065] Figure 1 This is a flowchart of the power load forecasting method based on big data.

[0066] Figure 2 This is the first sub-process flow chart of the power load forecasting method based on big data.

[0067] Figure 3 This is the second sub-process diagram of the power load forecasting method based on big data.

[0068] Figure 4 This is the third sub-process block diagram of the power load forecasting method based on big data.

[0069] Figure 5 This is the fourth sub-process flow chart of the power load forecasting method based on big data.

[0070] Figure 6 This is a structural block diagram of the power load forecasting system based on big data. DETAILED DESCRIPTION

[0071] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0072] Figure 1 This is a flowchart of a method for predicting power load based on big data. In an embodiment of the present invention, a method for predicting power load based on big data includes:

[0073] Step S100: Obtain a power grid model, configure metering nodes based on the power grid model and number them;

[0074] The power grid model is a model composed of power supply lines and power supply equipment. In the power supply site, the power grid model belongs to existing data, that is, for the technical solution of the present invention, the power grid model is known data. The power grid model is obtained, and metering nodes are configured and numbered in the power grid model. The metering nodes are points where monitoring equipment is installed in the power grid. The monitoring equipment can use conventional power meters. In actual applications, each metering node is marked in the power grid model for each installation. The function of the power grid model containing metering nodes is mainly to connect with the administrator to facilitate the administrator to control the monitoring process of the entire power system.

[0075] Step S200: obtaining power load based on the metering node and synchronously calculating instantaneous load;

[0076] An electric power monitoring meter is installed at the metering node. The electric power monitoring meter is used to obtain the voltage, current or other electric power data in the power grid, collectively referred to as the electric power load, and process the electric power load to calculate the instantaneous load. It should be noted that in actual scenarios, the data obtained by the electric power monitoring meter is instantaneous data itself, such as instantaneous current, etc. That is, the electric power load in the above content is instantaneous data. On this basis, the instantaneous load further calculated is actually the rate of change of the instantaneous data.

[0077] Step S300: Counting the instantaneous load within a preset time period, comparing the counted instantaneous loads, and clustering the metering nodes;

[0078] The management party of the execution entity of this method sets a time period, such as ten minutes, and obtains the instantaneous load of all metering nodes within the time period. By comparing the instantaneous loads, similar metering nodes can be classified into one category. In this process, it should be noted that the instantaneous load is the rate of change of the instantaneous data, which is a unitless value. The metering nodes are clustered based on the instantaneous load, and the metering nodes can be clustered regardless of their type.

[0079] Step S400: When a prediction instruction is received, a metering node is randomly selected until the selected metering node meets the preset node condition, the instantaneous load of the selected metering node is obtained, and the instantaneous load of all metering nodes is predicted based on the clustering result;

[0080] When the execution subject of this method receives a prediction instruction, it randomly selects a metering node, determines the category of the metering node based on the clustering result, and determines in real time the number of each type of metering nodes among the selected metering nodes. When the number of metering nodes of each type is greater than the preset number threshold, it is considered that the node condition is met. Then, the instantaneous load of the selected metering node is used as the instantaneous load of other metering nodes of the same type. In this process, the global load acquisition process of multiple metering nodes is simplified to a load sampling process.

[0081] Step S500: synchronously verifying the error rate of the previous prediction process, and updating the node condition based on the error rate;

[0082] During each prediction, data from a limited number of metering nodes (randomly selected) is obtained. The data from these metering nodes is real, and the others are predicted. In the previous prediction process, the data from the same metering node was very likely predicted. By comparing the real data and predicted data of the same metering node, the error rate can be determined. The larger the error rate, the more metering nodes are needed, and the smaller the error rate, the fewer metering nodes are needed.

[0083] Specifically, the node condition is a quantity threshold. When the number of any type of metering nodes among the selected metering nodes is greater than the quantity threshold, it is considered to meet the node condition. When the error rate is large, the quantity threshold is large, and when the error rate is small, the quantity threshold is small.

