Method and system for fitting output data of conventional power supply based on cloud transformation fitting

Abstract

The invention discloses a conventional power supply output data fitting method and system based on cloud transformation fitting, and belongs to the technical field of power system prediction. The method comprises the following steps: acquiring conventional power supply output data of a conventional power supply of a power system, and counting the conventional power supply output data to obtain an output distribution curve of the conventional power supply output data; fitting the conventional power supply output data according to the output distribution curve based on cloud transformation to obtain a distribution function of the output distribution curve; and taking the distribution function as a curve fitting equation for conventional power supply output data fitting. According to the method, fitting is performed on the data based on cloud transformation, and remarkable superiority is shown in the aspects of prediction precision improvement, resource saving, operation simplification, environmental protection promotion and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of power system prediction, and more specifically, to a conventional power output data fitting method and system based on cloud transformation fitting. Background Art

[0002] As the world pays more attention to the transformation of energy structure, the operation and dispatching and economic efficiency of thermal and hydropower as traditional power generation methods have received more and more attention. Medium- and long-term demand fluctuations, climate change and water resource availability will affect power generation capacity. In-depth understanding of the minimum output law can help power dispatching agencies plan power generation strategies more effectively and ensure safe and stable operation of the power grid.

[0003] In addition, the national policy regulation of thermal power and hydropower and the pressure of environmental protection also make it particularly important to optimize their output characteristics. Using modern data analysis technology and models, the minimum output characteristics of generators under different operating conditions can be captured more accurately, which not only helps to improve resource utilization efficiency, but also provides a scientific basis for market price formation and power trading. Summary of the invention

[0004] In view of the above problems, the present invention proposes a conventional power output data fitting method based on cloud transformation fitting, comprising:

[0005] Acquire conventional power output data of a conventional power source of the power system, perform statistics on the conventional power output data, and obtain an output distribution curve of the conventional power output data;

[0006] Based on cloud transformation, according to the output distribution curve, the conventional power supply output data is fitted to obtain a distribution function of the output distribution curve;

[0007] The distribution function is used as a curve fitting equation for fitting conventional power output data.

[0008] Optionally, based on cloud transformation, according to the output distribution curve, the conventional power supply output data is fitted to obtain a distribution function of the distribution curve, including:

[0009] Based on the cloud transformation, a cloud model that fits the output distribution curve is generated according to the output distribution curve, and based on the cloud model, the residual data distribution is determined, and based on the residual data distribution, the cloud model distribution function is repeatedly constructed until all the peaks of the output distribution curve are fitted, and the distribution function of the distribution curve is generated.

[0010] Optionally, based on the cloud transformation, generating a cloud model distribution function fitting the output distribution curve according to the output distribution curve includes:

[0011] Based on cloud transformation, the output distribution curve is identified, the peak position of each conventional power output data is determined, the peak position is used as the center of gravity position of the cloud, and the entropy of the cloud model is calculated based on the center of gravity position. Based on the center of gravity position and entropy, the normal cloud model formula is applied to generate a cloud model.

[0012] Optionally, determining residual data distribution based on the cloud model distribution function includes:

[0013] The data values ​​of the part of the output distribution curve that is fitted with the cloud model are removed, and the data values ​​of the remaining part of the output distribution curve are used as residual distribution.

[0014] Optionally, based on the residual data distribution, repeatedly constructing the number of cloud models until all the peaks of the output distribution curve are fitted, including:

[0015] According to the residual data distribution, repeatedly identifying the peaks of the output distribution curve to construct a cloud model until all the peaks are fitted;

[0016] After all the peaks are fitted, a number of corresponding cloud model distribution functions are obtained.

[0017] Optionally, generate a distribution function for the distribution curve, including:

[0018] A plurality of corresponding cloud model distribution functions are superimposed and fitted to generate a distribution function of a distribution curve.

[0019] Optionally, the method further includes: when generating a cloud model for fitting the output distribution curve, using an expected curve of the cloud model for fitting.

[0020] Optionally, when fitting the expected curve of the cloud model, determine the peak position, take the peak position as the center, screen out the first trough and the first peak on the left and right sides of the center respectively, calculate the difference between the first trough and the first peak, and compare the difference with a preset threshold. If the difference is greater than the preset threshold, record the value of the first peak. If the difference is less than the preset threshold, look for the next peak until the difference is greater than the preset threshold. Construct a value range of data points based on the recorded value of the first peak, and use the data points within the value range as cloud droplets. Based on the cloud droplets, use the inverse cloud algorithm to calculate the initial value of entropy.

