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Here, we assume that at step k + 1, the nominal load and wind power output are known (or can be predicted based on wind power prediction method [27] and historical demand data).
In order to employ the model predictive controller, we propose a wind power prediction system, which is used by the controller within its predictive optimization.
Fig. 1 Stacked autoencoder feature extraction for wind power prediction.
Wind power prediction errors and tieline constraints are incorporated.
Fig. 3 Double-stage ANFIS wind power prediction algorithm.
Fig. 1 ANFIS-based double-stage wind power prediction model.
Labour has called his balance of power prediction "bluster and bluff".
In previous studies (Tasnim et al. 2014), an ensemble framework was considered for wind power prediction.
In general, problems of the signal power prediction are considered in [21].
In this paper, an algorithm for wind power prediction is presented using autoencoder.
MCS is used to incorporate local network constraints and wind power prediction errors.
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