[0084] Figure 2 This is a block diagram of the first sub-process of the power load forecasting method based on big data. The steps of obtaining the power load based on the metering node and synchronously calculating the instantaneous load include:

[0085] Step S201: For each metering node, obtain the power load according to a preset frequency timing;

[0086] Step S202: sorting the acquired power loads based on time, and performing discrete Fourier transform on the sorted power loads to determine frequency distribution characteristics;

[0087] Step S203: selecting a preset number of frequencies from the frequency distribution features, and creating a frequency-based trigonometric function sum as a reference function for the fitting process;

[0088] Step S204: fitting the sorted power loads using the benchmark function to obtain a fitting function for each metering node;

[0089] Step S205: Deriving the fitting function to obtain a load derivative function, and determining the instantaneous load at each moment based on the load derivative function;

[0090] The benchmark function is: Where N is the preset number; a0, a n and b n are all randomly determined constants, w n It is the nth frequency value after sorting the N selected frequencies.

[0091] For each metering node, the power load is acquired at a preset frequency and then sorted chronologically to obtain a discrete data set. In actual applications, the power load has a certain periodicity. For example, the power load in industrial areas is higher during working hours on weekdays and lower during rest time. However, this periodicity is not strict.

[0092] By performing a discrete Fourier transform on a discrete data set, we can obtain a spectrum diagram, acquire the frequency distribution in the discrete data set, select the most important frequency, and create a trigonometric function sum from the frequency as the benchmark function for the fitting process. Then, the discrete data set is fitted with the benchmark function. Since the frequency is known, only the amplitude and phase need to be determined. The fitting process can use the minimum distance as the goal, that is, the sum of the distances between each data in the discrete data set and the corresponding function value in the fitting function is minimized.

[0093] The fitting function is derived to obtain a load derivative function, and the instantaneous load at each moment, that is, the rate of change of the instantaneous data, is determined based on the load derivative function.

[0094] Figure 3 This is a second sub-flow diagram of the power load forecasting method based on big data. The steps of counting instantaneous loads within a preset time period, comparing the counted instantaneous loads, and clustering metering nodes include:

[0095] Step S301: receiving a time period input by a management party, and intercepting a load derivative function within the time period;

[0096] Step S302: For any two metering nodes, calculate the difference function of the load derivative function, and calculate the integral of the difference function over the time period as the node distance;

[0097] Step S303: clustering the metering nodes based on the node distances.

[0098] The above content is a clustering process, which receives the time period input by the management party and intercepts the load derivative function within the time period. The time period can be five minutes, ten minutes or other shorter times. After the time period is determined, the time period part is intercepted in the determined load derivative function.

[0099] For any metering node, the load derivative function within the time period can be obtained. For any two metering nodes, the difference function between the load derivative functions is calculated, and then the integral of the difference function over the time period is calculated as the node distance, which is used to characterize the degree of difference between the two nodes. The metering nodes can be clustered based on the node distance.

[0100] Specifically, the step of clustering the metering nodes based on the node distance includes:

[0101] Receive the number of categories entered by the management party;

[0102] Randomly select the number of metering nodes in the metering nodes as the category center;

[0103] For any metering node, it is classified according to its distance from the center of each category;

[0104] After all metering nodes are classified once, the category center is updated;

[0105] The loop is executed until a preset exit condition is reached; the exit condition includes that the number of iterations reaches a threshold and the update amount of the category center is less than a preset update amount threshold.

[0106] The above content provides a specific metering node clustering scheme, which is essentially an application of the K-means clustering method. First, the number of classifications input by the management party of the execution subject of this method is received, and the number of metering nodes of the classification is randomly selected from all metering nodes as category centers. Then, for any metering node, the nearest category center is selected to classify it, and the number of categories can be obtained. After all metering nodes are classified once, the centers of each type of metering nodes are calculated as new category centers; finally, the above process is executed in a loop. If the category center no longer changes or the change is small enough, the classification is considered to be completed. At this time, the last classification result can be retained.

[0107] For any metering node, the rule for classifying it according to its distance from the center of each category is: label i =argmin 1≤j≤k ||x i -μ j ||;label i is the classification result of the i-th metering node, x i represents the i-th metering node, μ j represents the jth category center, and k represents the total number of category centers;

[0108] The updated category center is the center of the same type of metering nodes in space.

