An ultra-short-term wind power forecasting method and system considering meteorological characteristics

Abstract

This disclosure proposes a method and system for ultra-short-term wind power forecasting considering meteorological characteristics, including: decomposing the wind power sequence by using the ensemble empirical mode decomposition method to obtain the corresponding IMF sub-layer; performing dimensionality reduction and extraction of wind power characteristics based on PCA; Recombine the IMF sublayer and dimensionality reduction features into a feature vector; recombine the current feature vector into the LSTM network for wind power prediction. Compared with other forecasting methods, this method has a better effect in wind power forecasting.

Description

technical field [0001] The present disclosure relates to the fields of environmental protection and energy technology, and in particular to an ultra-short-term wind power prediction method and system considering meteorological characteristics. Background technique [0002] With the sharp decline of fossil energy reserves and the increasingly prominent ecological and environmental problems, wind power, as a renewable clean energy, is developing rapidly in the world. In recent years, with the continuous increase in the proportion of wind power installed capacity, the impact of wind power volatility and randomness on the power system has become increasingly obvious, and the ensuing phenomenon of wind curtailment has become increasingly serious. Therefore, in order to suppress the impact of strong randomness of wind energy, ensure the safe operation of the power grid, and increase the rate of wind energy absorption, it is necessary to accurately predict wind power. [0003] The...

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Problems solved by technology

However, none of the above patents has fully extracted the characteristics of meteorological information, and the prediction accuracy needs to be improved

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