  The advancement of 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 poses new
challenges to the network operational planning with underlying uncertainties.
This paper proposes a novel Bayesian probabilistic technique for forecasting
renewable solar 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 quantile loss, reconstruction error, and deterministic
forecasting evaluation metrics such as root-mean square error. It is inferred
from the numerical results that VAE-Bayesian BiLSTM outperforms other
probabilistic and deterministic deep learning methods for solar power
forecasting in terms of accuracy and computational efficiency for different
sizes of the dataset.