[0109] Figure 4 This is a block diagram of the third sub-process of the power load forecasting method based on big data. When a forecast instruction is received, a metering node is randomly selected until the selected metering node meets the preset node conditions, the instantaneous load of the selected metering node is obtained, and the instantaneous load of all metering nodes is predicted based on the clustering results. The steps include:

[0110] Step S401: When a prediction instruction is received, a metering node is randomly selected and its category is obtained;

[0111] Step S402: Calculate the number of metering nodes in each category. When the number does not exceed a preset threshold, continue selecting. When the number exceeds the preset threshold, stop selecting.

[0112] Step S403: Obtain the instantaneous load of the selected metering node and calculate its ratio to the instantaneous load at the previous moment;

[0113] Step S404: For other metering nodes of the same category, the product of the instantaneous load at the previous moment and the ratio is calculated as the predicted instantaneous load.

[0114] When a prediction instruction is received, it indicates that the user needs to obtain metering data of each metering node. When the number of metering nodes is large, the process of obtaining data actually requires a lot of resources. Under the architecture of the technical solution of the present invention, only a limited number of metering nodes need to be randomly selected. When the selected metering node meets the conditions, the metering data of the selected metering node is obtained, and the metering data of other metering nodes of the same type are predicted. The prediction method is as follows:

[0115] For the selected metering node, the instantaneous load of the selected metering node is obtained, and its ratio to the instantaneous load at the previous moment is calculated. For metering nodes of the same type, the instantaneous load at the previous moment is obtained, and its product with the ratio is calculated to obtain the predicted instantaneous load of the metering node.

[0116] Figure 5 This is a fourth sub-flow chart of the power load forecasting method based on big data. The steps of synchronously verifying the error rate of the previous forecasting process and updating the node conditions based on the error rate include:

[0117] Step S501: Read the calculated ratio, and calculate the absolute value of the difference between the ratio and 1 as the error rate;

[0118] Step S502: determining a scaling ratio according to the direct proportion of the error rate, multiplying the scaling ratio by the quantity threshold and rounding the result to an integer, to be used as the updated node condition.

[0119] In an example of the technical solution of the present invention, the calculated ratio is read, the absolute value of the difference between the ratio and one is calculated, and the difference is converted into a percentage as the error rate; the scaling ratio is determined by the error rate, and the larger the error rate, the larger the scaling ratio. The updated quantity threshold can be obtained by multiplying the scaling ratio by the quantity threshold and rounding. The larger the error rate, the larger the quantity threshold, and the more metering nodes are selected.

[0120] It should be noted that the range of the scaling ratio can be pre-set by the management, for example, 0 to 3. At this time, a constant is determined based on this range, and the error rate is converted to the range of 0 to 3 by the constant. The rounding process is generally rounded up.

[0121] Figure 6 : This is a structural block diagram of a power load forecasting system based on big data. In an embodiment of the present invention, a power load forecasting system based on big data, the system 10 includes:

[0122] The node configuration module 11 is used to obtain a power grid model, configure metering nodes based on the power grid model, and number them;

[0123] The instantaneous load acquisition module 12 is used to obtain the power load based on the metering node and synchronously calculate the instantaneous load;

[0124] The node clustering module 13 is used to count the instantaneous load within a preset time period, compare the counted instantaneous load, and cluster the metering nodes;

[0125] The node selection application module 14 is configured to, upon receiving a prediction instruction, randomly select a metering node until the selected metering node meets a preset node condition, obtain the instantaneous load of the selected metering node, and predict the instantaneous load of all metering nodes based on the clustering result;

[0126] A node condition updating module 15 is used to synchronously verify the error rate of the previous prediction process and update the node condition based on the error rate;

[0127] The node condition is a quantity threshold. When the number of any type of metering nodes among the selected metering nodes is greater than the quantity threshold, the node condition is considered to be met.