[0021] On the other hand, the present invention also proposes a conventional power output data fitting system based on cloud transformation fitting, comprising:

[0022] A statistical unit, used to obtain conventional power output data of conventional power supplies of the power system, perform statistics on the conventional power output data, and obtain an output distribution curve of the conventional power output data;

[0023] A fitting unit, configured to fit the conventional power supply output data according to the output distribution curve based on cloud transformation to obtain a distribution function of the output distribution curve;

[0024] The output unit is used to use the distribution function as a curve fitting equation for fitting conventional power supply output data.

[0025] Optionally, based on cloud transformation, according to the output distribution curve, the conventional power supply output data is fitted to obtain a distribution function of the distribution curve, including:

[0026] Based on the cloud transformation, a cloud model that fits the output distribution curve is generated according to the output distribution curve, and based on the cloud model, the residual data distribution is determined, and based on the residual data distribution, the cloud model distribution function is repeatedly constructed until all the peaks of the output distribution curve are fitted, and the distribution function of the distribution curve is generated.

[0027] Optionally, based on the cloud transformation, generating a cloud model distribution function fitting the output distribution curve according to the output distribution curve includes:

[0028] Based on cloud transformation, the output distribution curve is identified, the peak position of each conventional power output data is determined, the peak position is used as the center of gravity position of the cloud, and the entropy of the cloud model is calculated based on the center of gravity position. Based on the center of gravity position and entropy, the normal cloud model formula is applied to generate a cloud model.

[0029] Optionally, determining residual data distribution based on the cloud model distribution function includes:

[0030] The data values ​​of the part of the output distribution curve that is fitted with the cloud model are removed, and the data values ​​of the remaining part of the output distribution curve are used as residual distribution.

[0031] Optionally, based on the residual data distribution, repeatedly constructing the cloud model number until all the peaks of the output distribution curve are fitted, including:

[0032] According to the residual data distribution, repeatedly identifying the peaks of the output distribution curve to construct a cloud model until all the peaks are fitted;

[0033] After all the peaks are fitted, a number of corresponding cloud model distribution functions are obtained.

[0034] Optionally, generate a distribution function for the distribution curve, including:

[0035] A plurality of corresponding cloud model distribution functions are superimposed and fitted to generate a distribution function of a distribution curve.

[0036] Optionally, when generating a cloud model for fitting the output distribution curve, an expected curve of the cloud model is used for fitting.

[0037] Optionally, when fitting the expected curve of the cloud model, determine the peak position, take the peak position as the center, screen out the first trough and the first peak on the left and right sides of the center respectively, calculate the difference between the first trough and the first peak, and compare the difference with a preset threshold. If the difference is greater than the preset threshold, record the value of the first peak. If the difference is less than the preset threshold, look for the next peak until the difference is greater than the preset threshold. Construct a value range of data points based on the recorded value of the first peak, and use the data points within the value range as cloud droplets. Based on the cloud droplets, use the inverse cloud algorithm to calculate the initial value of entropy.

[0038] In yet another aspect, the present invention further provides a computing device, comprising: one or more processors;

[0039] a processor for executing one or more programs;

[0040] When the one or more programs are executed by the one or more processors, the above-described method is implemented.

[0041] In yet another aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, the method described above is implemented.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The present invention provides a conventional power output data fitting method based on cloud transformation fitting, comprising: obtaining conventional power output data of conventional power supplies of a power system, performing statistics on the conventional power output data, and obtaining an output distribution curve of the conventional power output data; based on cloud transformation, fitting the conventional power output data according to the output distribution curve to obtain a distribution function of the output distribution curve; using the distribution function as a curve fitting equation for fitting conventional power output data. The present invention fits data based on cloud transformation, and exhibits significant advantages in improving prediction accuracy, saving resources, simplifying operations, and promoting environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a flow chart of the method of the present invention;

[0045] Figure 2 It is a framework diagram of cloud transformation fitting conventional power output in the present invention;

[0046] Figure 3 It is a schematic diagram of correlation analysis of factors affecting the output prediction of conventional power sources in the present invention;

[0047] Figure 4 It is a polynomial fitting diagram of the conventional power output in the present invention;

[0048] Figure 5 It is a random forest fitting diagram of the conventional power output in the present invention;

[0049] Figure 6 It is a cloud transformation fitting diagram of conventional power output in the present invention;

[0050] Figure 7 It is a structural diagram of the system of the present invention. DETAILED DESCRIPTION

[0051] Now, exemplary embodiments of the present invention are described with reference to the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the accompanying drawings are not intended to limit the present invention. In the accompanying drawings, the same units / elements are marked with the same reference numerals.

