A Bayesian Deep Learning Technique for Multi-Step Ahead Solar Generation Forecasting

no code implementations • 21 Mar 2022 • Devinder Kaur, Shama Naz Islam, Md. Apel Mahmud

In this paper, we propose an improved Bayesian bidirectional long-short term memory (BiLSTM) neural networks for multi-step ahead (MSA) solar generation forecasting.

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

no code implementations • 24 Mar 2021 • Devinder Kaur, Shama Naz Islam, Md. Apel Mahmud, Md. Enamul Haque, Adnan Anwar

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).

Computational Efficiency Dimensionality Reduction +1

Energy Forecasting in Smart Grid Systems: A Review of the State-of-the-art Techniques

no code implementations • 25 Nov 2020 • Devinder Kaur, Shama Naz Islam, Md. Apel Mahmud, Md. Enamul Haque, ZhaoYang Dong

Energy forecasting has a vital role to play in smart grid (SG) systems involving various applications such as demand-side management, load shedding, and optimum dispatch.

Management Probabilistic Deep Learning