Wind power short-term prediction method

Abstract

The invention discloses a wind power short-term prediction method, which comprises the following steps: S1, acquiring the wind speed, wind direction, temperature, humidity and pressure information of a wind power plant, the time resolution of data being 15 minutes, and cleaning the acquired wind power plant data to eliminate unreasonable data; s2, performing normalization processing on the cleaned data, and then performing variational mode decomposition on the normalized wind speed data; s3, taking the wind speed modal component, the wind direction, the temperature, the humidity and the pressure intensity information after variational modal decomposition as input data of a time convolutional network, taking actual power data as target output, and constructing a short-term wind power prediction model; and S4, accurately describing the distribution characteristic of the wind power short-term prediction error by using a Gaussian mixture model, and calculating the uncertainty distribution characteristic of the wind power short-term prediction error according to the distribution characteristic. According to the method, the prediction precision of the wind power can be effectively improved, and the uncertainty distribution characteristic of the short-term prediction error of the wind power can be accurately calculated.

Description

technical field [0001] The invention belongs to the technical field of wind power energy, and in particular relates to a short-term prediction method of wind power. Background technique [0002] The development and utilization of clean energy has become a feasible solution for human beings to deal with climate change and environmental pollution. As a kind of clean energy, wind energy has been widely developed and utilized in recent years. Due to the intermittence, randomness and uncertainty of wind power, large-scale wind power grid integration has brought great challenges to the safe and stable operation of the grid. Accurately predicting the output power of wind power has become an effective way to ensure the safe and stable operation of the power grid and improve wind power consumption. According to different forecasting mechanisms, wind power forecasting can be divided into two categories: physical forecasting methods and statistical forecasting methods. [0003] The p...

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Problems solved by technology

However, there is a lack of time variables in these statistical models, and there is no way to describe the time correlation characteristics between data, which limits the improvement of the prediction accuracy of statistical models.

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