Method and system for estimating productivity utilization rate of power consumption enterprise

Abstract

The invention discloses a power utilization enterprise capacity utilization rate estimation method and system, and the method comprises the steps: obtaining the electric quantity data of a power utilization enterprise in a preset time interval, carrying out the electric quantity decomposition of the electric quantity data through a reference electric quantity effective duration decomposition model, obtaining a reference electric quantity curve, converting the reference electric quantity curve into a three-dimensional image, and obtaining an actual capacity image. According to the method, the actual capacity image is obtained, the top plane of the actual capacity image is fitted to obtain a top plane equation, and the capacity utilization rate of the power utilization enterprise is obtained based on the actual capacity image and the top plane equation, so that estimation is carried out only by using easily obtained electric quantity data, and the problem that reference electric quantity envelope calculation depends on maximum value point selection is solved; the actual capacity is represented by the actual capacity image, and the potential capacity is represented by the top plane equation, so that the limitation of difficult capacity data acquisition in the prior art is eliminated, the accuracy of capacity utilization rate estimation is effectively improved, and the complexity of estimation is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of capacity utilization calculation, and in particular to a method and system for estimating the capacity utilization of an electricity-consuming enterprise. Background Art

[0002] Capacity utilization, as a key indicator for measuring a company's production efficiency and the health of its resource allocation, has gradually become a focus of attention in academia and industry. For electricity users (specifically those relying on electricity as their primary energy source), capacity utilization is not only a key parameter for evaluating operational efficiency and market conditions, but also an indispensable source of information for power system planning and scheduling. For electricity users, high capacity utilization means more efficient resource utilization, lower costs, and greater market competitiveness. For the power industry, it also means efficient operation and optimal scheduling of the power system.

[0003] However, the calculation and analysis of capacity utilization faces numerous challenges. Factors such as market fluctuations, technological innovation, and fluctuations in raw material prices can impact enterprise production plans, thus affecting the accuracy and reliability of capacity utilization. Furthermore, the unique electricity consumption patterns of electricity users complicate capacity utilization assessment. Currently, capacity utilization is primarily calculated using survey statistics, cost function methods, cointegration methods, and peak methods. While these methods have achieved some success in practice, they still have significant limitations from the perspective of the power industry. For example, the production factor data required by survey statistics, cost function methods, and cointegration methods are often difficult to obtain accurately and require high human and information costs. While the peak method is more suitable for calculating capacity utilization for electricity users, the difficulty in obtaining actual capacity data in real time and the lack of objective criteria for selecting the maximum actual capacity make it difficult to fit existing peak methods to realistic potential capacity curves, resulting in low accuracy and reliability in estimated capacity utilization. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and system for estimating the capacity utilization rate of an electricity-consuming enterprise, which can effectively improve the accuracy of the capacity utilization rate estimation and reduce the complexity of the estimation.

[0005] In order to solve the above technical problems, a technical solution adopted by the present invention is: A method for estimating the capacity utilization rate of an electricity-consuming enterprise comprises the following steps: Obtain electricity consumption data for a preset time period of the electricity user; Decomposing the power data using a benchmark power effective duration decomposition model to obtain a benchmark power curve; Converting the reference power curve into a three-dimensional image to obtain an actual capacity image, and fitting the top plane of the actual capacity image to obtain a top plane equation, wherein the top plane equation represents potential capacity; The capacity utilization rate of the electricity consuming enterprise is obtained based on the actual capacity image and the top plane equation.

[0006] In order to solve the above technical problems, another technical solution adopted by the present invention is: A system for estimating the capacity utilization rate of an electricity-consuming enterprise includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented: Obtain electricity consumption data for a preset time period of the electricity user; Decomposing the power data using a benchmark power effective duration decomposition model to obtain a benchmark power curve; Converting the reference power curve into a three-dimensional image to obtain an actual capacity image, and fitting the top plane of the actual capacity image to obtain a top plane equation, wherein the top plane equation represents potential capacity; The capacity utilization rate of the electricity consuming enterprise is obtained based on the actual capacity image and the top plane equation.

