A photovoltaic power station output power prediction method based on wavelet transformation and an extreme learning machine

Abstract

The invention relates to a photovoltaic power station output power prediction method based on wavelet transformation and an extreme learning machine. Firstly, a prediction data set of a photovoltaic power station is extracted from a historical power generation and meteorological environment parameter monitoring data set of the photovoltaic power station; Secondly, preprocessing the photovoltaic power station prediction data set; then, a PCA algorithm is adopted to extract features from historical power data of the photovoltaic power station. Binary classification is carried out through a K-means algorithm, and the classification is divided into a smooth type and a fluctuation type; and finally, obtaining meteorological characteristic parameters of a to-be-predicted day through the NWP to generate a test set, judging the type of the test set according to the Euclidean distance, and traversing to find an optimal training set. And the output power of the photovoltaic power station is predicted by directly utilizing an extreme learning machine network in a smooth manner. And the fluctuation type needs to perform feature extraction on each object of the data through a WT algorithm, predict one by one and reconstruct predicted values. According to the photovoltaic power station output power prediction method based on the extreme learning machine, the accuracy of photovoltaic power station output power prediction can be effectively improved.

Description

technical field [0001] The invention relates to a photovoltaic power station output power prediction technology, in particular to a photovoltaic power station output power prediction method based on wavelet transform and extreme learning machine. Background technique [0002] Since the factors affecting photovoltaic power generation are unstable, the timing changes of the output power of photovoltaic power stations are not smooth, and have high volatility, intermittent and randomness. When a large number of photovoltaic power stations are integrated into the existing In some national grid systems, it will have a great impact on the balance of the entire power generation system, and at the same time bring huge challenges to the safe and stable operation of the entire power system and the production and utilization of electric energy. In-depth research on the output power prediction of photovoltaic power stations , has high academic research and practical application value. I...

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Problems solved by technology

[0005] At present, there is no research on the simultaneous introduction of WT and K-means algorithms into extreme learning machines to predict the output power of photovoltaic power plants in published literature and patents

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