Ultra-short-term prediction method of wind power based on self-learning composite data source

Abstract

The invention discloses a wind power ultra-short-term prediction method based on a self-learning composite data source, which includes inputting data to obtain autoregressive sliding average model parameters; The predicted results are obtained from the autoregressive moving average model, and post-evaluation is performed on the predicted results, that is, the error between the predicted value and the measured value is analyzed. If the predicted error is greater than the maximum allowable error, the model order and model parameters are estimated again. Through the prediction of wind power in the process of wind power generation, key information is provided for real-time scheduling of new energy power generation, day-ahead planning of new energy power generation, monthly plan of new energy power generation, evaluation of new energy power generation capacity and estimation of abandoned wind power. Through the introduction of composite data sources, the ultra-short-term prediction accuracy of wind power can be effectively improved, so as to achieve the purpose of effectively increasing the electricity consumption of new energy on the premise of ensuring the safe, stable and economical operation of the power grid.

Description

technical field [0001] The present invention relates to the technical field of wind power forecasting in the process of new energy power generation, in particular to a super-short-term wind power forecasting method based on self-learning composite data derived from a regression moving average model. Background technique [0002] Most of the large-scale new energy bases generated after my country's wind power enters the stage of large-scale development are located in the "three north regions" (Northwest, Northeast, and North China). Large-scale new energy bases are generally far away from the load center, and their power needs to be transmitted to load center for consumption. Due to the intermittence, randomness and volatility of wind and light resources, the output of wind power and photovoltaic power generation in large-scale new energy bases will fluctuate in a large range, which will further lead to fluctuations in the charging power of the transmission network, which will...

AI Technical Summary

Problems solved by technology

Due to the intermittence, randomness and volatility of wind and light resources, the output of wind power and photovoltaic power generation in large-scale new energy bases will fluctuate in a large range, which will further lead to fluctuations in the charging power of the transmission network, which will affect the safety of power grid operation. raises a series of questions

Images