[0007] The beneficial effects of the present invention are: obtaining electricity data of a preset time interval of an electricity-consuming enterprise, decomposing the electricity data using a benchmark electricity effective duration decomposition model, obtaining a benchmark electricity curve, converting the benchmark electricity curve into a three-dimensional image, obtaining an actual production capacity image, and fitting the top plane of the actual production capacity image to obtain a top plane equation, and obtaining the capacity utilization rate of the electricity-consuming enterprise based on the actual production capacity image and the top plane equation, thereby only requiring the use of easily obtained electricity data for estimation, and by fitting the top plane of the actual production capacity image to approximate the envelope of the benchmark electricity, the problem that the calculation of the benchmark electricity envelope depends on the selection of the maximum point is solved, the actual production capacity is represented by the actual production capacity image, the potential production capacity is represented by the top plane equation, and the capacity utilization rate is obtained based on the actual production capacity image and the top plane equation, thereby eliminating the limitation of the difficulty in obtaining existing production capacity data, thereby effectively improving the accuracy of the capacity utilization rate estimation and reducing the complexity of the estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 This is a flowchart of a method for estimating the capacity utilization rate of an electricity-consuming enterprise according to an embodiment of the present invention; Figure 2 This is a schematic structural diagram of a system for estimating the capacity utilization rate of an electricity-consuming enterprise according to an embodiment of the present invention; Figure 3 Schematic diagram of a reference electricity curve in a method for estimating the capacity utilization rate of an electricity-consuming enterprise according to an embodiment of the present invention; Figure 4 This is a schematic diagram of top plane fitting in the method for estimating the capacity utilization rate of an electricity-consuming enterprise according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the trend of capacity utilization rate changes in Fujian Province from 2019 to 2024 in the method for estimating capacity utilization rate of power consuming enterprises in an embodiment of the present invention; Figure 6 This is a schematic diagram of a curve showing the change in the four-block calculation results of the capacity utilization rate in Fujian Province for each quarter from 2019 to 2024 in the method for estimating the capacity utilization rate of power users in an embodiment of the present invention; Figure 7 This is a schematic diagram of a curve showing the change in the 0-block calculation results of the capacity utilization rate in Fujian Province in each quarter from 2019 to 2024 in the method for estimating the capacity utilization rate of power consuming enterprises in an embodiment of the present invention. DETAILED DESCRIPTION

[0009] To illustrate the technical content, achieved objectives and effects of the present invention in detail, the following description is given in conjunction with the embodiments and accompanying drawings.

[0010] Please refer to Figure 1 A method for estimating the capacity utilization rate of an electricity-consuming enterprise comprises the following steps: Obtain electricity consumption data for a preset time period of the electricity user; Decomposing the power data using a benchmark power effective duration decomposition model to obtain a benchmark power curve; Converting the reference power curve into a three-dimensional image to obtain an actual capacity image, and fitting the top plane of the actual capacity image to obtain a top plane equation, wherein the top plane equation represents potential capacity; The capacity utilization rate of the electricity consuming enterprise is obtained based on the actual capacity image and the top plane equation.

[0011] From the above description, it can be seen that the beneficial effects of the present invention are: obtaining electricity data of a preset time interval of an electricity-consuming enterprise, decomposing the electricity data using a benchmark electricity effective duration decomposition model, obtaining a benchmark electricity curve, converting the benchmark electricity curve into a three-dimensional image, obtaining an actual production capacity image, and fitting the top plane of the actual production capacity image to obtain a top plane equation, and obtaining the capacity utilization rate of the electricity-consuming enterprise based on the actual production capacity image and the top plane equation, so that only easily obtainable electricity data needs to be used for estimation, and by fitting the top plane of the actual production capacity image to approximate the envelope of the benchmark electricity, the problem that the calculation of the benchmark electricity envelope depends on the selection of the maximum point is solved, the actual production capacity is represented by the actual production capacity image, and the potential production capacity is represented by the top plane equation, and the capacity utilization rate is obtained based on the actual production capacity image and the top plane equation, eliminating the limitation of the difficulty in obtaining existing production capacity data, thereby effectively improving the accuracy of the capacity utilization rate estimation and reducing the complexity of the estimation.