[0052] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0053] Embodiment 1:

[0054] The accurate prediction of conventional power output of power system has a direct impact on the dispatching scheme of power system. In order to improve the prediction accuracy, scholars have extensively studied and proposed a variety of related models. However, these methods still have significant deficiencies when dealing with data with complex time-frequency characteristics, noise interference and local mutations. Traditional fitting methods, such as polynomial regression and linear regression, usually assume that the data is stationary and are suitable for describing the overall trend. However, power output data is often affected by multiple factors such as weather changes, load fluctuations and equipment operating status, showing significant non-stationarity and local fluctuations. Traditional methods based on the stationary assumption are difficult to accurately capture these non-stationary characteristics, resulting in poor prediction of short-term fluctuations and emergencies, thereby increasing the prediction error.

[0055] On the other hand, most traditional fitting methods rely on global models, which often make it difficult to finely handle local fluctuations and instantaneous changes in the data. Power output data often contains short-term sudden fluctuations, such as load fluctuations or sudden changes in equipment output, and global models are usually unable to accurately capture these local features, resulting in low accuracy of the fitting results. In contrast, cloud transform overcomes the shortcomings of these traditional methods with its unique time-frequency localization characteristics and multi-resolution analysis capabilities, showing obvious advantages.

[0056] Cloud transform can decompose signals at different scales, extracting high-frequency details and capturing low-frequency overall trends, adapting to the variable characteristics of power output data. Through multi-scale analysis, cloud transform can simultaneously identify short-term fluctuations and long-term trends in signals, effectively overcoming the limitation that traditional methods cannot handle non-stationary signals. In the fitting of power output data, cloud transform can adaptively adjust the decomposition scale according to local changes in the data, thereby accurately capturing mutations or local fluctuations in the data. Especially in the face of complex dynamic changes, cloud transform can provide more detailed fitting results.

[0057] In addition, the local feature capture capability of Cloud Transform enables it to accurately fit instantaneous changes and sudden fluctuations in power output data, which is difficult to do with traditional methods. By carefully analyzing the signal at multiple scales, Cloud Transform can efficiently extract key features in power output data, reduce the risk of overfitting, and improve the generalization ability of the model. This adaptive analysis method enables Cloud Transform to provide higher fitting accuracy and stronger prediction capabilities when processing power system data with complex dynamic characteristics.

[0058] Therefore, considering the advantages of cloud transform in multi-resolution analysis, local feature capture, noise suppression and adaptive fitting, compared with traditional methods, cloud transform can better handle non-stationary data, local mutations and noise interference, and significantly improve the accuracy and stability of fitting results.

[0059] By analyzing the output data of conventional power sources, it is found that climate, primary energy prices, dates and conventional power output data satisfy a series of complex nonlinear differential equations and integral equations. Optimizing the production process containing a large number of differential and integral equations will face the problem of "dimensionality curse" and cannot meet the needs of conventional power output forecasting. Therefore, the relationship model between relevant factors and conventional power output data needs to be fundamentally transformed from differential and integral equations to algebraic equations to reduce the computational complexity.

[0060] Cloud transformation is a process of converting quantitative data into qualitative concepts, which can find the regularity and statistical characteristics of data from massive data. Cloud transformation has the advantage of comprehensive analysis of fuzziness, randomness and the correlation between the two, and is suitable for curve fitting problems with fuzziness and randomness in the sample collection process. One-dimensional cloud transformation can only perform single-factor analysis, while conventional power output is affected by multiple factors, so multi-dimensional cloud transformation theory is required.

[0061] Based on this, the present invention proposes a conventional power output data fitting method based on cloud transformation fitting, such as Figure 1 As shown, including:

[0062] Step 1: obtaining conventional power output data of a conventional power source of a power system, performing statistics on the conventional power output data, and obtaining an output distribution curve of the conventional power output data;

[0063] Step 2: Based on cloud transformation, according to the output distribution curve, the conventional power output data is fitted to obtain a distribution function of the output distribution curve;

[0064] Step 3: Using the distribution function as a curve fitting equation for fitting conventional power output data.

[0065] Wherein, based on the cloud transformation, according to the output distribution curve, the conventional power supply output data is fitted to obtain the distribution function of the distribution curve, including:

[0066] Based on the cloud transformation, a cloud model that fits the output distribution curve is generated according to the output distribution curve, and based on the cloud model, the residual data distribution is determined, and based on the residual data distribution, the cloud model distribution function is repeatedly constructed until all the peaks of the output distribution curve are fitted, and the distribution function of the distribution curve is generated.

