Abstract
Aiming to solve the problem of low precision of traditional photovoltaic power forecast method under abrupt weather conditions. In this paper, a high-precision photovoltaic power prediction method based on similarity time and LM-BP neural network is proposed. Firstly, the factors affecting the output power of photovoltaic power station are analyzed, and the short-term output power model of photovoltaic power station is established based on similar day and LM-BP neural network. Then, from the perspective of model training efficiency and prediction accuracy, the deficiencies in the short-term power prediction of photovoltaic power stations based on similar days and LM-BP algorithm are analyzed. Secondly, the prediction model of LM-BP neural network based on similar hours is established. Finally, Jiaxing photovoltaic power station is taken as an example for simulation verification. The simulation results show that the proposed method has high accuracy in predicting photovoltaic power under abrupt weather conditions.
Highlights
Weather typeMeteorological factors vary greatly under different weather conditions, which leads to great differences in photovoltaic power under different weather conditions
Introduction modelThe results show that the prediction accuracy of the model is higher under sunny conditions
If the weather conditions in a certain place do not change much in a certain period of time, the output power curve of the photovoltaic power station should be consistent with the change rule of the solar radiation intensity change curve
Summary
Meteorological factors vary greatly under different weather conditions, which leads to great differences in photovoltaic power under different weather conditions. If the weather conditions in a certain place do not change much in a certain period of time, the output power curve of the photovoltaic power station should be consistent with the change rule of the solar radiation intensity change curve. Under changeable weather conditions, the main factors affecting solar radiation attenuation will constantly change, making the amount of radiation received by photovoltaic modules fluctuate repeatedly in a certain period of time. At this time, the variation law of photovoltaic power station output power and solar radiation intensity will be greatly different
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