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

Kaur, Devinder, Islam, Shama Naz and Mahmud, Md Apel (2022) A Bayesian Deep Learning Technique for Multi-Step Ahead Solar Generation Forecasting. Working Paper. arXiv. (Submitted)

2203.11379v1.pdf - Submitted Version
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Official URL: https://doi.org/10.48550/arXiv.2203.11379


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.

Item Type: Report (Working Paper)
Uncontrolled Keywords: Multi-step ahead forecasting, Bayesian deep learning, power systems, renewable generation forecasting
Subjects: F300 Physics
G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Rachel Branson
Date Deposited: 16 Nov 2022 11:45
Last Modified: 16 Nov 2022 11:45
URI: https://nrl.northumbria.ac.uk/id/eprint/50669

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