[0012] Furthermore, the step of using a benchmark power effective duration decomposition model to decompose the power data to obtain a benchmark power curve includes: Obtaining the ambient temperature, ambient humidity, and ambient wind speed during a preset time interval of the electricity user; Calculate the perceived temperature at any time of day within the preset time interval based on the ambient temperature, the ambient humidity, and the ambient wind speed; Classifying all days within the preset time interval into typical days and atypical days according to the perceived temperature; Establishing an electricity consumption structure based on the electricity consumption data of the typical day and the atypical day; Establishing a first polynomial relationship between the baseline power and the sampling day index and a second polynomial relationship between the air conditioning power and the air conditioning effective duration on the sampling day; constructing a convex optimization problem based on the first polynomial relationship, the second polynomial relationship, and the power consumption structure; The convex optimization problem is solved to obtain first polynomial coefficients, and a reference power curve is obtained according to the first polynomial coefficients and a relationship between the first polynomial.

[0013] From the above description, it can be seen that a convex optimization problem is constructed based on the first polynomial relationship, the second polynomial relationship, and the electricity consumption structure. The convex optimization problem is solved to obtain the first polynomial coefficients, and the benchmark electricity curve is obtained based on the first polynomial coefficients and the first polynomial relationship. Since the enterprise's electricity consumption can be decomposed into air-conditioning electricity used for cooling or heating and benchmark electricity, the relationship between air-conditioning electricity and production electricity consumption is weak and has almost no effect on capacity utilization. Therefore, the benchmark electricity curve is separated for subsequent capacity utilization estimation, which improves the estimation efficiency and ensures the accuracy of the estimation.

[0014] Furthermore, the establishing of the power consumption structure based on the power consumption data of the typical day and the atypical day includes: ; Where, E i represents the electricity consumption on day i, B i represents the baseline power consumption on day i, A i represents the air conditioning power consumption on day i, Indicates all days, represents a typical day, Indicates an atypical day; The establishing of a first polynomial relationship between the reference power and the sampling day index and a second polynomial relationship between the air conditioning power and the air conditioning effective duration on the sampling day includes: ; ; Where, P represents the maximum value of the first polynomial order, i represents the sampling day index, p Indicates the p polynomial of order, represents the first polynomial coefficient, Q represents the maximum value of the second polynomial order, q Indicates the q polynomial of order, represents the second polynomial coefficient, Indicates sampling day i How long does the air conditioning last? The constructing of a convex optimization problem based on the first polynomial relationship, the second polynomial relationship, and the power consumption structure includes: ; Where, represents the first weight factor, represents the second weighting factor.

[0015] From the above description, we can see that typical days are days with only baseline electricity consumption, and atypical days are days with air-conditioning electricity consumption. Based on the first polynomial relationship, the second polynomial relationship, and the electricity consumption structure, a convex optimization problem is constructed to accurately fit the baseline electricity consumption curve.

[0016] Furthermore, converting the reference power curve into a three-dimensional image to obtain an actual power production image includes: Determine the mapping period; Mapping the reference power time series data in the reference power curve to a two-dimensional image based on the mapping period as pixel values in the two-dimensional image until all the reference power time series data in the reference power curve are mapped; The two-dimensional image is converted into a three-dimensional image to obtain an actual production capacity image.

[0017] From the above description, it can be seen that based on the mapping period, the benchmark power time series data in the benchmark power curve is mapped to a two-dimensional image as the pixel value in the two-dimensional image, and then the two-dimensional image is converted into a three-dimensional image, so as to visualize the concept of the benchmark power curve, which facilitates the subsequent approximation of the top plane as the envelope of the benchmark power curve.

[0018] Furthermore, the top plane of the actual production capacity image is fitted to obtain a top plane equation, and the top plane equation represents the potential production capacity and includes: Establishing a top plane equation of the actual production capacity image; A linear programming problem is constructed based on the top plane equation and the actual production capacity image, and the linear programming problem is solved to obtain coefficients in the top plane equation.

[0019] From the above description, it can be seen that a linear programming problem is constructed based on the top plane equation and the actual production capacity image, and the linear programming problem is solved to obtain the coefficients in the top plane equation. The top plane equation can be used to accurately represent the potential production capacity.

[0020] Furthermore, the top plane equation for establishing the actual production capacity image includes: z = ax + by + c ; Where, z Represents the height of a point on the top plane in three-dimensional space, a represents the first coefficient, b represents the second coefficient, c represents the third coefficient, x represents the horizontal coordinate of the point on the top plane, y The vertical coordinate of the point on the top plane; The linear programming problem constructed based on the top plane equation and the actual production capacity image includes: ; Where, J Indicates the total number of rows in the actual capacity image, K Indicates the total number of columns in the actual capacity image, j Indicates the actual production capacity image j OK, k Indicates the actual production capacity image k List, I(j,k) Represents the actual production capacity image.

