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Power grid saturation load prediction method and device and terminal equipment

A load forecasting and power grid load technology, applied in forecasting, data processing applications, instruments, etc., can solve the problem of low accuracy of load forecasting results, and achieve the effect of solving pseudo-regression problems, reducing external factors, and improving calculation speed.

Pending Publication Date: 2021-04-20
INST OF ECONOMIC & TECH STATE GRID HEBEI ELECTRIC POWER +2
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Problems solved by technology

[0004] The embodiment of the present invention provides a power grid saturation load prediction method, which aims to solve the problem of low accuracy of the power demand saturation time and load prediction results of the existing prediction methods

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  • Power grid saturation load prediction method and device and terminal equipment
  • Power grid saturation load prediction method and device and terminal equipment
  • Power grid saturation load prediction method and device and terminal equipment

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specific Embodiment approach

[0036] Optionally, in a specific implementation of the grid saturation load prediction method provided in the embodiment of the present invention, the Granger causality analysis model is an F statistic model;

[0037] The F statistic model is:

[0038]

[0039] Among them, R r is the residual sum of squares corresponding to the unconstrained regression model, R u is the residual sum of squares corresponding to the constrained regression model, q is the maximum lag period of external factors, p is the maximum lag period of the historical load sequence, and n is the number of influencing factors;

[0040] Among them, the unconstrained regression model is:

[0041]

[0042] Among them, y t is the load value of the power grid to be predicted under unconstrained conditions at time t, α 0 is the load of the power grid to be predicted at the initial moment, α k is the load coefficient of the historical load sequence in the kth lag period, y t-k is the load value of the po...

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Abstract

The invention provides a power grid saturation load prediction method and apparatus, and a terminal device. The method comprises the steps of obtaining a current value of an influence factor of a to-be-predicted power grid based on a preset Granger causality analysis result; inputting the current value of the influence factor of the to-be-predicted power grid into a preset Gaussian process regression model, and determining a load prediction value of the to-be-predicted power grid; determining a future planning value of an influence factor of the to-be-predicted power grid based on the load prediction value; and inputting the future planning values of the influence factors of the to-be-predicted power grid into a preset Gaussian process regression model, and determining a saturation load prediction value of the to-be-predicted power grid. According to the power grid saturation load prediction method and apparatus, and the terminal device provided by the invention, the Granger causality analysis is introduced to screen the external factors influencing the power grid load, so that unnecessary external factors are reduced, the problem of pseudo regression generated in the power grid saturation load prediction process is solved. And the accuracy of power grid saturation load prediction is improved.

Description

technical field [0001] The invention relates to the field of grid planning, in particular to a method, device and terminal equipment for grid saturation load prediction. Background technique [0002] Saturation interval prediction is aimed at predicting the power demand of the final scale of the power grid, which can provide guidance for power grid planning and medium and long-term power market transactions. At present, the existing saturated interval prediction technology mainly uses the existing interval time series for extrapolation, and combines the time criterion of the saturated interval to realize the saturated interval prediction, such as: growth curve model; or uses common economic or demographic factors, combined with Planning value, establishing a regression model to realize saturated interval prediction, for example: cointegration regression model. [0003] However, there are many external factors that affect power demand, and it is easy to generate false regres...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06
Inventor 赵阳贺春光安佳坤范文奕杨书强孙鹏飞郭伟邵华张菁胡诗尧檀晓林黄凯韩璟琳赵子珩翟广心胡平董昕赵辉李铁良
Owner INST OF ECONOMIC & TECH STATE GRID HEBEI ELECTRIC POWER
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