Bus net load prediction method based on ARIMA and phase-space reconstruction

A phase space reconstruction and prediction method technology, applied in the direction of prediction, instrumentation, data processing applications, etc., can solve the problems of ineffective analysis of the nonlinear components of the net load, weak anti-noise ability, large amount of calculation, etc., and achieve strong tracking and The effect of capturing ability, strong anti-noise ability, and small amount of calculation

Active Publication Date: 2018-04-20
HOHAI UNIV
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Problems solved by technology

The disadvantages of these methods are: large amount of calculation, low reliability and weak anti-noise ability, and cannot effectively analyze the nonlinear components of the payload

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  • Bus net load prediction method based on ARIMA and phase-space reconstruction
  • Bus net load prediction method based on ARIMA and phase-space reconstruction
  • Bus net load prediction method based on ARIMA and phase-space reconstruction

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Embodiment example

[0108] In order to verify the effectiveness of the phase space reconstruction SVR (CC-SVR) net power prediction model, the present invention selects the total bus power and distributed photovoltaic output of a city in May 2017 for ten days, based on Eviews8.0 and MATLAB8.2 To verify the proposed model, the real-time monitoring sampling period of data is 5 minutes. Select the data of the first seven days in the sample for forecasting model modeling, and the data of the next three days as the forecast data. The forecast results of the last three days of the data sample are given below and the results are analyzed.

[0109] (1) ARIMA(1,1,1,) model prediction results

[0110] Modeled with payload data from the previous seven days of the sample, figure 2 is the prediction result of the prediction model for the net load of the next three days in the sample.

[0111] It can be seen from the figure that the ARIMA model can better predict the overall trend of net load changes, that ...

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Abstract

The invention discloses a bus net load prediction method based on ARIMA and phase-space reconstruction (SVR). The method comprises the following steps: 1, determining the parameters of an ARIMA modeland establishing the ARIMA model; 2, subjecting the residual sequence of the ARIMA model to SVR based on a CC method; 3, based on a reconstructed residual matrix, creating a net load nonlinear part prediction model by using SVR; and 4, obtaining a final result of net load prediction. The prediction model provided by the invention makes full use of the good tracking ability of the ARIMA model to linear changes and the good capture ability of the SVR to nonlinear changes. The SVR via CC method is based on a theory that the embedding space dimension and time delay are interdependent, has low calculated quantity, high reliability and good anti-noise performance. The SVR model has good tracking and catching ability for nonlinear changes, and can effectively analyze the nonlinear components of the net load.

Description

technical field [0001] The invention relates to a load forecasting method in the technical field of power system automation, in particular to a bus net load forecasting method based on ARIMA and phase space reconstruction SVR. Background technique [0002] The power system needs to provide users with safe and reliable power. Due to the fact that power itself cannot be stored in large quantities, real-time power supply and load balance are one of the key factors for the stable operation of the power system, and load forecasting is an important means to ensure this balance. . With the continuous development of new energy technologies, distributed power sources such as wind power and photovoltaics are integrated into the power grid, and their strong randomness and volatility will further threaten the stable operation of the power grid. Economic operation is of great significance. The net load of the bus is affected by work and rest time, production process, climate, holidays ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06
CPCG06Q10/04G06Q50/06Y04S10/50
Inventor 梅飞刘皓明李玉杰袁晓玲王力
Owner HOHAI UNIV
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