Adaptive Photovoltaic Daily Power Forecasting Algorithm based on EEMD-ANN
With the continuous development of clean energy,the installed capacity of photovoltaic power sources in China is constantly increasing.In order to cope with the severe challenges brought by its ran-domness,volatility,uncertainty and other characteristics to the safe operation of the power grid,the re-search combines the set empirical mode decomposition(EEMD)method to process the original time se-ries,decomposing it into limited and small oscillation modes to form clearer signal inputs.Then,artificial neural networks(ANN)are used to mine the patterns of historical data,and an adaptive photovoltaic dai-ly electricity prediction model based on EEMD-ANN is constructed.The results of taking the daily elec-tricity consumption process of a photovoltaic power station in southern China as an example show that the prediction results obtained by the model have good prediction accuracy and are a practical method for pre-dicting photovoltaic electricity consumption.
NETL
NSTL
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