[0128] Furthermore, the instantaneous load acquisition module 12 includes:

[0129] The power load acquisition unit is used to obtain the power load of each metering node according to a preset frequency timing;

[0130] A spectrum identification unit is used to sort the acquired power loads based on time, and perform discrete Fourier transform on the sorted power loads to determine frequency distribution characteristics;

[0131] A reference function determination unit is used to select a preset number of frequencies from the frequency distribution characteristics and create a frequency-based trigonometric function sum as a reference function for the fitting process;

[0132] The power load fitting unit is used to fit the sorted power loads using the benchmark function to obtain a fitting function for each metering node;

[0133] a fitting function derivation unit, configured to derive the fitting function to obtain a load derivative function, and determine the instantaneous load at each moment based on the load derivative function;

[0134] The benchmark function is: Where n is the preset number; a0, a n and b n are all randomly determined constants, w nIt is the nth frequency value after sorting the N selected frequencies.

[0135] Specifically, the node clustering module 13 includes:

[0136] A derivative function interception unit is used to receive a time period input by a management party and intercept the load derivative function within the time period;

[0137] The node distance calculation unit is used to calculate the difference function of the load derivative function of any two metering nodes, and calculate the integral of the difference function over the time period as the node distance;

[0138] A clustering execution unit is configured to cluster the metering nodes based on the node distances.

[0139] Furthermore, the node selection application module 14 includes:

[0140] A category query unit is used to randomly select a metering node and obtain the category to which it belongs when receiving a prediction instruction;

[0141] a quantity determination unit, configured to calculate the number of metering nodes of each category, and continue selecting when the number does not exceed a preset number threshold, and stop selecting when the number exceeds the preset number threshold;

[0142] A ratio calculation unit, used to obtain the instantaneous load of the selected metering node and calculate the ratio of the instantaneous load to the instantaneous load at the previous moment;

[0143] The comparison prediction unit is used to calculate the product of the instantaneous load at the previous moment and the ratio for other metering nodes of the same category as the predicted instantaneous load.

[0144] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for power load forecasting based on big data, characterized in that: The method comprises: Obtain the power grid model, configure the metering nodes based on the power grid model and number them; The power load is obtained based on the metering node, and the instantaneous load is calculated simultaneously. The calculation process of the instantaneous load includes: After obtaining the power load, sorting it according to time sequence to obtain a discrete data group, obtaining the frequency distribution in the discrete data group, creating a trigonometric function sum form from the frequency, using the form as a reference function for the fitting process, fitting the discrete data group with the reference function to obtain a fitting function, derivatizing the fitting function to obtain a load derivative function, and determining the instantaneous load at each moment based on the load derivative function; Count the instantaneous load within a preset time period, compare the counted instantaneous load, and cluster the metering nodes; When a prediction instruction is received, a metering node is randomly selected and the category to which it belongs is obtained; Calculate the number of metering nodes of each category, and continue selecting when the number does not exceed a preset number threshold; and stop selecting when the number exceeds the preset number threshold; Obtain the instantaneous load of the selected metering node and calculate its ratio to the instantaneous load at the previous moment; For other metering nodes of the same category, the product of the instantaneous load at the previous moment and the ratio is calculated as the predicted instantaneous load; Read the calculated ratio, and calculate the absolute value of the difference between the ratio and 1 as the error rate; Determine a scaling ratio according to the direct proportion of the error rate, multiply the scaling ratio by the quantity threshold and round the result to an integer, and use the result as the updated node condition; The node condition is a quantity threshold. When the number of any type of metering nodes among the selected metering nodes is greater than the quantity threshold, the node condition is considered to be met.

2. The power load forecasting method based on big data according to claim 1, characterized in that: The step of obtaining the power load based on the metering node and synchronously calculating the instantaneous load includes: For each metering node, the power load is obtained regularly according to the preset frequency; Sorting the acquired power loads based on time, and performing discrete Fourier transform on the sorted power loads to determine frequency distribution characteristics; Selecting a preset number of frequencies from the frequency distribution characteristics, and creating a frequency-based trigonometric function sum as a benchmark function for the fitting process; The benchmark function is used to fit the sorted power load to obtain the fitting function of each metering node; Derivative the fitting function to obtain a load derivative function, and determine the instantaneous load at each moment based on the load derivative function; The benchmark function is: Where N is the preset number; a0, a n and b n are all randomly determined constants, w n It is the nth frequency value after sorting the N selected frequencies.

