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Short-term wind power prediction method based on covariance

A wind power forecasting and wind power technology, applied in neural learning methods, genetic models, genetic rules, etc., can solve problems such as slow running of forecasting programs and complex combined forecasting models

Inactive Publication Date: 2016-07-20
ELECTRIC POWER RES INST OF GUANGXI POWER GRID CO LTD
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Problems solved by technology

Although the accuracy of this method is high, the combined forecasting model is complex and the forecasting program runs slowly

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  • Short-term wind power prediction method based on covariance
  • Short-term wind power prediction method based on covariance
  • Short-term wind power prediction method based on covariance

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Embodiment Construction

[0061] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the examples of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0062] Such as figure 1 As shown, a short-term wind power prediction method based on covariance includes the following steps:

[0063] Step 1: Obtain real-time data of wind farms, including real-time wind power data, numerical weather forecast data, real-time Internet access data, and wind tower meteorological station data; real-time data acquisition starts from 0:00 the next day, and predicts the wind power for the next 72 hours, time-resolved The rate is 15 min...

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Abstract

The invention belongs to the technical field of wind power forecast and specifically relates to a short-term wind power prediction method based on covariance. The method is characterized by obtaining data, comprising real-time wind power data, numerical weather prediction data, real-time network data and wind tower meteorological observatory data, of a wind power plant in real time; carrying out covariance calculation on the numerical weather prediction data, the real-time network data and the wind tower meteorological observatory data obtained in real time, wherein the covariance calculation prediction carries out wind power prediction based on an NWP space difference model, a BP neural network model and an LS-SVM model respectively; and carrying out error analysis on the real-time wind power data and covariance calculation prediction three-model data through weight distribution combination to obtain a final prediction result. The precision of the used prediction algorithm is higher than that of a single physical model and a statistical model, thereby facilitating development of wind power prediction. The method can solve the problem of large error of the single prediction model, is high in predication precision and meets grid-connection requirements of the wind power plant.

Description

technical field [0001] The invention belongs to the technical field of wind power prediction, in particular to a short-term wind power prediction method based on covariance. Background technique [0002] Wind energy is currently the renewable energy with the greatest potential for large-scale commercial development and utilization. Wind power generation is an effective way to utilize wind energy on a large scale, and it is also the most realistic choice for my country's energy and power sustainable development strategy. With the large-scale connection of wind farms to the main power grid, the power fluctuations of wind farms will have a certain impact on the stability of grid voltage and frequency, which in turn will affect the safe and stable operation of the grid. The power generation and consumption of the grid needs to be balanced at all times, and wind energy is an intermittent energy source. The active power output of wind farms changes with the change of wind speed, w...

Claims

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Application Information

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IPC IPC(8): G06Q50/06G06N3/08G06F17/15G06F17/16G06N3/12
CPCG06Q50/06G06F17/15G06F17/16G06N3/084G06N3/126
Inventor 张阁高立克杨艺云肖静李小伟黎敏肖园园郭敏秦丽娟
Owner ELECTRIC POWER RES INST OF GUANGXI POWER GRID CO LTD
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