  In this paper, we propose an improved Bayesian bidirectional long-short term
memory (BiLSTM) neural networks for multi-step ahead (MSA) solar generation
forecasting. The proposed technique applies alpha-beta divergence for a more
appropriate consideration of outliers in the solar generation data and
resulting variability of the weight parameter distribution in the neural
network. The proposed method is examined on highly granular solar generation
data from Ausgrid using probabilistic evaluation metrics such as Pinball loss
and Winkler score. Moreover, a comparative analysis between MSA and the
single-step ahead (SSA) forecasting is provided to test the effectiveness of
the proposed method on variable forecasting horizons. The numerical results
clearly demonstrate that the proposed Bayesian BiLSTM with alpha-beta
divergence outperforms standard Bayesian BiLSTM and other benchmark methods for
MSA forecasting in terms of error performance.
