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Microgrid short-term load prediction method based on empirical mode decomposition

An empirical mode decomposition, short-term load forecasting technology, applied in forecasting, instrumentation, data processing applications, etc., can solve the problems of unconsidered, long forecasting time, and low forecasting result accuracy.

Pending Publication Date: 2019-11-19
SHANGHAI UNIVERSITY OF ELECTRIC POWER
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Problems solved by technology

Most of the above methods predict the power load curve as a whole when modeling, resulting in slow prediction speed and long prediction time; and the comprehensive influence of various meteorological factors is not considered, resulting in low prediction accuracy

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  • Microgrid short-term load prediction method based on empirical mode decomposition
  • Microgrid short-term load prediction method based on empirical mode decomposition
  • Microgrid short-term load prediction method based on empirical mode decomposition

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Embodiment Construction

[0068] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0069] Such as figure 1 As shown, a short-term load forecasting method for microgrid based on empirical mode decomposition includes the following steps:

[0070] S1. Collect the original load data and perform preprocessing to obtain the total load curve;

[0071] S2. Using the empirical mode decomposition method, the total load curve is decomposed into a trend load curve and a fluctuating load curve;

[0072] S3. According to the trend load curve, the trend load forecast value is obtained by constructing a gray forecast model based on the amplitude compression method;

[0073] S4. According to the fluctuating load curve, by establishing a MIC historical matrix considering the correlation of similar load days, and simultaneously establishing a UTCI historical library including meteorological factors and geographical location factors, the flu...

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Abstract

The invention relates to a microgrid short-term load prediction method based on empirical mode decomposition, and the method comprises the steps: S1, collecting original load data, carrying out the preprocessing, and obtaining a total load curve; S2, decomposing the total load curve into a trend load curve and a fluctuation load curve through empirical mode decomposition; S3, constructing a grey prediction model based on an amplitude compression method to obtain a trend load prediction value; S4, establishing an MIC historical matrix considering load similar day relevance and a UTCI historicallibrary including meteorological and geographical location factors, and obtaining a fluctuation load prediction value; and S5, reconstructing the trend load prediction value and the fluctuation loadprediction value, and obtaining a short-term load prediction value to control the working state of the distributed power supply in the microgrid. Compared with the prior art, the method has the advantages that the influence of meteorological and geographical location factors on load prediction is comprehensively considered on the basis of analyzing load characteristics, high prediction accuracy isachieved, and prediction speed is increased.

Description

technical field [0001] The invention relates to the technical field of load forecasting, in particular to a short-term load forecasting method for a microgrid based on empirical mode decomposition. Background technique [0002] Microgrid short-term load forecasting is the key to realizing microgrid intelligent scheduling and optimizing energy efficiency management. Controlling the output of distributed power sources in microgrids according to short-term load forecasting values ​​is of great significance for safe power supply, economic cost reduction, and stable operation of microgrids. . The load of the user-side microgrid has the characteristics of a small base and is easily affected by external factors and fluctuates violently, and the nonlinear complexity of its curve is much higher than that of the large power grid load curve. In actual forecasting, the existing forecasting models often result in insufficient forecasting accuracy due to ignoring the influence of multipl...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06
CPCG06Q10/04G06Q50/06Y02A90/10
Inventor 薛阳张宁吴海东俞志程叶晓康华茜孙越李蕊
Owner SHANGHAI UNIVERSITY OF ELECTRIC POWER
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