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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Vydáno v:	IEEE transactions on sustainable energy Ročník 11; číslo 2; s. 571 - 583
Hlavní autoři:	Khodayar, Mahdi, Mohammadi, Saeed, Khodayar, Mohammad E., Wang, Jianhui, Liu, Guangyi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.04.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
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Abstract	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 graph. In contrast to all learning formulations in the area of discriminative pattern recognition, we propose a scalable generative optimization/algorithm theoretically proved to capture distributions at the nodes of a graph. Our model is able to generate samples from the probability densities learned at each node. This probabilistic data generation model, i.e., convolutional graph autoencoder (CGAE), is devised based on the localized first-order approximation of spectral graph convolutions, deep learning, and the variational Bayesian inference. We apply the CGAE to a new problem, the spatio-temporal probabilistic solar irradiance prediction. Multiple solar radiation measurement sites in a wide area in northern states of the U.S. are modeled as an undirected graph. Using our proposed model, the distribution of future irradiance given historical radiation observations is estimated for every site/node. Numerical results on the national solar radiation database show state-of-the-art performance for probabilistic radiation prediction on geographically distributed irradiance data in terms of reliability, sharpness, and continuous ranked probability score.
AbstractList	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 graph. In contrast to all learning formulations in the area of discriminative pattern recognition, we propose a scalable generative optimization/algorithm theoretically proved to capture distributions at the nodes of a graph. Our model is able to generate samples from the probability densities learned at each node. This probabilistic data generation model, i.e., convolutional graph autoencoder (CGAE), is devised based on the localized first-order approximation of spectral graph convolutions, deep learning, and the variational Bayesian inference. We apply the CGAE to a new problem, the spatio-temporal probabilistic solar irradiance prediction. Multiple solar radiation measurement sites in a wide area in northern states of the U.S. are modeled as an undirected graph. Using our proposed model, the distribution of future irradiance given historical radiation observations is estimated for every site/node. Numerical results on the national solar radiation database show state-of-the-art performance for probabilistic radiation prediction on geographically distributed irradiance data in terms of reliability, sharpness, and continuous ranked probability score.
Author	Khodayar, Mahdi Mohammadi, Saeed Khodayar, Mohammad E. Wang, Jianhui Liu, Guangyi
Author_xml	– sequence: 1 givenname: Mahdi orcidid: 0000-0003-4683-7810 surname: Khodayar fullname: Khodayar, Mahdi email: mahdik@smu.edu organization: Department of Electrical and Computer Engineering, Southern Methodist University, Dallas, TX, USA – sequence: 2 givenname: Saeed orcidid: 0000-0003-1823-9653 surname: Mohammadi fullname: Mohammadi, Saeed email: smohammadi@smu.edu organization: Department of Electrical and Computer Engineering, Southern Methodist University, Dallas, TX, USA – sequence: 3 givenname: Mohammad E. orcidid: 0000-0003-3856-5704 surname: Khodayar fullname: Khodayar, Mohammad E. email: mkhodayar@smu.edu organization: Department of Electrical and Computer Engineering, Southern Methodist University, Dallas, TX, USA – sequence: 4 givenname: Jianhui orcidid: 0000-0002-9716-3484 surname: Wang fullname: Wang, Jianhui email: jianhui@smu.edu organization: Department of Electrical and Computer Engineering, Southern Methodist University, Dallas, TX, USA – sequence: 5 givenname: Guangyi orcidid: 0000-0001-9822-2039 surname: Liu fullname: Liu, Guangyi email: guangyi.liu@geirina.net organization: Global Energy Interconnection Research Institute North America (GEIRI North America or GEIRINA), San Jose, CA, USA
