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Power load prediction method based on optimal selection of typical daily load curve

A technology of power load and forecasting method, which is applied in the field of power load forecasting based on the optimal selection of typical daily load curves, which can solve the problems of reducing the accuracy of subsequent power load forecasting, the lack of accuracy in the selection of typical daily loads, and the lack of typicality without considering the compactness of sample points In order to achieve the effect of optimizing the clustering effect, improving the clustering accuracy and reducing the operation cost

Pending Publication Date: 2022-02-11
SHANGHAI DIANJI UNIV
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AI Technical Summary

Problems solved by technology

However, the existing possible fuzzy C-means clustering algorithm (PCM) pays attention to typicality without considering the compactness of sample points, which will result in cluster consistency; the probabilistic fuzzy C-means algorithm (PFCM) overcomes the PCM cluster consistency And the shortcomings of FCM's sensitivity to distorted data, but the calculation of the objective function needs to calculate the parameters in FCM first, which increases the calculation cost
This will lead to a lack of accuracy in the selection of typical daily loads, thereby reducing the accuracy of subsequent power load forecasts

Method used

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  • Power load prediction method based on optimal selection of typical daily load curve
  • Power load prediction method based on optimal selection of typical daily load curve
  • Power load prediction method based on optimal selection of typical daily load curve

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Embodiment

[0063] Such as figure 1As shown, a power load forecasting method based on optimal selection of typical daily load curves includes the following steps:

[0064] S1. Acquiring load raw data;

[0065] S2. Preprocessing the original load data to obtain characteristic indicators, specifically performing standardization processing and distortion data screening processing on the original load data in sequence;

[0066] S3. Using the improved PFCM algorithm combined with the fuzzy linear discriminant method (FLDA) to perform cluster analysis on the preprocessed load raw data and determine the cluster center matrix;

[0067] S4. Determine the base date. According to the Pearson correlation coefficient method, calculate the correlation between the base date of each month and each cluster center, and determine the category to which each month belongs. The base date is specifically the monthly average load day;

[0068] S5. Taking the sample point with the highest degree of membership i...

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Abstract

The invention relates to a power load prediction method based on optimal selection of a typical daily load curve. The method comprises the following steps: obtaining load original data; preprocessing the load original data to obtain a characteristic index; combining an improved PFCM algorithm with a fuzzy linear discriminant method (FLDA) to perform clustering analysis on the preprocessed original load data, and determining a clustering center matrix; determining a reference date, respectively calculating the correlation between the reference date of each month and each clustering center according to a Pearson correlation coefficient method, and determining the category to which each month belongs; and taking the sample point with the maximum membership degree in each class as a typical daily load curve, and performing power load prediction based on the typical daily load curve to obtain a corresponding prediction result. Compared with the prior art, by improving the PFCM algorithm and combining the FLDA method, the typical daily load curve closer to the clustering center can be optimized and selected, and therefore the accuracy of power load prediction is effectively improved.

Description

technical field [0001] The invention relates to the technical field of power load forecasting, in particular to a power load forecasting method based on optimal selection of typical daily load curves. Background technique [0002] Power load forecasting is an important part of power system planning and the basis of power system economic operation, which is extremely important to power system planning and operation. The key to load forecasting is to collect a large amount of historical data, establish a scientific and effective forecasting model, use effective algorithms, conduct a large number of experimental studies based on historical data, sum up experience, and constantly revise models and algorithms to truly reflect load changes. law. [0003] Before power load forecasting, it is often necessary to select a typical daily load curve in advance. The traditional method is to select a typical daily load curve with a certain characteristic index, or directly select the dail...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06Q10/04G06Q50/06
CPCG06Q10/04G06Q50/06G06F18/2321G06F18/2132G06F18/241
Inventor 邬浩泽朱晨烜王朝威
Owner SHANGHAI DIANJI UNIV
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