Journal
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
Volume 2, Issue 3, Pages 265-276Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSTE.2011.2114680
Keywords
Feature selection; forecasting engine; hybrid neural network; particle swarm optimization; wind power forecasting
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Following the growing share of wind energy in electric power systems, several wind power forecasting techniques have been reported in the literature in recent years. In this paper, a wind power forecasting strategy composed of a feature selection component and a forecasting engine is proposed. The feature selection component applies an irrelevancy filter and a redundancy filter to the set of candidate inputs. The forecasting engine includes a new enhanced particle swarm optimization component and a hybrid neural network. The proposed wind power forecasting strategy is applied to real-life data from wind power producers in Alberta, Canada and Oklahoma, U. S. The presented numerical results demonstrate the efficiency of the proposed strategy, compared to some other existing wind power forecasting methods.
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