  The advancement in distributed generation technologies in modern power
systems has led to a widespread integration of renewable power generation at
customer side. However, the intermittent nature of renewable energy pose new
challenges to the network operational planning with underlying uncertainties.
This paper proposes a novel Bayesian probabilistic technique for forecasting
renewable power generation by addressing data and model uncertainties by
integrating bidirectional long short-term memory (BiLSTM) neural networks while
compressing the weight parameters using variational autoencoder (VAE). Existing
Bayesian deep learning methods suffer from high computational complexities as
they require to draw a large number of samples from weight parameters expressed
in the form of probability distributions. The proposed method can deal with
uncertainty present in model and data in a more computationally efficient
manner by reducing the dimensionality of model parameters. The proposed method
is evaluated using pinball loss, reconstruction error, and other forecasting
evaluation metrics. It is inferred from the numerical results that VAE-Bayesian
BiLSTM outperforms other probabilistic deep learning methods in terms of
forecasting accuracy and computational efficiency for different sizes of the
dataset.
