Convolutional Graph Autoencoder: A Generative Deep Neural Network for Probabilistic Spatio-Temporal Solar Irradiance Forecasting

Machine learning on graphs is an important and omnipresent task for a vast variety of applications including anomaly detection and dynamic network analysis. In this paper, a deep generative model is introduced to capture continuous probability densities corresponding to the nodes of an arbitrary gra...

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Veröffentlicht in:	IEEE transactions on sustainable energy Jg. 11; H. 2; S. 571 - 583
Hauptverfasser:	Khodayar, Mahdi, Mohammadi, Saeed, Khodayar, Mohammad E., Wang, Jianhui, Liu, Guangyi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.04.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Anomalies Artificial neural networks Bayesian analysis Computational modeling Data models Deep learning Deep neural network Forecasting Geographical distribution Graph neural networks Irradiance Learning algorithms Machine learning Mathematical analysis Mathematical model Neural networks Nodes Optimization Pattern recognition Predictive models probabilistic forecasting Probabilistic logic Radiation Radiation measurement Solar radiation spatio-temporal forecasting spectral graph convolutions Statistical analysis Statistical inference variational Bayesian inference
ISSN:	1949-3029, 1949-3037
Online-Zugang:	Volltext
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