[0021] From the above description, it can be seen that by constructing a spatial parameter-containing plane equation as the top plane of the actual production capacity image, the data can be presented in a way that is closer to the real world and the characteristics of the data can be described more accurately.

[0022] Furthermore, obtaining the capacity utilization rate of the electricity consuming enterprise based on the actual capacity image and the top plane equation includes: ; Where, CU represents the capacity utilization rate of electricity-consuming enterprises, Indicates the conversion factor.

[0023] From the above description, we can see that the capacity utilization rate is equal to the ratio of the sum of the pixels of each pixel point in the actual capacity image to the sum of the values of the top plane of the corresponding position coordinates, multiplied by the conversion coefficient. The capacity utilization rate estimated in this way can accurately reflect the capacity utilization of the enterprise.

[0024] Furthermore, the top plane of the actual production capacity image is fitted to obtain a top plane equation, and the top plane equation represents the potential production capacity and includes: Splitting the actual production capacity image into multiple sub-images; Establishing a top plane equation of the subgraph; A linear programming problem is constructed based on the top plane equation of the subgraph and the subgraph, and the linear programming problem is solved to obtain coefficients in the top plane equation of the subgraph.

[0025] From the above description, it can be seen that when fitting the top plane, the actual production capacity image can be split into multiple sub-images, and the top plane of each sub-image can be fitted separately, which can make the top plane more consistent with the actual production capacity image and improve the fitting accuracy of the top plane.

[0026] Furthermore, obtaining the capacity utilization rate of the electricity consuming enterprise based on the actual capacity image and the top plane equation includes: An initial capacity utilization rate is calculated based on the subgraph and the top plane equation of the subgraph; The capacity utilization rate of the electricity consuming enterprise is calculated based on all the initial capacity utilization rates.

[0027] From the above description, it can be seen that the initial capacity utilization rate is first calculated based on the subgraph and the top plane equation of the subgraph, and then the capacity utilization rate of the power user is calculated based on all the initial capacity utilization rates, thereby ensuring the estimation accuracy of the capacity utilization rate.

[0028] Please refer to Figure 2 Another embodiment of the present invention provides a system for estimating the capacity utilization rate of an electricity-consuming enterprise, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step in the above-mentioned method for estimating the capacity utilization rate of an electricity-consuming enterprise is implemented.

[0029] The above-mentioned method and system for estimating the capacity utilization rate of an electricity-consuming enterprise of the present invention can be applied to capacity utilization estimation scenarios, and are described below through specific implementation methods: Please refer to Figure 1 、 Figure 3-Figure 7 , embodiment 1 of the present invention is: A method for estimating the capacity utilization rate of an electricity-consuming enterprise comprises the following steps: S1. Obtaining electricity consumption data of a preset time interval of an electricity-consuming enterprise.

[0030] The preset time interval is the time interval for which the capacity utilization rate needs to be estimated.

[0031] S2. Decomposing the power data using a benchmark power effective duration decomposition model to obtain a benchmark power curve, specifically including S21-S27: S21 . Obtain the ambient temperature, ambient humidity, and ambient wind speed during a preset time interval of the electricity user.

[0032] S22: Calculate the perceived temperature at any time of day within the preset time interval based on the ambient temperature, the ambient humidity, and the ambient wind speed, specifically: ; ; ; Where, T A Indicates the perceived temperature at any time of the day within the preset time interval. W WCL Indicates the wind chill index, T e Indicates the ambient temperature (in degrees Fahrenheit), H HI Indicates the heat index, W s Indicates the ambient wind speed, R H Indicates the ambient humidity.

[0033] S23. Divide all days within the preset time interval into typical days and atypical days according to the perceived temperature.

[0034] Specifically, a preset suitable body temperature range is determined; the number of hours each day during the preset time interval during which the body temperature is outside the preset suitable body temperature range is determined; if the number of hours exceeds a preset duration, the current day corresponding to the number of hours is determined to be an atypical day; otherwise, the current day corresponding to the number of hours is determined to be a typical day.

[0035] In an optional embodiment, the preset suitable temperature range is [59°F, 75.2°F], and the preset duration is 5 hours.

[0036] Among them, atypical days are days that generate air-conditioning electricity, and typical days are days that only have baseline electricity.