[0067] Wherein, based on the cloud transformation, according to the output distribution curve, a cloud model distribution function fitting the output distribution curve is generated, including:

[0068] Based on cloud transformation, the output distribution curve is identified, the peak position of each conventional power output data is determined, the peak position is used as the center of gravity position of the cloud, and the entropy of the cloud model is calculated based on the center of gravity position. Based on the center of gravity position and entropy, the normal cloud model formula is applied to generate a cloud model.

[0069] Wherein, based on the cloud model distribution function, determining the residual data distribution includes:

[0070] The data values ​​of the part of the output distribution curve that is fitted with the cloud model are removed, and the data values ​​of the remaining part of the output distribution curve are used as residual distribution.

[0071] Wherein, based on the residual data distribution, the cloud model number is repeatedly constructed until all the peaks of the output distribution curve are fitted, including:

[0072] According to the residual data distribution, repeatedly identifying the peaks of the output distribution curve to construct a cloud model until all the peaks are fitted;

[0073] After all the peaks are fitted, a number of corresponding cloud model distribution functions are obtained.

[0074] The distribution function for generating the distribution curve includes:

[0075] A plurality of corresponding cloud model distribution functions are superimposed and fitted to generate a distribution function of a distribution curve.

[0076] The method further includes: when generating a cloud model for fitting the output distribution curve, using the expected curve of the cloud model for fitting.

[0077] Among them, when the expected curve of the cloud model is used for fitting, the peak position is determined, and the first trough and the first peak are screened out on the left and right sides around the center with the peak position as the center, and the difference between the first trough and the first peak is calculated, and the difference is compared with the preset threshold. If the difference is greater than the preset threshold, the value of the first peak is recorded. If the difference is less than the preset threshold, the next peak is found until the difference is greater than the preset threshold. The value range of the data point is constructed based on the recorded value of the first peak, and the data points within the value range are used as cloud droplets. Based on the cloud droplets, the initial value of entropy is calculated using the inverse cloud algorithm.

[0078] The present invention is further described below with reference to specific cases:

[0079] The specific case steps framework is as follows Figure 2 As shown, including:

[0080] (1) Count the output of conventional power sources and obtain the distribution curve f[i].

[0081] (2) Fitting the obtained force data based on cloud transformation:

[0082] ① In the statistically obtained conventional power output distribution curve, identify the position of each data peak one by one. The peak value is used to determine the center of gravity position (expectation) Ex[i] (i=0,…,m-1) of the cloud model; the value of the peak position is recorded as the center of gravity (expectation) of the cloud. The determination of the center of gravity position plays a decisive role in the subsequent entropy calculation and model fitting, so the accuracy of the peak selection and position should be ensured. After the peak position has been determined, the entropy En of the cloud model is calculated based on this position. iThe entropy calculation process includes analyzing the volatility and dispersion of the cloud model near the center of gravity to build a model suitable for describing the distribution characteristics of the peak data. Based on the calculated expectation and entropy values, the normal cloud model formula is applied to generate a distribution function f suitable for fitting the peak. i (x), which is used to characterize the local features of the data.

[0083] ② On the distribution curve, use the cloud model distribution function calculated in the previous step to convert the data value f[i] in the current data distribution f[i] that matches the cloud model fitting part i (x) is removed, and the remaining part f′[i] is retained as the new residual data distribution. For the new residual data, the peak identification and cloud model construction process in step 2 is repeated again, and new cloud models are continuously generated until all peaks are effectively fitted, and multiple corresponding cloud model distribution functions f are obtained. i (x), through multiple iterations, a comprehensive data distribution model is formed by superimposing multiple cloud model distribution functions. Each cloud model f i (x) represents a specific local data feature, which depicts the overall trend of the data curve layer by layer. Through the superposition and adjustment of the cloud model distribution function, a complete curve fitting equation is finally formed, which integrates all local features into the overall distribution and fits the overall distribution of conventional power output data with the best accuracy.

[0084] (3) Get the distribution function of the curve And use it as the curve fitting equation.

[0085] In step (2), when calculating the cloud model of the fitting distribution curve, the expected curve of the cloud model is used for fitting. The expected curve formula is: When using the cloud model for curve fitting, the determination of En is crucial. Ex can be determined in the jth step of the iteration. j After that, according to the actual situation, j Select several representative points around and use these representative points to obtain En through the reverse cloud algorithm j The specific implementation steps are as follows:

[0086] (1) Find the curve peak Ex.

