Mix kernel machine learning based fan batch power prediction method

Abstract

The invention provides a mix nuclear machine learning based fan batch power prediction method. The method includes: establishing a wind-field fan offline historical database; dividing historical data of fans of a wind field of the wind-field fan offline historical database into 12 historical data sets; performing batch division processing on the fans in the wind field; taking the fans closest to the intra-batch wind power average value of each batch as batch sampled fans; establishing wind power prediction models of the batch sampled fans in the wind field; multiplying wind power predication values of the batch sampled fans by the number of the intra-batch fans and summarizing the wind power predication values of the batch sampled fans with the number of the intra-batch fans according to wind power prediction of the batch sampled fans through future weather information of the wind field to acquire the total wind power prediction value of the wind field. Weather data and wind power data are collected, wind power of the different batch sampled fans of the wind field is predicated, Gaussian kernel function and polynomial kernel function are combined to serve as kernel function, better applicability is achieved, the purpose of prediction of the wind power of the entire wind field is realized, and power dispatching of the wind field is guaranteed.

Description

technical field [0001] The invention belongs to the technical field of wind power forecasting in wind farms, and in particular relates to a batch power forecasting method for wind turbines based on mixed-core machine learning. Background technique [0002] In recent years, with the increasing scarcity of global petroleum energy, the warning of nuclear power caused by the earthquake in Japan, and the increase in greenhouse gas emissions, wind energy has become the world's growing energy demand. Therefore, it has become a trend to accelerate the development of safe and clean energy industries including wind power. In order to improve the ability of my country's power grid to receive wind power and improve the utilization efficiency of wind farms, my country's National Energy Administration promulgated the "Interim Measures for the Management of Wind Farm Power Forecasting and Forecasting" in July 2011. The operating wind farm must establish a wind power forecasting system and ...

AI Technical Summary

Problems solved by technology

The above method uses wind power error information, probability model distribution of wind power, and traditional data analysis methods to predict the wind power of the wind field and identify the prediction error, but it does not combine the distribution of the location of the wind turbines in the wind field wind power prediction

Images