Journal
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
Volume 9, Issue 6, Pages 1873-1888Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s12652-017-0648-4
Keywords
Feature selection; Improved fusion algorithm; Multi-stage forecast engine; Wavelet transform; Smart grid
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By changing the electricity market in smart grids, the consumers will be able to react to the electricity price. As close correlation of price and load, the density of this reaction can affect to demand curve and shift it in market. For this purpose, an accurate prediction model is demanded for optimal operation as well as planning in power system. For this purpose, we proposed a new hybrid forecast model based on dual-tree complex wavelet transform and multi-stage forecast engine (MSFE). In this model at first, the signal entered to proposed wavelet transform and then, it is filtered by new feature selection. After that, the signal predicted by proposed MSFE in three steps. An intelligent algorithm is applied to the forecast engine to increase its ability and prediction accuracy during the process. Finally, the improved fusion algorithm gather the outputs of MSFE. Effectiveness of the proposed method has been implemented over Australia's and New England electricity market data. Obtained results compared with several prediction models which demonstrate the validity of proposed model.
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