[0087] (2) Take n points on both sides of Ex (the size of n can be chosen experimentally). With Ex as the center, find the value of the first trough and the value of the first peak in the range (Ex-n, Ex), and calculate whether the difference between the two is greater than a certain threshold. If it is greater than the threshold, the value of this point is recorded as x. left If it is less than the specific value, ignore this change and continue to look for the next peak until the condition is met; similarly, find x on the right right . Compare Ex-x leftWith x righ -Ex value, take the smaller one, assuming it is x right -Ex.

[0088] (3) [Ex-fabs(x righ -Ex),Ex+fabs(x right -Ex)] as cloud droplets, and the initial value of En is calculated using the reverse cloud algorithm that does not require certainty information.

[0089] This paper selects the public operation data of the Belgian power grid in 2018 as the data set, and compares the effects of polynomial fitting, random forest fitting and cloud transformation fitting. The fitting results of each method are shown in Figure 3 Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. Generally, the components of the embodiments of the present invention described and shown in the figures herein can be arranged and designed in various different configurations, including the following:

[0090] (1) Identify key factors from a wide variety of influencing factors.

[0091] In actual production, there are many factors that affect the output of the power grid, mainly including the following: temperature, dew point temperature, wind speed, cloud height, crude oil price, industrial production volume, date. In order to obtain the analytical algebraic model of conventional power output data and related factors, it is necessary to first identify the key factors from a wide variety of influencing factors, determine the factors that have the greatest impact on power consumption, and ignore minor factors to simplify the complex production process relationship. This method uses the Pearson and Spearman correlation coefficient weighted analysis method to identify key influencing factors.

[0092] The Pearson correlation coefficient calculation formula is as follows:

[0093]

[0094] in, represents the sum of squares of the deviations from the mean of X;

[0095] represents the sum of squares of the deviations from the mean of Y;

[0096] It represents the sum of squares of the mean deviations of X and Y. The formula for calculating the Spearman correlation coefficient is as follows:

[0097]

[0098] Among them, R(x) and R(y) are the positions of x and y respectively; and Respectively represent the average rank.

[0099] The simplified Spearman formula is as follows:

[0100]

[0101] Among them, d i It represents the difference in the rank value of the i-th data pair, and n represents the total number of observed samples.

[0102] The weighted formulas for Pearson and Spearman correlation coefficients are as follows:

[0103]

[0104] (2) The temperature, dew point temperature, and date are identified as key influencing factors, and the corresponding relationship between them and the conventional power output data is stored in the set Ξ:

[0105]

[0106] Among them, x1,x2,…,x L is the key influencing factor, y is the conventional power output data, (x1, x2, …x L ,y) k is the corresponding relationship of the kth group of values, N s is the total number of elements in the set. Draw a distribution curve based on the set Ξ, and let m=1.

[0107] (3) Determine the location of the mth peak point of the distribution curve, and find the nearest trough on the left and right sides with the peak point as the center. Calculate the independent variable x according to the following formula n Expectations n And the entropy corresponding to this expectation is Yx n .

[0108]

[0109] in, and are the independent variables x that can be obtained between the two troughs. n The data distribution function f is calculated using the normal cloud model m (x1,x2,…,x L ).

[0110]

[0111] The normal cloud model is based on normal distribution and is universal. Let m=m+1.

[0112] (4) Repeat step (3) until m = N p , where Np is the total number of peak points in the distribution curve. The curve equation after fitting is shown as follows

[0113]

[0114] Where: a m is the amplitude coefficient, and its value can be determined by least squares parameter estimation; is the value of each controllable factor at time t during the operation; is the corresponding electrical power.

[0115] During the specific implementation process, based on open source meteorological factors, industrial production and measured data on conventional power output, all historical data for the whole year of 2022 were selected as the data set with a step size of 1 hour.

[0116] In order to fully evaluate the effect of the present invention, the following three fitting methods are set for comparison:

[0117] Method 1: Polynomial fitting

[0118] Method 2: Random Forest Fitting

[0119] Method 3: Cloud Transform Fitting

[0120] Method 1 uses polynomial fitting, with independent variables set as temperature and time data, and dependent variables set as conventional power output data. First, select the highest order of independent variables, where the temperature independent variable order is 3 and the time independent variable order is 2, so as to build a polynomial model, and then substitute the data points into the polynomial model to obtain a linear equation system, and finally use the least squares method to solve the linear equation system. The obtained model is as shown in formula (1.10), and the parameters are shown in Table 1.

[0121]

[0122] Table 1

[0123]

[0124] Method 2 uses the random forest fitting method, defines the feature quantities as temperature, dew point temperature, date, and day of the week, and the target variable as conventional power load; divides the data set into a training set of 292 samples and a test set of the remaining 73 samples; normalizes the features and target variables to the interval [0,1]; creates a random forest model containing 40 decision trees; uses the trained model to predict the test set, and denormalizes the prediction results to the original target variable range.

