Power load component index analysis method, device, computer equipment and medium

Abstract

The present application relates to an analysis method, device, computer equipment and medium for an electric load component index. The method comprises: according to a preset screening rule, determining a preset number of electric power users from the area to be analyzed as sample objects; according to the electric power index coefficient corresponding to each sample object and the daily electric power consumption of each sample object, obtaining the total electric power consumption of the area to be analyzed and the daily electric power consumption change rate of the area to be analyzed; according to a preset proportional coefficient, summing the total electric power consumption of the day and the daily electric power consumption change rate to obtain the electric power load comprehensive variable value of the area to be analyzed. The use of this method can improve the accuracy of the analysis results of the electric power load.

Description

Technical Field

[0001] The present application relates to the technical field of power user service management, and in particular to a method, device, computer equipment and medium for analyzing a power load component index. Background Art

[0002] Electricity is an important driving force for modern cities, and electricity data is also an important indicator of urban economic development. However, the existing research on power load characteristics is not closely related to the actual changes in users' production behavior. Specifically, the random load fluctuations under the disturbance of objective factors such as inherent fluctuations, weather, and whether it is a working day have not been identified and distinguished. In addition, while my country's economic aggregate has risen rapidly, the problem of unbalanced development has become more and more prominent, and profit distribution is seriously unbalanced in different industries and enterprises of different sizes. The existing research on power load characteristics is mainly aimed at analyzing the electricity consumption of the whole society or the electricity consumption of certain industries. The sample objects of the research are not representative, resulting in the problem of inaccurate analysis results of power load. Summary of the invention

[0003] Based on this, it is necessary to provide an analysis method, device, computer equipment and medium for an electric load component index that can improve the accuracy of the analysis results of the electric load in response to the above-mentioned technical problems.

[0004] A method for analyzing an electric power load component index, the method comprising:

[0005] According to the preset screening rules, a preset number of power users are determined from the area to be analyzed as sample objects;

[0006] According to the electricity index coefficient corresponding to each of the sample objects and the daily electricity consumption of each of the sample objects, the total daily electricity consumption of the area to be analyzed and the daily electricity consumption change rate of the area to be analyzed are obtained;

[0007] According to a preset proportionality coefficient, the total electricity consumption on the day and the rate of change of the electricity consumption on the day are summed to obtain the comprehensive variable value of the power load in the area to be analyzed.

[0008] In one embodiment, obtaining the total daily electricity consumption of the area to be analyzed according to the electricity index coefficient corresponding to each sample object and the daily electricity consumption of each sample object includes:

[0009] Select any day in a year as the base date, and take the weighted sum of the base date as the base value;

[0010] Determining the electricity consumption of each of the sample objects on the base day;

[0011] The daily power load component index value of the area to be analyzed is obtained according to the reference value, the power index coefficient corresponding to each of the sample objects, the daily power consumption of each of the sample objects and the power consumption of each of the sample objects on the reference day.

[0012] In one embodiment, obtaining the daily power consumption change rate of the area to be analyzed according to the power index coefficient corresponding to each sample object and the daily power consumption of each sample object includes:

[0013] The daily electricity consumption change rate of the area to be analyzed is obtained according to the reference value, the electricity index coefficient corresponding to each of the sample objects, the daily electricity consumption of each of the sample objects and the ratio of the electricity consumption of each of the sample objects on the reference day.

[0014] In one embodiment, the method further comprises:

[0015] According to the economic correlation coefficient of each of the sample objects, a first electricity index coefficient corresponding to each of the sample objects is obtained;

[0016] According to the business expansion potential coefficient of each of the sample objects, a second power index coefficient corresponding to each of the sample objects is obtained;

[0017] The first power index coefficient is multiplied by the second power index coefficient to obtain the power index coefficient corresponding to each of the sample objects.

[0018] In one embodiment, the method further comprises:

[0019] Using gross domestic product as a benchmark indicator;

[0020] The time difference correlation coefficient between the benchmark index and the electricity consumption index of each of the sample objects is determined as the economic correlation coefficient of each of the sample objects.