[0037] S24: Establishing a power consumption structure based on the power consumption data of the typical day and the atypical day, specifically: ; Where,E i represents the electricity consumption on day i, B i represents the baseline power consumption on day i, A i represents the air conditioning power consumption on day i, Indicates all days, represents a typical day, Indicates an atypical day. A typical day is a day when the air conditioner's power consumption is zero, meaning that on a typical day, power consumption equals the baseline power consumption.

[0038] The electricity consumption of an enterprise can be decomposed into the sum of the air-conditioning electricity used for cooling or heating and the benchmark electricity (i.e. the electricity consumption other than the air-conditioning electricity). Therefore, the above electricity consumption structure is established.

[0039] S25. Establish a first polynomial relationship between the benchmark power and the sampling day index, and a second polynomial relationship between the air conditioning power and the air conditioning effective duration on the sampling day, specifically: ; ; Where, P represents the maximum value of the first polynomial order, i represents the sampling day index, p Indicates the p polynomial of order, represents the first polynomial coefficient, Q represents the maximum value of the second polynomial order, q Indicates the q polynomial of order, represents the second polynomial coefficient, Indicates sampling day i How long does the air conditioning last?

[0040] S26: Construct a convex optimization problem based on the first polynomial relationship, the second polynomial relationship, and the power consumption structure, specifically: ; Where, represents the first weight factor, In an optional embodiment, the first weighting factor and the second weighting factor are both 1.

[0041] S27, solving the convex optimization problem to obtain first polynomial coefficients, and obtaining a reference power curve based on the relationship between the first polynomial coefficients and the first polynomial, such as Figure 3 shown.

[0042] In an optional implementation, solving the convex optimization problem to obtain first polynomial coefficients includes: The convex optimization problem is solved by an interior point method to obtain first polynomial coefficients.

[0043] The reason for decomposing and obtaining the benchmark electricity curve is that non-production electricity consumption has little correlation with the enterprise's production capacity. Its existence will affect the accuracy of electricity consumption estimation and capacity utilization, so it needs to be eliminated.

[0044] like Figure 3 As shown, the envelope calculation of the reference power curve in the prior art relies on the selection of the maximum value point.

[0045] S3. Convert the reference power curve into a three-dimensional image to obtain an actual capacity image, and fit the top plane of the actual capacity image to obtain a top plane equation, wherein the top plane equation represents the potential capacity, such as Figure 4 As shown, specifically including S31-S34: S31. Determine a mapping period.

[0046] Specifically, if there is no confusion, B ( n )=[ B 1, B 2,…, B N ] represents the benchmark power time series data in the stable actual production time interval [t1, t2] (i.e. the preset time interval), FFT [ B ( n )]=[ b 1, b 2,…, b N ]express B ( n ) is the fast discrete Fourier transform of .

[0047] The benchmark electricity time series data often shows periodic characteristics. According to Fourier transform theory, the benchmark electricity time series data B ( n )The most significant frequency is equal to FFT [ B ( n )] corresponds to the frequency with the maximum amplitude of the component. FFT [ B ( n )] has the largest component index: ; B ( n ) sampling interval for ; FFT [ B ( n )] is: ; Obviously, B ( n )The most significant period and its most significant frequency are reciprocals of each other. Therefore, B ( n The most significant period of ) is: ; In actual calculation, FFT [ B ( n )] has the largest magnitude component, so it is advisable to It is the minimum index corresponding to all the maximum amplitude components. In engineering calculations, the mapping period can be selected according to the empirical period of electricity, such as quarter or month, to map the curve. In this way, the mapping period can be determined by using Fourier transform to find the most significant period of the component.

[0048] S32. Mapping the reference electric quantity time series data in the reference electric quantity curve to a two-dimensional image based on the mapping period as pixel values in the two-dimensional image until all reference electric quantity time series data in the reference electric quantity curve are mapped.

[0049] like Figure 4 As shown, the horizontal axis of the reference power curve represents time, and the vertical axis represents the reference power value. When converted into a two-dimensional image, the reference power value is used as the pixel value in the image according to the mapping period to obtain a two-dimensional image.

[0050] S33: Convert the two-dimensional image into a three-dimensional image to obtain an actual production capacity image.

[0051] like Figure 4 As shown, the two-dimensional image is placed in a three-dimensional coordinate system, the specific value of the benchmark power value is used as the Z-axis coordinate value, and the two-dimensional image is stereoscopically transformed into a three-dimensional image. Each pixel is a square cylinder with a height mark, and the actual production capacity image is obtained.