[0125] Compare the fitting curves drawn by the three fitting methods with the true value data, such as Figure 4 , 5, 6. By comparing the results and characteristics, it is found that polynomial fitting is simple to calculate, efficient, intuitive in form, easy to explain and understand, but it is not suitable for nonlinear relationships and produces large errors near the endpoints of the data. Random forest fitting can provide very high prediction accuracy, can handle data with missing values, and is not easy to overfit, but its model is complex, difficult to explain, and has no clear mathematical expression. Cloud transform fitting can simulate complex data by combining multiple normal distributions. And each normal distribution corresponds to a feature in the data, the output of the model is easier to explain, and has a clear mathematical expression.

[0126] Table 2 compares the fitting errors generated by the three fitting methods, in which two error indicators are considered, namely R 2 (Coefficient of Determination) and RMSE (Root Mean Squared Error), the calculation formulas of each indicator are shown in formula (11) and formula (12). i is the true value, is the predicted value, is the mean.

[0127]

[0128] Table 2

[0129]

[0130] As shown in Table 2, the R 2 The coefficient is 0.17803 higher than the traditional polynomial fitting method, and the RMSE coefficient is 31.88% lower than the polynomial fitting method. Compared with advanced machine learning methods such as random forest, R 2 The coefficient is not much different from the RMSE coefficient. Figure 4 , 5 As shown in Figure 6, cloud transform fitting shows its unique advantages. It not only provides higher fitting accuracy, but also has clear mathematical expressions compared to machine learning methods such as random forests, which makes the results of the model easier to interpret and understand. This clear expression facilitates the analysis and optimization of the model, and also makes the prediction process of the model more transparent, which helps users verify and trust the prediction results of the model. In addition, this feature of the cloud transform fitting method is particularly important in practical applications. It allows engineers and decision makers to better understand the working principle of the model, so as to make more scientific and reasonable decisions in actual power system planning and scheduling. In summary, the cloud transform fitting method has shown its superiority in both accuracy and interpretability, which makes it have important application value in the field of power system prediction.

[0131] Compared with the prior art, the present invention has the following advantages: it realizes the transformation of conventional power supply output data fitting from differential and integral models to algebraic models, solves the difficult problems of analytical modeling and model dimensionality reduction, and provides a methodological basis for the conventional power supply output law in the medium and long term, including the confidence interval of the minimum output of conventional power supply.

[0132] Embodiment 2:

[0133] The present invention also proposes a conventional power output data fitting system 200 based on cloud transformation fitting, such as Figure 7 As shown, including:

[0134] A statistical unit 201 is used to obtain conventional power output data of conventional power sources of the power system, perform statistics on the conventional power output data, and obtain an output distribution curve of the conventional power output data;

[0135] A fitting unit 202, configured to fit the conventional power supply output data according to the output distribution curve based on cloud transformation to obtain a distribution function of the output distribution curve;

[0136] The output unit 203 is used to use the distribution function as a curve fitting equation for fitting conventional power supply output data.

[0137] Wherein, based on the cloud transformation, according to the output distribution curve, the conventional power supply output data is fitted to obtain the distribution function of the distribution curve, including:

[0138] Based on the cloud transformation, a cloud model that fits the output distribution curve is generated according to the output distribution curve, and based on the cloud model, the residual data distribution is determined, and based on the residual data distribution, the cloud model distribution function is repeatedly constructed until all the peaks of the output distribution curve are fitted, and the distribution function of the distribution curve is generated.

[0139] Wherein, based on the cloud transformation, according to the output distribution curve, a cloud model distribution function fitting the output distribution curve is generated, including:

[0140] Based on cloud transformation, the output distribution curve is identified, the peak position of each conventional power output data is determined, the peak position is used as the center of gravity position of the cloud, and the entropy of the cloud model is calculated based on the center of gravity position. Based on the center of gravity position and entropy, the normal cloud model formula is applied to generate a cloud model.

[0141] Wherein, based on the cloud model distribution function, determining the residual data distribution includes:

[0142] The data values ​​of the part of the output distribution curve that is fitted with the cloud model are removed, and the data values ​​of the remaining part of the output distribution curve are used as residual distribution.

[0143] Wherein, based on the residual data distribution, the cloud model number is repeatedly constructed until all the peaks of the output distribution curve are fitted, including:

[0144] According to the residual data distribution, repeatedly identifying the peaks of the output distribution curve to construct a cloud model until all the peaks are fitted;

[0145] After all the peaks are fitted, a number of corresponding cloud model distribution functions are obtained.

