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The advancement of distributed generation technologies in modern powersystems has led to a widespread integration of renewable power generation atcustomer side. However, the intermittent nature of renewable energy poses newchallenges to the network operational planning with underlying uncertainties.This paper proposes a novel Bayesian probabilistic technique for forecastingrenewable solar generation by addressing data and model uncertainties byintegrating bidirectional long short-term memory (BiLSTM) neural networks whilecompressing the weight parameters using variational autoencoder (VAE). ExistingBayesian deep learning methods suffer from high computational complexities asthey require to draw a large number of samples from weight parameters expressedin the form of probability distributions. The proposed method can deal withuncertainty present in model and data in a more computationally efficientmanner by reducing the dimensionality of model parameters. The proposed methodis evaluated using quantile loss, reconstruction error, and deterministicforecasting evaluation metrics such as root-mean square error. It is inferredfrom the numerical results that VAE-Bayesian BiLSTM outperforms otherprobabilistic and deterministic deep learning methods for solar powerforecasting in terms of accuracy and computational efficiency for differentsizes of the dataset.

A VAE-Bayesian Deep Learning Scheme for Solar Generation Forecasting based on Dimensionality Reduction