Please use this identifier to cite or link to this item: http://hdl.handle.net/11328/3920
Title: An advanced generative deep learning framework for probabilistic spatio-temporal wind power forecasting
Authors: Jalali, S. M.
Khodayar, M.
Khosravi, A.
Osório, Gerardo J.
Nahavandi, S.
Catalão, João P. S.
Keywords: Deep learning
Probabilistic forecasting
Variational bayesian inference
Spectral graph convulutions
Evolutionary algorithm
Issue Date: Sep-2021
Publisher: IEEE
Citation: Jalali, S. M., Khodayar, M, Khosravi, A., Osório, G. J., Nahavandi, S., & Catalão, J. P. S. (2021). An advanced generative deep learning framework for probabilistic spatio-temporal wind power forecasting. In Proceedings of the 21th IEEE International Conference on Environment and Electrical Engineering and 5th IEEE Industrial and Commercial Power Systems Europe (EEEIC 2021 / I&CPS Europe 2021), Bari, Italy, 7-10 September 2021 (pp. 1-6). doi: 10.1109/EEEIC/ICPSEurope51590.2021.9584664. Disponível no Repositório UPT, http://hdl.handle.net/11328/3920
Abstract: This paper presents a deep generative model for capturing the conditional probability distribution of future wind power given its history by modeling and pattern recognition in a dynamic graph. The dynamic nodes show the wind sites while the dynamic edges reflect the correlation between the nodes. We propose a scalable optimization model, which is theoretically proved to catch distributions at nodes of the graph, contrary to all learning formulations in the sector of discriminatory pattern recognition. The density of probabilities for each node can be used as samples in our framework. This probabilistic deep convolutional Auto-encoder (PDCA), is based on the deep learning of localized first-order approximation of spectral graph convolutions, a novel evolutionary algorithm, and the Bayesian variational inference concepts. The presented generative model is used for the spatio-temporal probabilistic wind power problem in a wide 25 wind sites located in California, the USA for up to 24h ahead prediction. The experimental findings reveal that our proposed model outperforms other competitive temporal and spatio-temporal algorithms in terms of reliability, sharpness, and continuously ranked probability score.
URI: http://hdl.handle.net/11328/3920
ISBN: 978-1-6654-3613-7
Appears in Collections:REMIT - Publicações em Livros de Atas Internacionais / Papers in International Proceedings

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