[0146] The distribution function for generating the distribution curve includes:

[0147] A plurality of corresponding cloud model distribution functions are superimposed and fitted to generate a distribution function of a distribution curve.

[0148] When generating a cloud model for fitting the output distribution curve, the expected curve of the cloud model is used for fitting.

[0149] Among them, when the expected curve of the cloud model is used for fitting, the peak position is determined, and the first trough and the first peak are screened out on the left and right sides around the center with the peak position as the center, and the difference between the first trough and the first peak is calculated, and the difference is compared with the preset threshold. If the difference is greater than the preset threshold, the value of the first peak is recorded. If the difference is less than the preset threshold, the next peak is found until the difference is greater than the preset threshold. The value range of the data point is constructed based on the recorded value of the first peak, and the data points within the value range are used as cloud droplets. Based on the cloud droplets, the initial value of entropy is calculated using the inverse cloud algorithm.

[0150] In Examples 1 and 2, by comparing the output prediction capabilities of the cloud transformation method and the traditional linear regression method in a specific area, the results show that the prediction error using the cloud transformation model is reduced by 15% to 25%. Compared with the traditional linear regression model, its fitting accuracy is higher, and it can better capture the nonlinear characteristics in the output data and grasp the law of changes in the output level of conventional power sources. The cloud transformation-based fitting method improves the prediction accuracy, allowing the dispatch center to more effectively arrange the operation of the generator set and avoid over- or under-generation.

[0151] At the same time, the construction and implementation of the cloud transformation model is relatively simple, especially in the data preprocessing stage. Through automated algorithms, operators can quickly obtain the required prediction results, reducing dependence on senior technicians and reducing operational complexity. Accurate output forecasting can reduce dependence on fossil fuels and promote the effective use of renewable energy.

[0152] Power plants that use the cloud transformation method to fit conventional power output data have reduced a large amount of carbon emissions, and their environmental benefits are significantly improved compared to traditional methods.

[0153] In addition, the present invention also enhances the ability to handle uncertainty.

[0154] Experimental results show that the present invention can maintain good prediction performance in dynamic environments, especially in extreme weather conditions, showing stronger adaptability and stability. Cloud transformation can transform complex nonlinear relationships into manageable forms by quantifying uncertainty, making the model more adaptable when facing complex environments. The theoretical basis of this method lies in fuzzy set theory and probability statistics, which can effectively integrate information from different sources, reduce information loss, and thus improve overall prediction capabilities.

[0155] In summary, the present invention has demonstrated significant superiority in improving prediction accuracy, saving resources, simplifying operations, and promoting environmental protection, fully demonstrating the practical value and potential impact of the invention.

[0156] Embodiment 3:

[0157] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding functions, so as to implement the steps of the method in the above embodiment.

[0158] Embodiment 4:

[0159] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both a built-in storage medium in a computer device and an extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the method in the above embodiment.

[0160] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0161] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0162] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0163] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0164] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0165] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A conventional power output data fitting method based on cloud transformation fitting, characterized in that: include: Acquire conventional power output data of a conventional power source of the power system, perform statistics on the conventional power output data, and obtain an output distribution curve of the conventional power output data; Based on cloud transformation, according to the output distribution curve, the conventional power supply output data is fitted to obtain a distribution function of the output distribution curve; The distribution function is used as a curve fitting equation for fitting conventional power output data.

2. The method according to claim 1, characterized in that The cloud-based transformation, according to the output distribution curve, fits the conventional power output data to obtain the distribution function of the distribution curve, including: Based on the cloud transformation, a cloud model that fits the output distribution curve is generated according to the output distribution curve, and based on the cloud model, the residual data distribution is determined, and based on the residual data distribution, the cloud model distribution function is repeatedly constructed until all the peaks of the output distribution curve are fitted, and the distribution function of the distribution curve is generated.

3. The method according to claim 2, characterized in that The cloud-based transformation generates a cloud model distribution function fitting the output distribution curve according to the output distribution curve, including: Based on cloud transformation, the output distribution curve is identified, the peak position of each conventional power output data is determined, the peak position is used as the center of gravity position of the cloud, and the entropy of the cloud model is calculated based on the center of gravity position. Based on the center of gravity position and entropy, the normal cloud model formula is applied to generate a cloud model.

4. The method according to claim 2, characterized in that: The determining the residual data distribution based on the cloud model distribution function includes: The data values ​​of the part of the output distribution curve that is fitted with the cloud model are removed, and the data values ​​of the remaining part of the output distribution curve are used as residual distribution.

