Bus load air temperature sensitivity analysis method and system based on interval classification

Abstract

The invention relates to the technical field of sensitivity analysis, in particular to a bus load air temperature sensitivity analysis method and system based on interval classification, and the method comprises the steps: determining a clustered air temperature interval; and performing linear regression analysis on the bus load data in each air temperature interval to predict bus load changes under different air temperature conditions. The method has the beneficial effects that the air temperature is divided into a plurality of intervals, and clustering and linear regression analysis are performed on the bus load data in each interval, so that the influence of the air temperature change on the bus load can be more accurately captured, and the accuracy of load prediction is improved. According to the method, the understanding of a power load change rule is enhanced, a more refined load management strategy is provided for a power system, power resource distribution is optimized, the operation cost of a power grid is reduced, and the stability and the reliability of the power grid are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of sensitivity analysis, and in particular to a bus load temperature sensitivity analysis method and system based on interval classification. Background Art

[0002] With the acceleration of industrialization and urbanization, the demand for electricity is growing, and load management of power systems is becoming increasingly important. Temperature is one of the important factors affecting power load, especially in tropical and subtropical areas such as Hainan, where temperature changes have a significant impact on residential and commercial power loads. In these areas, the use of temperature-sensitive appliances such as air conditioners increases with rising temperatures, leading to peaks and fluctuations in power load. Therefore, accurately analyzing and predicting the impact of temperature changes on bus loads is crucial for the stable operation of the power grid and the rational allocation of power resources.

[0003] Traditional load forecasting methods often use a single temperature range to analyze the entire load data. This method ignores the differences in the sensitivity of the load to temperature changes in different temperature ranges. For example, the lowest cooling temperature of residential air conditioners is 16 degrees, the most suitable temperature for the human body is 22 degrees, and the human body generally starts to feel hot and sweat above 30 degrees. This means that in different temperature ranges, the use of air conditioners and the law of load changes are different. Therefore, the traditional single temperature range analysis method cannot accurately capture the subtle effects of temperature changes on the load, resulting in insufficient accuracy of the prediction results. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a bus load temperature sensitivity analysis method and system based on interval classification, which can solve the problems mentioned in the background technology.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a bus load temperature sensitivity analysis method based on interval classification, comprising determining clustered temperature intervals;

[0009] Linear regression analysis is performed on the bus load data in each temperature range to predict the bus load changes under different temperature conditions.

[0010] As a preferred solution of the bus load temperature sensitivity analysis method based on interval classification of the present invention, wherein: the clustered temperature interval is determined, including

[0011] Divide according to the distribution of historical temperature data and actual analysis needs;

[0012] The temperature is divided into at least three ranges, including low temperature range, medium temperature range and high temperature range.

[0013] As a preferred solution of the bus load temperature sensitivity analysis method based on interval classification of the present invention, wherein: determining the clustered temperature interval also includes

[0014] Draw a scatter plot based on the data and observe the distribution shape of the scatter plot to divide the linear temperature range.

[0015] As a preferred solution of the bus load temperature sensitivity analysis method based on interval classification of the present invention, a linear regression analysis is performed on the bus load data in each temperature interval, including:

[0016] Collect historical data series of bus load and temperature within a preset time period;

[0017] Clean the collected historical data series of bus load and temperature to remove abnormal values;

[0018] Use the linear regression tool in statistical software or programming language to fit the regression model and obtain the regression equation.

[0019] As a preferred solution of the bus load temperature sensitivity analysis method based on interval classification of the present invention, wherein: predicting bus load changes under different temperature conditions includes:

[0020] According to the regression equation for each temperature interval;

[0021] The bus load corresponding to each temperature is predicted.

[0022] As a preferred solution of the bus load air temperature sensitivity analysis method based on interval classification of the present invention, wherein: in the regression equation, the bus load is the dependent variable and the temperature is the independent variable.

[0023] As a preferred solution of the bus load air temperature sensitivity analysis method based on interval classification of the present invention, the method further comprises determining the parameters in the regression equation by adopting the least square method.

[0024] In a second aspect, the present invention provides a bus load temperature sensitivity analysis method based on interval classification, comprising: a determination module for determining a clustered temperature interval;

[0025] The analysis and prediction module is used to perform linear regression analysis on the bus load data in each temperature range to predict the bus load changes under different temperature conditions.

[0026] In a third aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0027] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when the computer program is executed by a processor.

