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• W2565141427 abstract "The production of energy from renewable energy systems RES arrays has the properties of nonlinearity and uncertainty and usually fluctuates with the changes of weather and other factors. In this paper, an integrated method based on case-based reasoning CBR, empirical mode decomposition EMD and artificial neural network ANN is proposed to predict the productivity of the RES. At first, the non-stationary time series is pre-processed by CBR to find out the ten days with the most similarity to the predicted day. Then uses EMD decomposed times series of ten days we selected into a series of IMFs Intrinsic Mode Functions with features of stationarity and multiple time scale, for each IMF component, constructing models of ANN to predict. Finally would be straight line fit to final predict result. The simulation results show that the productivity predicted by using this integrated algorithm is more accurate than that of single artificial neutral network model." @default.
• W2565141427 created "2017-01-06" @default.
• W2565141427 creator A5042329532 @default.
• W2565141427 creator A5084134658 @default.
• W2565141427 date "2016-01-01" @default.
• W2565141427 modified "2023-09-29" @default.
• W2565141427 title "A new method to predict short-term wind power in the renewable energy systems" @default.
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• W2565141427 doi "https://doi.org/10.1504/ijwmc.2016.078214" @default.
• W2565141427 hasPublicationYear "2016" @default.
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