[0021] In one embodiment, the method further comprises:

[0022] According to the annualized business expansion ratio of each of the sample objects and the capacity saturation of each of the sample objects, the business expansion potential coefficient of each of the sample objects is obtained.

[0023] In one embodiment, the preset screening rules include sampling space, sampling criteria and sampling method.

[0024] An analysis device for an electric load component index, the device comprising:

[0025] A first determination module is used to determine a preset number of power users from the area to be analyzed as sample objects according to a preset screening rule;

[0026] A first acquisition module is used to obtain the total daily electricity consumption of the area to be analyzed and the daily electricity consumption change rate of the area to be analyzed according to the electricity index coefficient corresponding to each sample object and the daily electricity consumption of each sample object;

[0027] The second acquisition module is used to sum the total electricity consumption on the day and the rate of change of the electricity consumption on the day according to a preset proportionality coefficient to obtain the comprehensive variable value of the power load in the area to be analyzed.

[0028] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0029] According to the preset screening rules, a preset number of power users are determined from the area to be analyzed as sample objects;

[0030] According to the electricity index coefficient corresponding to each of the sample objects and the daily electricity consumption of each of the sample objects, the total daily electricity consumption of the area to be analyzed and the daily electricity consumption change rate of the area to be analyzed are obtained;

[0031] According to a preset proportionality coefficient, the total electricity consumption on the day and the rate of change of the electricity consumption on the day are summed to obtain the comprehensive variable value of the power load in the area to be analyzed.

[0032] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0033] According to the preset screening rules, a preset number of power users are determined from the area to be analyzed as sample objects;

[0034] According to the electricity index coefficient corresponding to each of the sample objects and the daily electricity consumption of each of the sample objects, the total daily electricity consumption of the area to be analyzed and the daily electricity consumption change rate of the area to be analyzed are obtained;

[0035] According to a preset proportionality coefficient, the total electricity consumption on the day and the rate of change of the electricity consumption on the day are summed to obtain the comprehensive variable value of the power load in the area to be analyzed.

[0036] The above-mentioned analysis method, device, computer equipment and medium of the power load component index, because a preset number of power users are determined from the area to be analyzed as sample objects according to the preset screening rules, the sample objects screened and determined according to the preset screening rules have high representativeness, so that the total daily power consumption of the area to be analyzed and the daily power consumption rate of the area to be analyzed can be obtained according to the power index coefficient corresponding to the determined sample objects with high representativeness and the daily power consumption of each sample object, and then the total daily power consumption and the daily power consumption rate can be summed according to the preset proportional coefficient to accurately obtain the comprehensive variable value of the power load in the area to be analyzed, thereby improving the accuracy of the analysis results of the power load in the area to be analyzed. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A diagram of an application environment of an analysis method of a power load component index in one embodiment;

[0038] Figure 2 A schematic diagram of a flow chart of a method for analyzing a power load component index in one embodiment;

[0039] Figure 3 is a flow chart of a method for analyzing a power load component index in another embodiment;

[0040] Figure 4 is a flow chart of a method for analyzing a power load component index in another embodiment;

[0041] Figure 5 is a flow chart of a method for analyzing a power load component index in another embodiment;

[0042] Figure 6 The figure is a structural block diagram of a device for analyzing a power load component index in one embodiment. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0044] The analysis method of the power load component index provided in the embodiment of the present application can be applied to Figure 1The computer device shown. The computer device includes a processor and a memory connected by a system bus, and a computer program is stored in the memory. When the processor executes the computer program, the steps of the following method embodiment can be executed. Optionally, the computer device may also include a network interface, a display screen and an input device. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory, and the non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. Optionally, the computer device can be a server, a personal computer, a personal digital assistant, or other terminal devices, such as a tablet computer, a mobile phone, etc., or a cloud or remote server. The embodiment of the present application does not limit the specific form of the computer device.