[0028] Compared with the prior art, the present invention has the following beneficial effects: by dividing the temperature into multiple intervals and performing clustering and linear regression analysis on the bus load data in each interval, the impact of temperature changes on bus loads can be captured more accurately, thereby improving the accuracy of load forecasting. This method not only enhances the understanding of the law of power load changes, but also provides a more refined load management strategy for the power system, which helps to optimize the allocation of power resources, reduce the operating costs of the power grid, and improve the stability and reliability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0030] Figure 1 The flowchart of bus load temperature sensitivity analysis method based on interval classification is shown in FIG.

[0031] Figure 2 A schematic diagram of the internal structure of a computer device. DETAILED DESCRIPTION

[0032] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0033] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0034] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0035] Example 1

[0036] Reference Figure 1 , which is the first embodiment of the present invention, provides a bus load temperature sensitivity analysis method based on interval classification, which includes:

[0037] S1. Determine the temperature range for clustering.

[0038] Furthermore, the temperature range of the cluster is determined, including

[0039] Divide according to the distribution of historical temperature data and actual analysis needs;

[0040] The temperature is divided into at least three ranges, including low temperature range, medium temperature range and high temperature range.

[0041] It should be noted that it can be divided according to the seasonal average temperature or at equal intervals. It can further be divided into a low temperature range below 16°, a high temperature range above 30°, and a medium temperature range between the two.

[0042] Furthermore, the temperature range of the cluster is determined, including

[0043] Draw a scatter plot based on the data and observe the distribution shape of the scatter plot to divide the linear temperature range.

[0044] It should be noted that the scatter plot is drawn with temperature as the horizontal axis and bus load as the vertical axis. By observing the distribution shape of the scatter plot, if the scatter points are roughly distributed near a straight line, then the linear relationship may be more obvious, and it is a temperature interval. In other words, the scatter plot can be divided into several linear temperature intervals.

[0045] It should be further explained that the classification can be carried out in the following three specific ways:

[0046] The first visual observation method: identify the clustering trend and linear characteristics of data points to divide the temperature interval. Specifically, observe the scatter plot to identify whether the data points show a piecewise linear trend composed of straight lines with different slopes. Further analyze the degree of clustering and dispersion of data points, especially in areas where data points are dense and approximately arranged in a straight line, and areas where data points are scattered or curved. These characteristics provide clues for the division of temperature intervals.

[0047] The second slope change detection method is to divide the temperature range boundary by calculating the slope between adjacent data points in the scatter plot and detecting significant changes in the slope. Specifically, the data point (x i ,y i ) and (x i+1 ,y i+1 ), slope Detect the change of slope. When the slope changes significantly, that is, the slope change rate exceeds the threshold, the corresponding value is the boundary of the linear interval. In this way, the boundaries of multiple linear intervals can be determined, which divide the scatter plot into different linear relationship areas;

[0048] The third clustering algorithm method: select a clustering algorithm, such as the K-Means clustering algorithm. Specifically, the x values ​​(independent variables) in the scatter plot are regarded as data points for clustering. For example, for the temperature data (x-axis) in a scatter plot, set the number of clusters to indicate that you want to divide the boundary temperature of three linear intervals. Determine the boundary points based on the clustering results. The clustering algorithm will divide the data points into different clusters, and the boundaries between clusters can be used as the boundaries of the linear intervals. However, this method requires attention to the parameter selection of the clustering algorithm and the verification of the results to ensure the rationality of the division.

[0049] S2. Perform linear regression analysis on the bus load data in each temperature range to predict the bus load changes under different temperature conditions, so as to determine the sensitivity of the bus load to temperature changes.

[0050] It should be noted that temperature sensitivity is defined as the relative change in bus load for every degree increase in ambient temperature.

[0051] Furthermore, a linear regression analysis is performed on the bus load data within each temperature range, including collecting historical data series of bus load and temperature within a preset time period, wherein the preset time period may be one year or multiple years, and further the time resolution of these data may be hours, half hours, etc., i=1,2,...,n;.

[0052] Clean the collected historical data series of bus load and temperature to remove outliers, where outliers refer to obviously unreasonable bus load and temperature data caused by equipment failure or data collection errors;

[0053] Use the linear regression tool in statistical software or programming language to fit the regression model and obtain the regression equation.

[0054] Furthermore, in the regression equation, bus load is the dependent variable and temperature is the independent variable.

[0055] Furthermore, the method also includes determining the parameters in the regression equation by adopting the least square method.

[0056] It should be noted that within a certain temperature range, there is a linear relationship between bus load (y) and air temperature (x), that is, y = a + bx + ε, where a is the intercept; b is the regression coefficient, which indicates the change in bus load when the air temperature changes by one unit; ε is the error term. The values ​​of a and b are determined by collecting historical bus load data and corresponding temperature data using the least squares method.