5. The method according to claim 2, characterized in that: The repeatedly constructing the cloud model number based on the residual data distribution until all the peaks of the output distribution curve are fitted includes: According to the residual data distribution, repeatedly identifying the peaks of the output distribution curve to construct a cloud model until all the peaks are fitted; After all the peaks are fitted, a number of corresponding cloud model distribution functions are obtained.

6. The method according to claim 2, characterized in that The distribution function for generating the distribution curve comprises: A plurality of corresponding cloud model distribution functions are superimposed and fitted to generate a distribution function of a distribution curve.

7. The method according to claim 2, characterized in that: The method further includes: when generating a cloud model for fitting the output distribution curve, using an expected curve of the cloud model for fitting.

8. The method according to claim 7, characterized in that When the expected curve of the cloud model is used for fitting, the peak position is determined, and the first trough and the first peak are respectively selected on the left and right sides around the center with the peak position as the center, and the difference between the first trough and the first peak is calculated, and the difference is compared with a preset threshold. If the difference is greater than the preset threshold, the value of the first peak is recorded. If the difference is less than the preset threshold, the next peak is searched until the difference is greater than the preset threshold. A value range of data points is constructed based on the recorded value of the first peak, and the data points within the value range are used as cloud droplets. Based on the cloud droplets, the initial value of entropy is calculated using the inverse cloud algorithm.

9. A conventional power output data fitting system based on cloud transformation fitting, characterized in that: include: A statistical unit, used to obtain conventional power output data of conventional power supplies of the power system, perform statistics on the conventional power output data, and obtain an output distribution curve of the conventional power output data; A fitting unit, configured to fit the conventional power supply output data according to the output distribution curve based on cloud transformation to obtain a distribution function of the output distribution curve; The output unit is used to use the distribution function as a curve fitting equation for fitting conventional power supply output data.

10. The system according to claim 9, characterized in that The cloud-based transformation, according to the output distribution curve, fits the conventional power output data to obtain the distribution function of the distribution curve, including: Based on the cloud transformation, a cloud model that fits the output distribution curve is generated according to the output distribution curve, and based on the cloud model, the residual data distribution is determined, and based on the residual data distribution, the cloud model distribution function is repeatedly constructed until all the peaks of the output distribution curve are fitted, and the distribution function of the distribution curve is generated.

11. The system according to claim 10, characterized in that The cloud-based transformation generates a cloud model distribution function fitting the output distribution curve according to the output distribution curve, including: Based on cloud transformation, the output distribution curve is identified, the peak position of each conventional power output data is determined, the peak position is used as the center of gravity position of the cloud, and the entropy of the cloud model is calculated based on the center of gravity position. Based on the center of gravity position and entropy, the normal cloud model formula is applied to generate a cloud model.

12. The system according to claim 10, characterized in that The determining the residual data distribution based on the cloud model distribution function includes: The data values ​​of the part of the output distribution curve that is fitted with the cloud model are removed, and the data values ​​of the remaining part of the output distribution curve are used as residual distribution.

13. The system according to claim 10, characterized in that The repeatedly constructing the cloud model number based on the residual data distribution until all the peaks of the output distribution curve are fitted includes: According to the residual data distribution, repeatedly identifying the peaks of the output distribution curve to construct a cloud model until all the peaks are fitted; After all the peaks are fitted, a number of corresponding cloud model distribution functions are obtained.

14. The system according to claim 10, characterized in that The distribution function for generating the distribution curve comprises: A plurality of corresponding cloud model distribution functions are superimposed and fitted to generate a distribution function of a distribution curve.

15. The system according to claim 10, characterized in that: When generating a cloud model for fitting the output distribution curve, the expected curve of the cloud model is used for fitting.

16. The system according to claim 15, characterized in that When the expected curve of the cloud model is used for fitting, the peak position is determined, and the first trough and the first peak are screened out on the left and right sides around the center with the peak position as the center, respectively. The difference between the first trough and the first peak is calculated, and the difference is compared with the preset threshold. If the difference is greater than the preset threshold, the value of the first peak is recorded. If the difference is less than the preset threshold, the next peak is searched until the difference is greater than the preset threshold. A value range of data points is constructed based on the recorded value of the first peak, and the data points within the value range are used as cloud droplets. Based on the cloud droplets, the initial value of entropy is calculated using the inverse cloud algorithm.

17. A computer device, characterized in that: include: one or more processors; a processor for executing one or more programs; When the one or more programs are executed by the one or more processors, the method according to any one of claims 1 to 8 is implemented.

18. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the method according to any one of claims 1 to 8 is implemented.

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