Wind power generation prediction method and product based on a cost-oriented gradient rising regression tree

Abstract

The invention provides a wind power generation prediction method and product based on a cost-oriented gradient rising regression tree, and the method comprises the steps: obtaining the wind power historical data of a to-be-predicted place, and predicting an error loss function model; solving a negative gradient value of the wind power historical data with respect to the prediction error loss function model as a residual error estimation value; training a gradient rising regression tree model by using the residual estimation value to obtain a cost-oriented gradient rising regression tree model;and predicting the wind power generation amount by using the cost-oriented gradient rising regression tree model. According to the invention, a gradient rising regression tree method is adopted; according to the method, the cost-oriented loss function can be effectively processed; two means of regression tree and gradient lifting are used for bringing the actual cost generated by the prediction error into the model construction and prediction process, and the gradient rising regression tree method is used for executing the optimal point prediction, so that the cost-oriented loss function canbe effectively processed, and the cost difference caused by high-estimation and low-estimation prediction can be reduced.

Description

technical field [0001] The invention relates to the field of new energy power generation forecasting, in particular to a wind power generation forecasting method and product based on a cost-oriented gradient ascending regression tree. Background technique [0002] Renewable energy sources, such as wind and solar power, are important alternatives to traditional methods of generating electricity. In many countries, renewable energy accounts for a significant share of total energy supply. However, the uncertainty of renewable energy generation has brought great challenges to its large-scale application in the power system. Renewable energy generation forecasting is considered to be one of the most cost-effective solutions. Accurate prediction provides strong support for power grid operation and grid security assessment, and plays a key role in applications such as power market and economic dispatch. [0003] Much research has focused on forecasting renewable energy generatio...

AI Technical Summary

Problems solved by technology

Although traditional unbiased point forecasting has attracted much attention in some applications, in these applications, since forecast errors are unavoidable, underestimation of renewable energy generation (predicted value is smaller than actual value) and overestimation of renewable energy The impact of power generation (predicted value is greater than actual value) on cost is quite different

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