[0057] It should be further explained that the least squares method (also known as the least square method) is a mathematical optimization technique. It finds the best function matching data by minimizing the sum of squares of errors. Specifically, given a set of data points (x i ,y i ), find a function y = a + bx such that To reach the minimum. That is, determine the values ​​of parameters a and b so that the residual sum of squares Minimum.

[0058] In order to find the minimum a and b, we take the partial derivatives of a and b respectively and set them equal to 0.

[0059] First find the partial derivative with respect to b make get Further expansion

[0060] When taking partial derivatives with respect to a, make

[0061] By combining these two equations (normal equations), we can solve for the values ​​of a and b.

[0062] The error term ε (for the th observation) is defined as the actual observation y i The predicted value of the regression model The difference between in

[0063] After calculating the predicted values ​​and obtaining a and b, for each x i , calculate the predicted value of the regression model For example, if the fitting results in a=2, b=1, for x=3, then

[0064] Calculate the error term, for each observation y i , calculate the error term For example, the actual observed value y = 8, the predicted value Then the error term ∈ i =8-7=1.

[0065] Finally, the error term for each temperature interval is:

[0066] Furthermore, the bus load changes under different temperature conditions are predicted, including

[0067] According to the regression equation for each temperature interval;

[0068] The bus load corresponding to each temperature is predicted.

[0069] In summary, the beneficial effect of a bus load temperature sensitivity analysis method based on interval classification is that by dividing the temperature into multiple intervals and performing clustering and linear regression analysis on the bus load data in each interval, it can more accurately capture the impact of temperature changes on bus loads, thereby improving the accuracy of load forecasting. This method not only enhances the understanding of the law of power load changes, but also provides a more refined load management strategy for the power system, which helps to optimize the allocation of power resources, reduce the cost of power grid operation, and improve the stability and reliability of the power grid.

[0070] Example 2

[0071] This embodiment provides a bus load temperature sensitivity analysis system based on interval classification, which includes a determination module for determining a clustered temperature interval;

[0072] The analysis and prediction module is used to perform linear regression analysis on the bus load data in each temperature range to predict the bus load changes under different temperature conditions.

[0073] The above-mentioned unit modules may be embedded in or independent of the processor in the computer device in the form of hardware, or may be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

[0074] Example 3

[0075] This embodiment provides a computer device, which may be a terminal, and its internal structure diagram may be as follows: Figure 2As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. 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. 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 communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a bus load temperature sensitivity analysis method based on interval classification is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse.

[0076] This embodiment also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the computer program is implemented to: determine a clustered temperature range;

[0077] Linear regression analysis is performed on the bus load data in each temperature range to predict the bus load changes under different temperature conditions.

[0078] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A bus load temperature sensitivity analysis method based on interval classification, characterized by: include, Determine the temperature range for clustering; Linear regression analysis is performed on the bus load data in each temperature range to predict the bus load changes under different temperature conditions.

2. The bus load temperature sensitivity analysis method based on interval classification according to claim 1 is characterized in that: The temperature interval for clustering is determined, including Divide according to the distribution of historical temperature data and actual analysis needs; The temperature is divided into at least three ranges, including low temperature range, medium temperature range and high temperature range.

3. The bus load temperature sensitivity analysis method based on interval classification according to claim 2 is characterized in that: The temperature range of the cluster is determined, and further includes Draw a scatter plot based on the data and observe the distribution shape of the scatter plot to divide the linear temperature range.

4. The bus load temperature sensitivity analysis method based on interval classification according to claim 3 is characterized in that: The linear regression analysis of the bus load data in each temperature range includes: Collect historical data series of bus load and temperature within a preset time period; Clean the collected historical data series of bus load and temperature to remove abnormal values; Use the linear regression tool in statistical software or programming language to fit the regression model and obtain the regression equation.

5. The bus load temperature sensitivity analysis method based on interval classification according to claim 4 is characterized in that: The prediction of bus load changes under different temperature conditions includes: According to the regression equation for each temperature interval; The bus load corresponding to each temperature is predicted.

6. The bus load temperature sensitivity analysis method based on interval classification according to claim 4 or 5, characterized in that: In the regression equation, bus load is the dependent variable and temperature is the independent variable.

7. The bus load temperature sensitivity analysis method based on interval classification according to claim 6 is characterized in that: The method also includes determining parameters in the regression equation by employing a least squares method.

8. A bus load temperature sensitivity analysis system based on interval classification, characterized in that: include: A determination module is used to determine the temperature range of the cluster; The analysis and prediction module is used to perform linear regression analysis on the bus load data in each temperature range to predict the bus load changes under different temperature conditions.

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 7 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 7 are implemented.

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