[0052] Among them, Represents benchmark power time series data B ( n )’s actual production capacity image, then B ( n )and I ( j,k ) is as follows: ; Where, Indicates floor operation, B i Represents benchmark power time series data B ( n )’s i-th sample value, J Indicates the total number of rows in the actual capacity image, K Indicates the total number of columns in the actual capacity image, j Indicates the actual production capacity image j OK, k Indicates the actual production capacity image k List, N Indicates the length of the benchmark power time series data. The inequality in the above formula means that when N Dissatisfied K When the actual production capacity image is an integer multiple of , zero padding can be used to complete it. Figure 4 As shown, the total number of rows and columns in the actual capacity image is 3.

[0053] S34. Fitting the top plane of the actual production capacity image to obtain a top plane equation.

[0054] In an optional implementation, S34 specifically includes S341-S342: S341: Establishing the top plane equation of the actual production capacity image, specifically: z = ax + by + c ; Where, z Represents the height of a point on the top plane in three-dimensional space, a represents the first coefficient, b represents the second coefficient, c represents the third coefficient, x represents the horizontal coordinate of the point on the top plane, y Indicates the vertical coordinate of a point on the top plane.

[0055] S342: constructing a linear programming problem based on the top plane equation and the actual production capacity image, and solving the linear programming problem to obtain coefficients in the top plane equation; The linear programming problem constructed based on the top plane equation and the actual production capacity image is specifically: ; Where, I(j,k) Represents the actual production capacity image.

[0056] In another optional embodiment, S34 specifically includes S341′-S343′: S341′, splitting the actual production capacity image into multiple sub-images I m ( j,k ).

[0057] The number of subgraphs can be set according to actual needs. The more subgraphs are split, the higher the accuracy of the estimated capacity utilization.

[0058] S342′, establish the top plane equation of the subgraph, specifically: z = a m x + b m y + c m ; Where, a m represents the fourth coefficient, b m represents the fifth coefficient, c m Represents the sixth coefficient.

[0059] S343′: construct a linear programming problem based on the top plane equation of the subgraph and the subgraph, and solve the linear programming problem to obtain coefficients in the top plane equation of the subgraph.

[0060] The linear programming problem constructed based on the top plane equation of the subgraph and the subgraph is specifically: .

[0061] Among them, in order to solve the problem of no standard in envelope selection and over-reliance on extreme points, it is proposed to replace the benchmark electricity curve with the actual production capacity image, and replace the benchmark electricity envelope with the top plane fitted by the actual production capacity image, so as to represent the actual production capacity with the actual production capacity image and represent the potential production capacity with the top plane equation.

[0062] S4. Obtaining the capacity utilization rate of the electricity consuming enterprise based on the actual capacity image and the top plane equation.

[0063] In an optional implementation manner, corresponding to S341-S342, S4 is specifically: ; Where, CU represents the capacity utilization rate of electricity-consuming enterprises, Indicates the conversion factor.

[0064] Among them, the real-time capacity utilization rate of the preset time interval CU i Then: .

[0065] The conversion coefficient is calculated by selecting multiple time periods with known authoritative capacity utilization data, calculating the capacity utilization results of these time periods using the actual capacity image without weighting, calculating the ratio of the two, obtaining multiple ratio data points for the time period, and calculating the conversion coefficient curve by fitting the data points.

[0066] In another optional embodiment, corresponding to S341′-S343′, S4 specifically includes S41-S42: S41. Calculate the initial capacity utilization rate based on the subgraph and the top plane of the subgraph, specifically: ; Where, represents the initial capacity utilization rate.

[0067] S42. Calculate the capacity utilization rate of the electricity-consuming enterprise based on all the initial capacity utilization rates, specifically: ; Where, CU represents the capacity utilization rate of electricity-consuming enterprises, Indicates the ratio of the number of pixels in the mth sub-image to the total number of pixels in the actual production capacity image.

[0068] The above method of the present invention is described in conjunction with practical applications, as follows: The dataset consists of two main parts: electricity consumption statistics for the entire Fujian Province and electricity consumption statistics for each of the nine prefecture-level cities within Fujian Province (Fuzhou, Putian, Quanzhou, Xiamen, Zhangzhou, Longyan, Sanming, Nanping, and Ningde). The data covers the period from January 2019 to July 2024.