[0045] In one embodiment, Figure 2 As shown in the figure, a method for analyzing the power load component index is provided. Figure 1 The computer device in the example is used to illustrate, including the following steps:

[0046] S201, according to a preset screening rule, determining a preset number of power users from the area to be analyzed as sample objects.

[0047] Specifically, the computer device determines a preset number of power users from the area to be analyzed as sample objects according to the preset screening rules. Optionally, the determined sample objects can be one percent of users in each industry. Optionally, the preset screening rules include sampling space, sampling standards and sampling methods. Optionally, the users in the sampling space need to meet the following conditions at the same time: ① have a power consumption time of more than 3 years. If not, select the users whose daily average power consumption ranks in the top 10 of all users since the declaration of installation of the meter; ② there are no or only a few obvious abnormal fluctuations or statistical errors in the historical power consumption; ③ there is no long-term no-load record. Optionally, the sampling standard includes selecting industry users with large power consumption, high economic relevance, high status and importance, and representative in the industry as sample users. Optionally, the sampling method includes: ① sorting the total power consumption of the sample space users in the previous year in descending order, and eliminating users who rank outside a certain number of places according to the difference in power consumption; ② selecting users with higher social importance; ③ selecting users with higher power consumption in each industry according to the national economic industry classification and relative proportion to make up the sample size until the target sample size N is reached. Optionally, the computer device may select 3 to 4 times the preset number of candidate users as the above sample objects.

[0048] S202, according to the electricity index coefficient corresponding to each sample object and the daily electricity consumption of each sample object, the total daily electricity consumption of the area to be analyzed and the daily electricity consumption change rate of the area to be analyzed are obtained.

[0049] Optionally, the computer device can obtain the total daily electricity consumption of the area to be analyzed based on the product value of the electricity index coefficient corresponding to each sample object and the daily electricity consumption of each sample object. Optionally, the computer device can also obtain the electricity consumption of each sample object on a determined base date, and obtain the daily electricity consumption change rate of the area to be analyzed based on the electricity consumption of each sample object on the base date and the total daily electricity consumption of the area to be analyzed.

[0050] S203, according to a preset proportionality coefficient, summing up the total electricity consumption on the day and the rate of change of electricity consumption on the day to obtain a comprehensive variable value of the power load of the area to be analyzed.

[0051] Optionally, the computer device can assign the same proportional coefficient to the total daily electricity consumption of each sample object and the daily electricity consumption change rate of each sample object, or can assign different proportional coefficients, and then sum the total daily electricity consumption and the daily electricity consumption change rate according to the assigned proportional coefficients to obtain the comprehensive variable value of the power load in the area to be analyzed.

[0052] In the above-mentioned analysis method of the power load component index, a preset number of power users are determined from the area to be analyzed as sample objects according to the preset screening rules. In this way, the sample objects screened and determined according to the preset screening rules have high representativeness. Therefore, the total daily power consumption of the area to be analyzed and the daily power consumption change rate of the area to be analyzed can be obtained according to the power index coefficient corresponding to the determined sample objects with high representativeness and the daily power consumption of each sample object. Then, the total daily power consumption and the daily power consumption change rate can be summed according to the preset proportional coefficient to accurately obtain the comprehensive variable value of the power load in the area to be analyzed, thereby improving the accuracy of the analysis results of the power load in the area to be analyzed.

[0053] In the above scenario where the total daily electricity consumption of the area to be analyzed is obtained based on the electricity index coefficient corresponding to each sample object and the daily electricity consumption of each sample object, in one embodiment, Figure 3 As shown, the above S202 includes:

[0054] S301, select any day in a year as a base date, and use the weighted sum of the base dates as a base value.

[0055] Specifically, the computer device selects any day in a year as the base date, and uses the weighted sum of the base date as the base value. Exemplarily, the computer device may select a day t0 in a year as the base period, and use the weighted sum of the day as the base value, for example, use the weighted sum of 1000 on the day as the base value, or may use other values ​​as the base value, which is not limited in this embodiment.