3. The power load forecasting method based on big data according to claim 2, characterized in that: The step of counting instantaneous loads within a preset time period, comparing the counted instantaneous loads, and clustering the metering nodes includes: Receive the time period input by the management party and intercept the load derivative function within the time period; For any two metering nodes, calculate the difference function of the load derivative function and calculate the integral of the difference function over the time period as the node distance; The metering nodes are clustered based on the node distances.

4. The power load forecasting method based on big data according to claim 3 is characterized in that: The step of clustering the metering nodes based on the node distances comprises: Receive the number of categories entered by the management party; Randomly select the number of metering nodes in the metering nodes as the category center; For any metering node, it is classified according to its distance from the center of each category; After all metering nodes are classified once, the category center is updated; The loop is executed until a preset exit condition is reached; the exit condition includes that the number of iterations reaches a threshold and the update amount of the category center is less than a preset update amount threshold; For any metering node, the rule for classifying it according to its distance from the center of each category is: label i =argmin 1≤j≤k ||x i -μ j ||;label i is the classification result of the i-th metering node, x i represents the i-th metering node, μ j represents the jth category center, and k represents the total number of category centers; The updated category center is the center of the same type of metering nodes in space.

5. A power load forecasting system based on big data, characterized in that: The system comprises: Node configuration module, used to obtain the power grid model, configure metering nodes based on the power grid model and number them; The instantaneous load acquisition module is used to obtain the power load based on the metering node and simultaneously calculate the instantaneous load. The calculation process of the instantaneous load includes: After obtaining the power load, sorting it according to time sequence to obtain a discrete data group, obtaining the frequency distribution in the discrete data group, creating a trigonometric function sum form from the frequency, using the form as a reference function for the fitting process, fitting the discrete data group with the reference function to obtain a fitting function, derivatizing the fitting function to obtain a load derivative function, and determining the instantaneous load at each moment based on the load derivative function; The node clustering module is used to count the instantaneous load within a preset time period, compare the counted instantaneous load, and cluster the metering nodes; A category query unit is used to randomly select a metering node and obtain the category to which it belongs when receiving a prediction instruction; a quantity determination unit, configured to calculate the number of metering nodes of each category, and continue selecting when the number does not exceed a preset number threshold, and stop selecting when the number exceeds the preset number threshold; A ratio calculation unit, used to obtain the instantaneous load of the selected metering node and calculate the ratio of the instantaneous load to the instantaneous load at the previous moment; The comparison prediction unit is used to calculate the product of the instantaneous load at the previous moment and the ratio for other metering nodes of the same category as the predicted instantaneous load; A node condition update module is used to synchronously verify the error rate of the previous prediction process and update the node condition based on the error rate; it includes: Read the calculated ratio, and calculate the absolute value of the difference between the ratio and 1 as the error rate; Determine a scaling ratio according to the direct proportion of the error rate, multiply the scaling ratio by the quantity threshold and round the result to an integer, and use the result as the updated node condition; The node condition is a quantity threshold. When the number of any type of metering nodes among the selected metering nodes is greater than the quantity threshold, the node condition is considered to be met.

6. The power load forecasting system based on big data according to claim 5, characterized in that: The instantaneous load acquisition module includes: The power load acquisition unit is used to obtain the power load of each metering node according to a preset frequency timing; A spectrum identification unit is used to sort the acquired power loads based on time, and perform discrete Fourier transform on the sorted power loads to determine frequency distribution characteristics; A reference function determination unit is used to select a preset number of frequencies from the frequency distribution characteristics and create a frequency-based trigonometric function sum as a reference function for the fitting process; The power load fitting unit is used to fit the sorted power loads using the benchmark function to obtain a fitting function for each metering node; a fitting function derivation unit, configured to derive the fitting function to obtain a load derivative function, and determine the instantaneous load at each moment based on the load derivative function; The benchmark function is: Where N is the preset number; a0, a n and b n are all randomly determined constants, w n It is the nth frequency value after sorting the N selected frequencies.

7. The power load forecasting system based on big data according to claim 6, characterized in that: The node clustering module includes: A derivative function interception unit is used to receive a time period input by a management party and intercept the load derivative function within the time period; The node distance calculation unit is used to calculate the difference function of the load derivative function of any two metering nodes, and calculate the integral of the difference function over the time period as the node distance; A clustering execution unit is configured to cluster the metering nodes based on the node distances.

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