A Power Interval Prediction Method Based on Kernel Extreme Learning Machine Model

Abstract

The present invention belongs to the field of power prediction of wind power generation and particularly relates to a method for predicting a wind power interval based on a particle swarm optimization nucleus limit learning machine model. The method comprises: carrying out data preprocessing, i.e. preprocessing historical data in SCADA according to correlation between a wind speed and power; initializing a KELM model parameter and carrying out calculation to obtain an initial output weight betaint; initializing a particle swarm parameter; constructing an optimization criterion F according to an evaluation index and carrying out particle swarm optimization searching to obtain a model optimal output weight betabest; and bringing test data into a KELM model formed by betabest to obtain a wind power prediction interval and evaluating each index of the prediction interval. The method is easy for engineering realization; a good prediction result can be obtained; not only can a future wind power possible fluctuation range be described, but also reliability of the prediction interval is effectively evaluated, possible fluctuation intervals of wind power at different confidence levels are given out and reference is better provided for a power system decision maker.

Description

technical field [0001] The invention belongs to the field of wind power generation power prediction, in particular to a method for wind power interval prediction based on a particle swarm optimization kernel extreme learning machine model. Background technique [0002] As a clean and renewable energy, wind energy has been widely used all over the world. It has the characteristics of safety, cleanness, abundance and huge resources. Wind power generation is the main form of human utilization of wind energy. However, the wind itself has strong randomness and instability, which will lead to violent fluctuations in wind power. Especially in recent years, the wind power industry has developed rapidly, and the installed capacity has continued to increase. Under the background of large-scale wind power access to the grid, the random instability of wind power power has brought severe challenges to the safe and stable operation of the power system. The wind power forecast is used to ...

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Problems solved by technology

However, in actual engineering applications, ELM also has some shortcomings: linear inseparability may occur when the calculation data is low-dimensional, which affects the prediction effect, resulting in random fluctuations in the output of the ELM model, and unsatisfactory stability and generalization capabilities.

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