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This article proposes a novel wind speed forecast algorithm framework, which combines a deep belief network (DBN) and an improved Local Predictor (LP). The DBN adopted is a multiple hidden layers deep neural network consisting of restricted Boltzmann machines (RBMs) with superior performances on classification and prediction applications of a large‐scale dataset, such as wind speed data. However,...
This paper proposes a novel approach for short-term wind power forecast, where wind speed is predicted and used to forecast wind power through a power curve obtained from historical data. With the help of the empirical mode decomposition (EMD) method, wind speed is decomposed into mean trend and stochastic component. Subsequently, p-step forecast is conducted for the two components separately. The...
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