Photovoltaic power station short-term power prediction method

Abstract

The invention discloses a photovoltaic power station short-term power prediction method, and the method comprises the steps: selecting meteorological factors such as season types, weather types and irradiation intensity / temperature as an input data set according to the historical output power data and numerical weather prediction data of a photovoltaic power station; preprocessing the input data set, extracting a day type feature vector, and clustering the day type feature vector by adopting a K-Means clustering method to obtain K different day type results; according to the numerical weatherprediction data of the prediction day, determining a day type to which the prediction day belongs, obtaining a data sample set of the most similar day of n days in the day type to which the predictionday belongs based on a similar day theory, taking the data sample set as prediction model training data, performing training modeling on the training data set by adopting a random forest regression prediction algorithm, and establishing a photovoltaic power station short-term power prediction model; and calling a photovoltaic power station short-term power prediction model based on the predictionday numerical weather prediction data, and obtaining a short-term power prediction result of the photovoltaic power station in the prediction day.

Description

technical field [0001] The invention relates to the technical field of photovoltaic power generation prediction, in particular to a method for short-term power prediction of a photovoltaic power station. Background technique [0002] With the rapid consumption of traditional energy and the enhancement of environmental awareness, clean, environmentally friendly and sustainable renewable energy has gained widespread attention. Photovoltaic power generation, as a relatively mature renewable energy power generation method, has been well developed and utilized. With the rapid development of renewable energy power generation technology, large-scale photovoltaic power plants will be connected to the grid. Due to the randomness and volatility of photovoltaic power generation output, the grid connection of large-scale photovoltaic power plants will affect the stability of the power grid. In order to achieve the balance of grid power, photovoltaic power generation enterprises need t...

AI Technical Summary

Problems solved by technology

[0003] After searching the existing technical literature, it is found that the commonly used prediction methods include support vector machines, neural networks, time series and other methods, but a single prediction method is sensitive to abnormal points and outliers, which easily makes the model overfit and reduces the prediction accuracy

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