Instantaneous Wind Condition Forecasting Method in Mountainous Areas Based on Computational Fluid Dynamics and Machine Learning

Abstract

The present invention provides a method for forecasting instantaneous wind conditions in mountainous areas based on computational fluid dynamics and machine learning. Real-time transient wind forecast data. The real-time transient wind data forecast model is established through the following steps: establish a digital geometric model of the wind field in the mountainous area to be forecast; use computational fluid dynamics method to perform numerical calculations of all wind directions and full wind speeds on the digital geometric model of the wind field in the mountainous area, and establish a wind field in the mountainous area. The wind environment simulation database of the field; the simulated wind data of the measured points and the points to be predicted are extracted; the real-time transient wind data forecast model is established through machine learning. The above-mentioned technical solution utilizes the ability of computational fluid dynamics simulation technology to finely capture complex wind conditions in three-dimensional space, and the ability of machine learning algorithms to reconstruct high-dimensional data matrices; it has more refined spatial forecasting capabilities and more accurate forecasting accuracy.

Description

technical field [0001] The invention relates to the technical field of wind power generation, in particular to a method for forecasting instantaneous wind conditions in mountainous areas based on computational fluid dynamics and machine learning. Background technique [0002] When a wind power generation system converts wind energy into electric energy, the wind energy state and evolution law of its location will directly determine the basic parameters such as wind energy conversion efficiency, power generation and service life of the wind power generation system. Therefore, the fine wind environment forecasting technology of wind farms is of great significance for the planning, design and refined operation of wind farms. [0003] At present, the widely used wind resource forecasting technology in wind fields is mainly mesoscale meteorological forecasting, and the forecast results are measured by wind towers or SCADA (supervision and control system of data acquisition). The...

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Problems solved by technology

[0006] Aiming at the deficiencies in the existing technology, the present invention proposes a method for forecasting instantaneous wind conditions in mountainous areas based on computational fluid dynamics and machine learning to solve the problems existing in the prior art in the forecasting of wind conditions in mountainous areas, and the fineness and accuracy of the forecast. Insufficient technical issues

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