[0056] S302: Determine the electricity consumption of each sample object on the base day.

[0057] Specifically, the computer device determines the power consumption of each sample object on the determined reference day. Optionally, the computer device may determine the power consumption of each sample object on the reference day based on the daily power consumption statistics of each sample object.

[0058] S303, obtaining the power load component index value of the area to be analyzed on the day according to the reference value, the power index coefficient corresponding to each sample object, the power consumption of each sample object on the day and the power consumption of each sample object on the reference day.

[0059] Specifically, the computer device obtains the power load component index value of the area to be analyzed on the day according to the determined base date, the power index coefficient corresponding to each sample object, the power consumption of each sample object on the day, and the power consumption of each sample object on the above base date. Optionally, the computer device can use the following formula Get the daily power load component index value of the area to be analyzed, where ω p represents the electricity index coefficient corresponding to each sample object, D(t0) represents the electricity consumption of each sample object on the reference day, D(t) represents the electricity consumption of each sample object on the current day, j represents the reference value, and i represents the number of sample objects. Optionally, the value of j in the above formula can be 1000. It should be noted that in order to avoid the influence of working days and rest days, the power load component index value of each sample object on the current day can also be defined as the average value of the actual index on the current day for seven days.

[0060] In this embodiment, the computer device selects any day of the year as a base date, takes the weighted sum of the base date as a base value, and further determines the power consumption of each sample object on the base date. In this way, the computer device can accurately obtain the power load component index value of the day for the area to be analyzed based on the determined base date, the power index coefficient corresponding to each sample object, the power consumption of each sample object on the day, and the power consumption of each sample object on the base date, thereby improving the accuracy of the power load component index value of the day for the area to be analyzed obtained by the computer device.

[0061] In the above scenario where the daily electricity consumption change rate of the area to be analyzed is obtained based on the electricity index coefficient corresponding to each sample object and the daily electricity consumption of each sample object, in one embodiment, the above S202 includes: obtaining the daily electricity consumption change rate of the area to be analyzed based on the benchmark value, the electricity index coefficient corresponding to each sample object, the daily electricity consumption of each sample object and the ratio of the electricity consumption of each sample object on the benchmark day.

[0062] In this embodiment, the computer device sums the above-mentioned reference value, the power index coefficient corresponding to each sample object, and the daily power consumption of each sample object, and divides the summed value by the power consumption of each sample object on the reference day to obtain the daily power consumption change rate of the area to be analyzed. Optionally, the computer device can use the following formula Get the daily electricity consumption change rate of the area to be analyzed, where ω p represents the electricity index coefficient corresponding to each sample object, D(t0) represents the electricity consumption of each sample object on the reference day, D(t) represents the electricity consumption of each sample object on the current day, j represents the reference value, and i represents the number of sample objects. Optionally, the value of j in the above formula can be 1000. It should be noted that in order to avoid the influence of working days and rest days, the daily power load characteristic value of each sample object can also be defined as the average value of the actual index of the current day for seven days.

[0063] In this embodiment, the computer device selects any day of the year as a base date, takes the weighted sum of the base date as a base value, and further determines the power consumption of each sample object on the base date. In this way, the computer device can accurately obtain the daily power consumption change rate of the area to be analyzed based on the determined base date, the power index coefficient corresponding to each sample object, the daily power consumption of each sample object and the power consumption ratio of each sample object on the base date, thereby improving the accuracy of the daily power consumption change rate of the area to be analyzed obtained by the computer device.

[0064] In the above scenario where the total daily electricity consumption of the area to be analyzed and the daily electricity consumption change rate of the area to be analyzed are obtained according to the electricity index coefficient corresponding to each sample object and the daily electricity consumption of each sample object, it is necessary to first determine the electricity index coefficient corresponding to each sample object. Based on the above embodiment, in one embodiment, Figure 4 As shown, the above method also includes:

[0065] S401, obtaining a first electricity index coefficient corresponding to each sample object according to the economic correlation coefficient of each sample object.