[0069] To facilitate analysis, the electricity consumption data for each research subject was categorized and organized by year, quarter, and month, with a 24-hour sampling period set to record the data. It should be noted that this sampling period can be flexibly adjusted based on actual needs.

[0070] After acquiring the electricity data, we first preprocessed the data, including filling in missing values and correcting negative data by replacing them with similar data from the missing days. We then used a top-plane-based industrial capacity utilization calculation method to estimate the annual and quarterly performance of the experimental subjects.

[0071] First, the total electricity consumption time series data of the electricity-consuming enterprises is decomposed using the benchmark electricity effective duration decomposition model to separate and obtain the benchmark electricity curve. The benchmark electricity curve is converted into an image, and the actual capacity image is used to represent the benchmark electricity curve. The envelope of the benchmark electricity is approximately estimated by fitting the top plane, and the capacity utilization rate of the enterprise is estimated based on the relationship between the capacity utilization rate and the electricity consumption curve and its envelope. The capacity utilization rate calculation results of multiple experimental objects are shown in Tables 1 and 2, and their changing trends are shown in Tables 1 and 2. Figure 5 、 Figure 6 and Figure 7 shown.

[0072] Table 1 Calculation results of average capacity utilization rates for Fujian Province and its nine prefecture-level cities from 2019 to 2024

[0073] Table 2 Calculation results of average capacity utilization rate in each quarter of Fujian Province and some prefecture-level cities from 2019 to 2024

[0074] Please refer to Figure 2 , the second embodiment of the present invention is: A system for estimating the capacity utilization rate of an electricity-consuming enterprise comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step of the method for estimating the capacity utilization rate of an electricity-consuming enterprise in Example 1 is implemented.

[0075] In summary, the present invention provides a method and system for estimating the capacity utilization rate of an electricity consumer. The method obtains electricity data for a preset time interval of the electricity consumer, decomposes the electricity data using a benchmark electricity effective duration decomposition model, obtains a benchmark electricity curve, converts the benchmark electricity curve into a three-dimensional image, obtains an actual capacity image, and fits the top plane of the actual capacity image to obtain a top plane equation. The capacity utilization rate of the electricity consumer is obtained based on the actual capacity image and the top plane equation. In this way, only easily accessible electricity data is required for estimation. By fitting the top plane of the actual capacity image to approximate the envelope of the benchmark electricity, the problem of the benchmark electricity envelope calculation relying on the selection of maximum points is solved. The actual capacity image is used to represent the actual capacity, and the top plane equation is used to represent the potential capacity. The capacity utilization rate is obtained based on the actual capacity image and the top plane equation, eliminating the limitation of the difficulty in obtaining existing capacity data, thereby effectively improving the accuracy of capacity utilization estimation and reducing the complexity of estimation. In addition, when fitting the top plane, the actual capacity image can be split into multiple sub-images, and the top plane of each sub-image is fitted separately. This can make the top plane more closely fit the actual capacity image, thereby improving the fitting accuracy of the top plane.

[0076] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's description and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for estimating the capacity utilization rate of an electricity-consuming enterprise, characterized in that: Including steps: Obtain electricity consumption data for a preset time period of the electricity user; Decomposing the power data using a benchmark power effective duration decomposition model to obtain a benchmark power curve; Converting the reference power curve into a three-dimensional image to obtain an actual capacity image, and fitting the top plane of the actual capacity image to obtain a top plane equation, wherein the top plane equation represents potential capacity; The capacity utilization rate of the electricity consuming enterprise is obtained based on the actual capacity image and the top plane equation.

2. The method for estimating the capacity utilization rate of an electricity-consuming enterprise according to claim 1, characterized in that: The step of using a benchmark power effective duration decomposition model to decompose the power data to obtain a benchmark power curve includes: Obtaining the ambient temperature, ambient humidity, and ambient wind speed during a preset time interval of the electricity user; Calculate the perceived temperature at any time of day within the preset time interval based on the ambient temperature, the ambient humidity, and the ambient wind speed; Classifying all days within the preset time interval into typical days and atypical days according to the perceived temperature; Establishing an electricity consumption structure based on the electricity consumption data of the typical day and the atypical day; Establishing a first polynomial relationship between the baseline power and the sampling day index and a second polynomial relationship between the air conditioning power and the air conditioning effective duration on the sampling day; constructing a convex optimization problem based on the first polynomial relationship, the second polynomial relationship, and the power consumption structure; The convex optimization problem is solved to obtain first polynomial coefficients, and a reference power curve is obtained according to the first polynomial coefficients and a relationship between the first polynomial.

