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Electric power system grey load prediction method based on exponential smoothing

An exponential smoothing method and exponential smoothing technology, which can be used in forecasting, data processing applications, instruments, etc., and can solve problems such as reducing the accuracy of gray forecasting models.

Inactive Publication Date: 2016-03-30
FUZHOU UNIVERSITY
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Problems solved by technology

[0005] Because the known electricity demand is affected by political, economic, climate and other related factors, the observation data sequence is random and uncertain, which reduces the accuracy of the gray prediction model.

Method used

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  • Electric power system grey load prediction method based on exponential smoothing
  • Electric power system grey load prediction method based on exponential smoothing
  • Electric power system grey load prediction method based on exponential smoothing

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Embodiment Construction

[0044] The technical solution of the present invention will be specifically described below in conjunction with the accompanying drawings.

[0045] Such as figure 1 As shown, a kind of gray load forecasting method of power system based on exponential smoothing of the present invention is realized according to the following steps:

[0046] Step S1: Get the original data column x (00) =[x (00) (1),x (00) (2), x (00) (3)...x (00) (n)];

[0047]Step S2: Select the number of times of exponential smoothing according to the characteristics of the trend of the time series. For the selection of times of exponential smoothing, follow the following rules: when the time series shows a smooth trend, use one-time exponential smoothing method; when the time series shows a linear trend, use two-time smoothing. Sub-exponential smoothing method; when the time series shows a nonlinear trend, it is estimated by three exponential smoothing methods; the three exponential smoothing models are:...

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Abstract

The invention relates to the electric power system load prediction field and especially relates to a grey load prediction method based on exponential smoothing. Aiming at conditions of randomness and uncertainty possessed by an original date sequence of a traditional grey model, the invention provides an improved method combining the exponential smoothing. Through using the exponential smoothing, weighting is performed on the original date sequence to generate a new sequence. Historical data which has large volatility and easily generates a large error is smoothed so that the sequence becomes a sequence which has a high regularity and shows an exponential function. Simultaneously, a background value of a grey model is optimized and a prediction error of the model is further reduced. Through combining the two prediction models, prediction precision is greatly increased; and the method is suitable for correlation departments of an electric power system and the like and is used to solve a load prediction problem in an electric power program.

Description

technical field [0001] The invention relates to the field of power system load forecasting, in particular to a gray load forecasting method for power systems based on exponential smoothing. Background technique [0002] With the vigorous development of the electric power industry and the continuous deepening of the reform of my country's electric power system, the research on the theory and method of electric load forecasting is becoming more and more important. Load forecasting is the process of analyzing the law of load changes based on historical load data and related influencing factors, comprehensively considering the reasons affecting load changes, using certain load forecasting models and research methods, and estimating the load value in a certain period of time in the future. . Its main task is to predict the spatial and temporal distribution of future electrical loads. The main purpose of power load forecasting is to predict and provide the development and level ...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06
CPCG06Q10/04G06Q50/06
Inventor 金涛张怡真魏海斌
Owner FUZHOU UNIVERSITY
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