[0066] Specifically, the computer device obtains the first power index coefficient corresponding to each sample object according to the economic correlation coefficient of each sample object. Optionally, the computer device may normalize the economic correlation coefficient of each sample object and use the normalized value as the first power index coefficient corresponding to each sample object.

[0067] S402: Obtain a second power index coefficient corresponding to each sample object according to the business expansion potential coefficient of each sample object.

[0068] Specifically, the computer device obtains the second power index coefficient corresponding to each sample object according to the business expansion potential coefficient of each sample object. Optionally, the computer device can normalize the business expansion potential coefficient of each sample object and use the normalized value as the second power index coefficient corresponding to each sample object.

[0069] S403: multiply the first power index coefficient by the second power index coefficient to obtain the power index coefficient corresponding to each sample object.

[0070] Specifically, the computer device multiplies the first power index coefficient obtained above by the second power index coefficient obtained above to obtain the power index coefficient corresponding to each of the sample objects. Optionally, the computer device may directly use the product value of the first power index coefficient and the second power index coefficient as the power index coefficient corresponding to each sample object, or may round the product value of the first power index coefficient and the second power index coefficient and use the rounded product value as the power index coefficient corresponding to each sample object.

[0071] In this embodiment, the computer device can accurately obtain the first electricity index coefficient corresponding to each sample object based on the economic correlation coefficient of each sample object, and can accurately obtain the second electricity index coefficient corresponding to each sample object based on the business expansion potential coefficient of each sample object. The first electricity index coefficient can be multiplied by the second electricity index coefficient to obtain the electricity index coefficient corresponding to each sample object. Since the accuracy of the obtained first electricity index coefficient and the second electricity index coefficient is improved, the accuracy of the obtained electricity index coefficient corresponding to each sample object is also improved.

[0072] In the above scenario where the first electricity index coefficient corresponding to each sample object is obtained according to the economic correlation coefficient of each sample object, the economic correlation coefficient of each sample object needs to be determined first. In one embodiment, Figure 5 As shown, the above method also includes:

[0073] S501, using gross domestic product as the benchmark indicator.

[0074] Specifically, the computer device uses the gross domestic product as a benchmark indicator. For example, the computer device can set the gross domestic product to y = [y1, y2, ..., y n ], and y=[y1,y2,...,y n ] as a benchmark indicator.

[0075] S502: Determine the time difference correlation coefficient between the benchmark index and the electricity consumption index of each sample object as the economic correlation coefficient of each sample object.

[0076] Specifically, the computer device determines the time difference correlation coefficient between the above-mentioned benchmark index and the power consumption index of each sample object as the economic correlation coefficient of each sample object. Exemplarily, the computer device can set the power consumption index of each sample object to x = [x1, x2, ..., x n ]. Optionally, the computer device may determine the economic relevance coefficient of each sample object by the following formula:

[0077]

[0078] In the formula, r l Represents the economic correlation coefficient of each sample object, l = 0, ±1, ±2, ..., ±L; l represents the number of periods of advance or lag, negative values ​​represent advance, positive values ​​represent lag; L is the maximum delay number, which is the number of data after the data is aligned, x t+l Indicates the power consumption index of each sample object, represents the average value of the electricity consumption index of each sample object, y t Represents the benchmark indicator, Represents the average value of the benchmark metric.

[0079] In this embodiment, the computer device uses the gross domestic product as a benchmark indicator, and the process of determining the time difference correlation coefficient between the benchmark indicator and the electricity consumption indicator of each sample object as the economic correlation coefficient of each sample object is very simple. In this way, the economic correlation coefficient of each sample object can be quickly determined, which improves the efficiency of determining the economic correlation coefficient of each sample object. In addition, the gross domestic product can reflect the strength of the correlation between the changes in electricity consumption in different industries and the economy. This process is relatively simple, which improves the efficiency of obtaining the strength of the correlation between the changes in electricity consumption in different industries and the economy.

