PV Power Forecasting with Univariate Input Based on mRMR-ESN

doi:10.12068/j.issn.1005-3026.2021.02.004

Abstract

Abstract: Two kinds of prediction model are designed， ie， the traditional multivariable forecasting model using weather variables as input and the new univariate forecasting model using historical power data as input. The mRMR(minimum-redundancy and maximum-relevance)method is used to extract the features of the two models respectively， and ESN(echo state network)， which has advantages in time series prediction， is used to simulate and predict the photovoltaic (PV) power in the next 5minutes.The simulation results show that the mRMR method is used to extract the features of the historical PV power data， then， the feature subset that can make the prediction model reach the optimal effect is determined. The feature subset is used as the input of the univariate prediction model to obtain more accurate prediction effect. The new univariate prediction model can provide a new PV prediction idea for PV power station.

Key words: echo state network; feature extraction; character subset; univariate input; photovoltaic power forecasting

CLC Number:

TP183
TM615

HAN Peng， GUO Tian， WANG Jin-kuan， SHI Ze-wei. PV Power Forecasting with Univariate Input Based on mRMR-ESN[J]. Journal of Northeastern University(Natural Science), 2021, 42(2): 174-179.

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