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Short-term power load forecasting method based on fuzzy clustering similar day

A technology of short-term power load and forecasting method, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve problems such as easy to fall into local extremum, affect application effect, over-fitting, etc., achieve simple structure, improve The effect of predicting speed and small amount of calculation

Inactive Publication Date: 2013-05-29
SHANGHAI JIAO TONG UNIV +2
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Problems solved by technology

However, the artificial neural network, especially the BP neural network, has shortcomings such as overfitting and easy to fall into local extremum, which affects the application effect.

Method used

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  • Short-term power load forecasting method based on fuzzy clustering similar day
  • Short-term power load forecasting method based on fuzzy clustering similar day
  • Short-term power load forecasting method based on fuzzy clustering similar day

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Embodiment

[0021] Such as figure 1 It is a flowchart of the present invention, and its steps are:

[0022] S1: Fuzzy clustering to find similar days; the specific S1 is:

[0023] S1.1: Aiming at the uncertain factors of meteorological types, the meteorological factors are subdivided into temperature, air pressure, wind speed, overcast and rainy, etc., together with the week type and date type, they constitute the influencing factors of similar days;

[0024] S1.2: Establish a fuzzy coefficient feature mapping table through fuzzy rules to realize the quantification of influencing factors; set U=[x 1 ,x 2 ,...,x n ] is n sample sets on the forecast day, each sample x j There are m feature indexes, namely sample x j can be expressed as x j =[x j1 ,x j2 ,...,x jm ] T ,(j=1,2,...,n). Establish fuzzy similarity matrix D. D=[d ij ] is used to represent the similarity matrix. In order to determine the fuzzy similarity matrix, the correlation coefficient method is used as follows: ...

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Abstract

The invention discloses a short-term power load forecasting method based on a fuzzy clustering similar day. The method includes that firstly meteorological factors are divided into temperature, pressure, wind speed, rain and other occasions and then constitute influence factors of the similar day with week styles and date styles, a fuzzy coefficient characteristic mapping table is built through fuzzy rules, clarification is carried out by the method of fuzzy clustering based on the preceding steps, the similar day is chosen according to clustering levels, according to obtained load data of the similar day, load sequences are projected to different scales and low frequency components are obtained by means of wavelet transformation, a support vector machine is optimized by means of a particle swarm optimization algorithm to achieve forecasting for a short-term power load low frequency portion, and forecasting for a high frequency portion is achieved by the method of weighted average. Eventually, application researches are carried out by means of the load data of a power grid in Shanghai city, and good forecasting effects can be achieved in weekdays, at weekends and in holidays.

Description

technical field [0001] The invention relates to the technical field of power load forecasting, in particular to a short-term power load forecasting method based on fuzzy clustering similar days. Background technique [0002] Power system load forecasting is an important content of power grid energy management system. Through accurate load forecasting, unit startup and shutdown can be arranged economically and rationally, reducing spinning reserve capacity, thereby reducing power generation costs and improving economic benefits. Therefore, it is of great significance to seek effective load forecasting methods and improve the accuracy of forecasting results. [0003] In the past ten years, many experts and scholars have proposed many forecasting methods based on nonlinear theory and its combination according to the characteristics of load forecasting. Among them, artificial neural network (ANN) has been widely used in power load forecasting due to its strong nonlinear mappin...

Claims

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Application Information

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IPC IPC(8): G06F19/00
Inventor 郑益慧王昕李立学于龙高明仕王书春张杨陈洪涛李柏成
Owner SHANGHAI JIAO TONG UNIV
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