[0080] In the above scenario where the second electricity index coefficient corresponding to each sample object is obtained based on the business expansion potential coefficient of each sample object, it is necessary to first determine the business expansion potential coefficient of each sample object. In one embodiment, the above method also includes: obtaining the business expansion potential coefficient of each sample object based on the annualized business expansion ratio of each sample object and the capacity saturation of each sample object.

[0081] Specifically, the computer device obtains the business expansion potential coefficient of each sample object based on the annualized business expansion ratio of each sample object and the capacity saturation of each sample object. It can be understood that the business expansion potential of a user is directly related to the development process of the user. Compared with users who have reached a stable stage of development, the increasingly prosperous industry is a potential stock of the economic trend and should be analyzed emphatically. In this regard, the annualized business expansion ratio and capacity saturation are proposed as two indicators to judge the business expansion potential of the user. Among them, the annualized business expansion ratio of each sample object can be obtained by the following formula: The capacity saturation of each sample object can be obtained by the following formula: Among them, the service life is rounded up; the capacity utilization ratio is obtained by the following graded method:

[0082] Actual proportion % <50 [50,60] (60,70] (70,80] >80 Calculate the ratio % 0 60 70 80 100

[0083] Combining two indicators: k = annualized business expansion ratio × capacity saturation, we can get the business expansion potential coefficient of each sample object, where k represents the business expansion potential coefficient of each sample object.

[0084] In this embodiment, the computer device can quickly obtain the business expansion potential coefficient of each sample object according to the annualized business expansion ratio of each sample object and the capacity saturation of each sample object, thereby improving the efficiency of obtaining the business expansion potential coefficient of each sample object.

[0085] It should be understood that although Figure 2-5 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 2-5 At least part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

[0086] In one embodiment, Figure 6 As shown, a device for analyzing a power load component index is provided, comprising: a first determination module, a first acquisition module and a second acquisition module, wherein:

[0087] The first determination module is used to determine a preset number of power users from the area to be analyzed as sample objects according to a preset screening rule.

[0088] The first acquisition module is used to obtain the total daily electricity consumption of the area to be analyzed and the daily electricity consumption change rate of the area to be analyzed according to the electricity index coefficient corresponding to each sample object and the daily electricity consumption of each sample object.

[0089] The second acquisition module is used to sum the total electricity consumption on the day and the rate of change of the electricity consumption on the day according to a preset proportionality coefficient to obtain the comprehensive variable value of the power load in the area to be analyzed.

[0090] Optionally, the preset screening rules include sampling space, sampling criteria and sampling method.

[0091] The power load component index analysis device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0092] On the basis of the above embodiment, optionally, the above first acquisition module includes: a selection unit, a determination unit and a first acquisition unit, wherein:

[0093] The selection unit is used to select any day in a year as the base date, and use the weighted sum of the base dates as the base value.

[0094] The determination unit is used to determine the electricity consumption of each sample object on the base day.

[0095] The first acquisition unit is used to obtain the daily power load component index value of the area to be analyzed according to the reference value, the power index coefficient corresponding to each sample object, the daily power consumption of each sample object and the power consumption of each sample object on the reference day.

[0096] The power load component index analysis device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0097] Based on the above embodiment, optionally, the above first acquisition module includes: a second acquisition unit, wherein:

[0098] The second acquisition unit is used to obtain the daily electricity consumption change rate of the area to be analyzed according to the reference value, the electricity index coefficient corresponding to each sample object, the daily electricity consumption of each sample object and the ratio of the electricity consumption of each sample object on the reference day.

[0099] The power load component index analysis device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0100] On the basis of the above embodiment, optionally, the above device further includes: a third acquisition module, a fourth acquisition module and a fifth acquisition module, wherein:

[0101] The third acquisition module is used to obtain the first electricity index coefficient corresponding to each sample object according to the economic correlation coefficient of each sample object.

[0102] The fourth acquisition module is used to obtain the second power index coefficient corresponding to each sample object according to the business expansion potential coefficient of each sample object.

