Real-time power prediction method of photovoltaic power station based on saga-fcm-lssvm model

Abstract

The present invention relates to a real-time power prediction method of a photovoltaic power station based on the SAGA-FCM-LSSVM model, which collects the power generation power of the photovoltaic power station in corresponding periods and the corresponding meteorological parameters on the weather station, and obtains meteorological-power parameter samples; performs daily meteorological-power parameter samples Preprocessing; according to the four statistical indicators combined with the fuzzy C-means clustering algorithm based on the simulated annealing genetic algorithm, the samples from the first day to the day before the forecast date are clustered; according to the meteorological characteristic values ​​of each cluster sample set , calculate the center point of each cluster meteorological feature value, and use the Euclidean distance to judge the category of the day to be predicted; use the parameter samples that belong to the same category as the day to be predicted to train the least squares support vector machine to obtain the training model; The meteorological parameters and power values ​​2 hours before the forecasted time of the forecasted day are input into the training model for real-time forecasting of the generated power at each moment of the forecasted day. The invention can predict the output power value of the photovoltaic power station at each moment in real time.

Description

technical field [0001] The invention relates to a real-time power prediction method of a photovoltaic power station based on a SAGA-FCM-LSSVM model. Background technique [0002] As an inexhaustible renewable energy, solar energy not only does not consume any earth resources, but also does not pollute the environment. Therefore, photovoltaic power generation has been widely concerned and applied under the attention of countries all over the world. However, the output of photovoltaic power generation is greatly affected by solar irradiance, temperature, humidity and other meteorological conditions. It will lead to a series of safety and stability problems in the power system. Therefore, with the large-scale application of photovoltaic arrays, accurate real-time prediction of photovoltaic power generation has become more and more important. [0003] In recent years, scholars have proposed various photovoltaic power prediction methods, which are mainly divided into two catego...

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Problems solved by technology

[0004] But these algorithms have some shortcomings: ANN model can be used for all classification and prediction problems, but it needs to specify various parameters related to network topology in the model, and it is easy to fall into local minimum

[0005] At present, there is no research on applying the SAGA-FCM-LSSVM algorithm to real-time power prediction of photovoltaic power plants in published literature and patents

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