Offshore wind power ultra-short-term prediction method based on LSTM deep learning network

Abstract

The invention discloses an offshore wind power ultra-short-term prediction method based on an LSTM deep learning network. The offshore wind power ultra-short-term prediction method comprises the following steps: firstly, carrying out various analysis on a wind speed time sequence, and combining various analysis results to extract the characteristic quantity of offshore wind power ultra-short-term prediction, thereby avoiding overfitting of a model caused by excessive input quantity; carrying out modeling prediction on the offshore wind speed by utilizing a deep learning method; and combining the actual wind speed-power curve of the offshore wind generating set to obtain an offshore wind power ultra-short-term predicted value. According to the offshore wind power ultra-short-term prediction method, the characteristic quantity of the ultra-short-term prediction of the offshore wind power is extracted according to the characteristics of the offshore wind speed, so that the input of the characteristic quantity with low correlation is avoided, and the prediction efficiency is improved while the offshore wind power prediction precision is improved.

Description

technical field [0001] The invention belongs to the field of offshore wind power forecasting, and in particular relates to an ultra-short-term forecasting method for offshore wind power. Background technique [0002] Accurate offshore wind power prediction is of great significance to system scheduling, stable operation and economic benefit improvement. Due to the short development time of offshore wind power, the short-term power forecasting technology mostly takes onshore wind power as the research object, and the offshore wind power forecasting technology is still in the initial stage of research and development. In order to make the prediction model applicable to the sea, it is necessary to consider the characteristics of the sea when selecting a suitable prediction model. [0003] Wind power in any region has strong randomness and instability, but there are obvious differences between onshore wind power and offshore wind power: my country's electricity load and onshore ...

AI Technical Summary

Problems solved by technology

However, the high specific heat capacity of seawater, the thermal effect of wind flow at sea, and the amplified wake effect make the physical modeling calculation of offshore wind farms very cumbersome; and due to differences in geographical environments, there are certain defects in the flexibility and generalization of physical modeling. Not suitable for ultra-short-term power forecasting at sea

Images