[0103] The fifth acquisition module is used to multiply the first power index coefficient by the second power index coefficient to obtain the power index coefficient corresponding to each sample object.

[0104] The power load component index analysis device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0105] Based on the above embodiment, optionally, the above device further includes: a second determination module and a third determination module, wherein:

[0106] The second determination module is used to take the gross domestic product as a benchmark indicator.

[0107] The third determination module is used to determine the time difference correlation coefficient between the benchmark index and the electricity consumption index of each sample object as the economic correlation coefficient of each sample object.

[0108] The power load component index analysis device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0109] Based on the above embodiment, optionally, the above device further includes: a sixth acquisition module, wherein:

[0110] The sixth acquisition module is used to obtain the business expansion potential coefficient of each sample object according to the annualized business expansion ratio of each sample object and the capacity saturation of each sample object.

[0111] The power load component index analysis device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0112] For the specific definition of the analysis device of the power load component index, please refer to the definition of the analysis method of the power load component index in the above text, which will not be repeated here. Each module in the above-mentioned analysis device of the power load component index can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0113] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0114] According to the preset screening rules, a preset number of power users are determined from the area to be analyzed as sample objects;

[0115] According to the electricity index coefficient corresponding to each sample object and the daily electricity consumption of each sample object, the total daily electricity consumption of the area to be analyzed and the daily electricity consumption change rate of the area to be analyzed are obtained;

[0116] According to the preset proportional coefficient, the total electricity consumption on the day and the rate of change of electricity consumption on the day are summed to obtain the comprehensive variable value of the power load in the area to be analyzed.

[0117] The implementation principle and technical effect of the computer device provided in the above embodiment are similar to those of the above method embodiment, and will not be repeated here.

[0118] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0119] According to the preset screening rules, a preset number of power users are determined from the area to be analyzed as sample objects;

[0120] According to the electricity index coefficient corresponding to each sample object and the daily electricity consumption of each sample object, the total daily electricity consumption of the area to be analyzed and the daily electricity consumption change rate of the area to be analyzed are obtained;

[0121] According to the preset proportional coefficient, the total electricity consumption on the day and the rate of change of electricity consumption on the day are summed to obtain the comprehensive variable value of the power load in the area to be analyzed.

[0122] The computer-readable storage medium provided in the above embodiment has similar implementation principles and technical effects as those of the above method embodiment, and will not be described in detail here.

[0123] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0124] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0125] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for analyzing a power load component index, characterized in that: The method comprises: According to the preset screening rules, a preset number of power users are determined from the area to be analyzed as sample objects; the preset screening rules include sampling space, sampling criteria and sampling method; According to the electricity index coefficient corresponding to each of the sample objects and the daily electricity consumption of each of the sample objects, the total daily electricity consumption of the area to be analyzed and the daily electricity consumption change rate of the area to be analyzed are obtained; According to a preset proportionality coefficient, the total amount of electricity consumption on the day and the rate of change of electricity consumption on the day are summed to obtain a comprehensive variable value of the power load of the area to be analyzed; The gross domestic product is used as a benchmark indicator; the time difference correlation coefficient between the benchmark indicator and the electricity consumption indicator of each sample object is determined as the economic correlation coefficient of each sample object; according to the economic correlation coefficient of each sample object, the first electricity index coefficient corresponding to each sample object is obtained; According to the annualized business expansion ratio of each of the sample objects and the capacity saturation of each of the sample objects, a business expansion potential coefficient of each of the sample objects is obtained; according to the business expansion potential coefficient of each of the sample objects, a second power index coefficient corresponding to each of the sample objects is obtained; The first power index coefficient is multiplied by the second power index coefficient to obtain the power index coefficient corresponding to each of the sample objects.

