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FA-FFCM-based station terminal load prediction method

A load forecasting and terminal technology, applied in forecasting, calculation models, biological models, etc., can solve problems such as low accuracy and slow calculation speed

Pending Publication Date: 2021-05-07
佳源科技股份有限公司
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Problems solved by technology

[0004] In order to solve the deficiencies in the prior art, the present invention provides a FA-FFCM-based station terminal load forecasting method, which solves the problems of low accuracy and slow calculation speed of the existing load forecasting method

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  • FA-FFCM-based station terminal load prediction method
  • FA-FFCM-based station terminal load prediction method
  • FA-FFCM-based station terminal load prediction method

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Embodiment Construction

[0054]The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments, so that those skilled in the art can implement it with reference to the description.

[0055] Such as figure 1 As shown, a FA-FFCM-based station terminal load prediction method includes steps:

[0056] Step 1: Obtain historical electricity consumption information data, and combine meteorological data to obtain historical load and daily feature vectors as clustering samples;

[0057] The daily feature vector is represented by Z, Z=(F, T L ,T H , R, S), where,

[0058] 1) F is the date type, the date type is the same, the similarity is 1; the same is the weekday / weekend but the date type is different, the similarity is 0.6; the date type and the weekday / weekend are different is 0.3.

[0059] 2)T L 1 is the temperature below 10°C, 2 is the temperature between 10°C and 20°C, and 3 is the temperature above 20°C;

[0060] 3)T H is the highest te...

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Abstract

The invention discloses a FA-FFCM-based station terminal load prediction method, and the method comprises the steps: 1, obtaining historical power utilization information data, and obtaining a historical load and daily feature vector as a clustering sample in combination with meteorological data; 2, clustering the clustering samples based on an FA-FFCM (firefly-fast fuzzy C-means clustering) method, determining an optimal clustering number, finding out a class to which a day before the prediction day belongs, and determining the class as a similar day; and 3, taking the load of the similar day as a prediction sample, and predicting the load at the hourly moment of the prediction day through a support vector machine. The method improves the precision of load prediction, has a good adaptive characteristic, and can be applied to the load prediction of a power system.

Description

technical field [0001] The invention relates to the technical field of station terminal load forecasting, in particular to a station terminal load forecasting method based on FA-FFCM. Background technique [0002] Station terminal load forecasting often uses clustering algorithms, including fuzzy clustering algorithm, K-Means algorithm, etc.; classic fuzzy clustering algorithm (fuzzy C-means clustering algorithm, FCM), for the objective function (C-means function) The optimal classification of the data set is obtained by repeatedly iteratively correcting the cluster membership matrix and cluster center. According to the maximum membership degree criterion, the data is assigned to the class with the largest membership degree; the cluster center position is updated according to the membership degree of the data samples in each class. Iterative loops are repeated so that samples with large similar rows in the data set are classified into the same class, while samples with larg...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/26G06K9/62G06N3/00
CPCG06Q10/04G06Q50/26G06N3/006G06F18/23213
Inventor 王二王孙侃卜权丁旸
Owner 佳源科技股份有限公司
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