3. The method for estimating the capacity utilization rate of an electricity-consuming enterprise according to claim 2, characterized in that: The establishing of the power consumption structure based on the power consumption data of the typical day and the atypical day includes: ； Where, E i represents the electricity consumption on day i, B i represents the baseline power consumption on day i, A i represents the air conditioning power consumption on day i, Indicates all days, represents a typical day, Indicates an atypical day; The establishing of a first polynomial relationship between the reference power and the sampling day index and a second polynomial relationship between the air conditioning power and the air conditioning effective duration on the sampling day includes: ； ； Where, P represents the maximum value of the first polynomial order, i represents the sampling day index, p Indicates the p polynomial of order, represents the first polynomial coefficient, Q represents the maximum value of the second polynomial order, q Indicates the q polynomial of order, represents the second polynomial coefficient, Indicates sampling day i How long does the air conditioning last? The constructing of a convex optimization problem based on the first polynomial relationship, the second polynomial relationship, and the power consumption structure includes: ； Where, represents the first weight factor, represents the second weighting factor.

4. The method for estimating the capacity utilization rate of an electricity-consuming enterprise according to claim 1, characterized in that: Converting the reference power curve into a three-dimensional image to obtain an actual power production image includes: Determine the mapping period; Mapping the reference power time series data in the reference power curve to a two-dimensional image based on the mapping period as pixel values in the two-dimensional image until all the reference power time series data in the reference power curve are mapped; The two-dimensional image is converted into a three-dimensional image to obtain an actual production capacity image.

5. The method for estimating the capacity utilization rate of an electricity-consuming enterprise according to claim 1, characterized in that: The top plane equation obtained by fitting the top plane of the actual production capacity image includes: Establishing a top plane equation of the actual production capacity image; A linear programming problem is constructed based on the top plane equation and the actual production capacity image, and the linear programming problem is solved to obtain coefficients in the top plane equation.

6. The method for estimating the capacity utilization rate of an electricity-consuming enterprise according to claim 5, characterized in that: The top plane equation for establishing the actual production capacity image includes: z = ax + by + c ； Where, z Represents the height of a point on the top plane in three-dimensional space, a represents the first coefficient, b represents the second coefficient, c represents the third coefficient, x represents the horizontal coordinate of the point on the top plane, y The vertical coordinate of the point on the top plane; The linear programming problem constructed based on the top plane equation and the actual production capacity image includes: ； Where, J Indicates the total number of rows in the actual capacity image, K Indicates the total number of columns in the actual capacity image, j Indicates the actual production capacity image j OK, k Indicates the actual production capacity image k List, I(j,k) Represents the actual production capacity image.

7. The method for estimating the capacity utilization rate of an electricity-consuming enterprise according to claim 6, characterized in that: The obtaining of the capacity utilization rate of the electricity consuming enterprise based on the actual capacity image and the top plane equation includes: ； Where, CU represents the capacity utilization rate of electricity-consuming enterprises, Indicates the conversion factor.

8. The method for estimating the capacity utilization rate of an electricity-consuming enterprise according to claim 1, characterized in that: The top plane equation obtained by fitting the top plane of the actual production capacity image includes: Splitting the actual production capacity image into multiple sub-images; Establishing a top plane equation of the subgraph; A linear programming problem is constructed based on the top plane equation of the subgraph and the subgraph, and the linear programming problem is solved to obtain coefficients in the top plane equation of the subgraph.

9. The method for estimating the capacity utilization rate of an electricity-consuming enterprise according to claim 8, characterized in that: The obtaining of the capacity utilization rate of the electricity consuming enterprise based on the actual capacity image and the top plane equation includes: An initial capacity utilization rate is calculated based on the subgraph and the top plane equation of the subgraph; The capacity utilization rate of the electricity consuming enterprise is calculated based on all the initial capacity utilization rates.

10. A system for estimating the capacity utilization rate of an electricity-consuming enterprise, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, each step of the method for estimating the capacity utilization rate of an electricity consumer according to any one of claims 1 to 9 is implemented.

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