2. The method according to claim 1, characterized in that According to the electricity index coefficient corresponding to each of the sample objects and the daily electricity consumption of each of the sample objects, the total daily electricity consumption of the area to be analyzed is obtained, including: Select any day in a year as the base date, and take the weighted sum of the base date as the base value; Determining the electricity consumption of each of the sample objects on the base day; The daily power load component index value of the area to be analyzed is obtained according to the reference value, the power index coefficient corresponding to each of the sample objects, the daily power consumption of each of the sample objects and the power consumption of each of the sample objects on the reference day.

3. The method according to claim 2, characterized in that According to the power index coefficient corresponding to each of the sample objects and the power consumption of each of the sample objects on the same day, the power consumption change rate of the area to be analyzed on the same day is obtained, including: The daily electricity consumption change rate of the area to be analyzed is obtained according to the reference value, the electricity index coefficient corresponding to each of the sample objects, the daily electricity consumption of each of the sample objects and the ratio of the electricity consumption of each of the sample objects on the reference day.

4. The method according to claim 1, characterized in that: According to the electricity index coefficient corresponding to each of the sample objects and the daily electricity consumption of each of the sample objects, the total daily electricity consumption of the area to be analyzed is obtained, including: The total daily electricity consumption of the area to be analyzed is obtained according to the product value of the electricity index system corresponding to each of the sample objects and the daily electricity consumption of each of the sample objects.

5. The method according to claim 1, characterized in that: According to the power index coefficient corresponding to each of the sample objects and the daily power consumption of each of the sample objects, the daily power consumption change rate of the area to be analyzed is obtained, including: Obtaining the electricity consumption of each of the sample objects on a determined base date; The daily electricity consumption change rate of the area to be analyzed is obtained according to the electricity consumption of each sample object on the reference day and the total electricity consumption of the area to be analyzed on that day.

6. An analysis device for power load component index, characterized in that: The device comprises: The first determination module is used to determine a preset number of power users from the area to be analyzed as sample objects according to a preset screening rule; the preset screening rule includes a sampling space, a sampling standard and a sampling method; A first acquisition module is used to obtain the total daily electricity consumption of the area to be analyzed and the daily electricity consumption change rate of the area to be analyzed according to the electricity index coefficient corresponding to each sample object and the daily electricity consumption of each sample object; A second acquisition module is used to sum the total power consumption on the day and the power consumption change rate on the day according to a preset proportional coefficient to obtain a comprehensive variable value of the power load of the area to be analyzed; The second determination module is used to use the gross domestic product as a benchmark indicator; the third determination module is used to determine the time difference correlation coefficient between the benchmark indicator and the electricity consumption indicator of each sample object as the economic correlation coefficient of each sample object; the third acquisition module is used to obtain the first electricity index coefficient corresponding to each sample object according to the economic correlation coefficient of each sample object; The sixth acquisition module is used to obtain the business expansion potential coefficient of each of the sample objects according to the annualized business expansion ratio of each of the sample objects and the capacity saturation of each of the sample objects; the fourth acquisition module is used to obtain the second power index coefficient corresponding to each of the sample objects according to the business expansion potential coefficient of each of the sample objects; The fifth acquisition module is used to multiply the first power index coefficient by the second power index coefficient to obtain the power index coefficient corresponding to each of the sample objects.

7. The device according to claim 6, characterized in that The first acquisition module includes a selection unit, a determination unit and a first acquisition unit: A selection unit, used to select any day in a year as a base date, and use the weighted sum of the base dates as a base value; A determination unit, configured to determine the power consumption of each of the sample objects on the reference day; The first acquisition unit is used to obtain the daily power load component index value of the area to be analyzed based on the benchmark value, the power index coefficient corresponding to each of the sample objects, the daily power consumption of each of the sample objects, and the power consumption of each of the sample objects on the benchmark day.

8. The device according to claim 7, characterized in that The first acquisition module includes a second acquisition unit; The second acquisition unit is used to obtain the daily electricity consumption change rate of the area to be analyzed based on the benchmark value, the electricity index coefficient corresponding to each sample object, the daily electricity consumption of each sample object and the ratio of the electricity consumption of each sample object on the benchmark day.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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