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Cites_doi	10.1016/j.rser.2018.02.002 10.1016/j.apenergy.2016.05.025 10.1063/1.4946798 10.1109/TPWRS.2016.2569608 10.1109/TSTE.2018.2847558 10.1016/j.renene.2016.09.012 10.1109/TSTE.2016.2535466 10.1016/j.renene.2016.12.095 10.1609/icwsm.v3i1.13937 10.1109/PEDG.2018.8447751 10.1016/j.ijforecast.2015.12.002 10.1109/IGARSS.2014.6947394 10.1109/TII.2017.2730846 10.1109/PESGM.2017.8273776 10.1016/j.ijforecast.2015.11.002 10.1016/0038-092X(76)90045-1 10.1016/j.envres.2017.08.039 10.1016/j.solener.2013.05.027 10.1109/ISGT-Asia.2018.8467888 10.1109/TPWRS.2015.2502423 10.1109/PESGM.2015.7285696 10.1016/j.apenergy.2015.08.011 10.1109/TSG.2018.2847223 10.1016/j.renene.2013.05.011 10.1016/j.solener.2014.12.014 10.1109/TSTE.2016.2544929 10.3390/en11112906 10.1109/TSTE.2017.2694551 10.3390/en10101591 10.1016/j.egypro.2015.03.208 10.3390/en11030528 10.1016/j.solener.2012.09.018 10.1109/CFIS.2015.7391664 10.1109/TPWRS.2013.2287871 10.1007/978-3-319-33747-0_17 10.1109/TSTE.2018.2844102 10.1109/TSTE.2016.2610523 10.1109/TPWRS.2016.2608740 10.1016/j.energy.2016.08.060 10.1016/j.rser.2018.03.003 10.1109/TPWRS.2018.2794541 10.1073/pnas.122653799 10.1016/j.ijforecast.2016.02.001 10.1016/j.rser.2017.05.212 10.17775/CSEEJPES.2015.00046 10.3390/app8050689 10.1109/ICMLA.2017.0-108 10.1109/APPEEC.2017.8308947 10.1016/j.renene.2016.01.039
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References	ref13 ref12 ref15 ref14 ref53 ref11 ref54 ref10 ref17 liu (ref9) 2018; 11 kingma (ref51) 0 ref16 ref18 szabó (ref55) 2014; 15 chollet (ref57) 2015 abadi (ref58) 0 ref46 ref48 ref47 ref41 ref44 ref43 ref49 ref8 ref7 le cadre (ref19) 0 ref4 ref3 ref6 ref5 ref40 ref35 ref34 kipf (ref52) 2016 ref37 ref36 ref31 tastu (ref45) 2013 ref30 ref33 ref32 ref2 ref1 ref39 ref38 bastian (ref56) 2009; 8 doersch (ref50) 2016 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref29 khodayar (ref42) 2018
References_xml	– ident: ref29 doi: 10.1016/j.rser.2018.02.002 – ident: ref27 doi: 10.1016/j.apenergy.2016.05.025 – ident: ref33 doi: 10.1063/1.4946798 – ident: ref14 doi: 10.1109/TPWRS.2016.2569608 – volume: 15 start-page: 283 year: 2014 ident: ref55 article-title: Information theoretical estimators toolbox publication-title: J Mach Learn Res – ident: ref46 doi: 10.1109/TSTE.2018.2847558 – ident: ref22 doi: 10.1016/j.renene.2016.09.012 – ident: ref12 doi: 10.1109/TSTE.2016.2535466 – ident: ref1 doi: 10.1016/j.renene.2016.12.095 – volume: 8 start-page: 361 year: 2009 ident: ref56 article-title: Gephi: An open source software for exploring and manipulating networks publication-title: ICWSM doi: 10.1609/icwsm.v3i1.13937 – ident: ref39 doi: 10.1109/PEDG.2018.8447751 – ident: ref28 doi: 10.1016/j.ijforecast.2015.12.002 – ident: ref10 doi: 10.1109/IGARSS.2014.6947394 – year: 2015 ident: ref57 article-title: Keras – year: 2013 ident: ref45 article-title: Space-time scenarios of wind power generation produced using a Gaussian copula with parametrized precision matrix – ident: ref3 doi: 10.1109/TII.2017.2730846 – ident: ref32 doi: 10.1109/PESGM.2017.8273776 – ident: ref23 doi: 10.1016/j.ijforecast.2015.11.002 – ident: ref5 doi: 10.1016/0038-092X(76)90045-1 – year: 2018 ident: ref42 article-title: Energy disaggregation via deep temporal dictionary learning – ident: ref48 doi: 10.1016/j.envres.2017.08.039 – ident: ref13 doi: 10.1016/j.solener.2013.05.027 – ident: ref43 doi: 10.1109/ISGT-Asia.2018.8467888 – ident: ref24 doi: 10.1109/TPWRS.2015.2502423 – ident: ref20 doi: 10.1109/PESGM.2015.7285696 – ident: ref54 doi: 10.1016/j.apenergy.2015.08.011 – start-page: 1 year: 0 ident: ref19 article-title: Solar PV power forecasting using extreme learning machine and information fusion publication-title: Proc Eur Symp Artif Neural Netw Comput Intell Mach Learn – ident: ref30 doi: 10.1109/TSG.2018.2847223 – ident: ref31 doi: 10.1016/j.renene.2013.05.011 – year: 2016 ident: ref50 article-title: Tutorial on variational autoencoders – ident: ref17 doi: 10.1016/j.solener.2014.12.014 – ident: ref47 doi: 10.1109/TSTE.2016.2544929 – ident: ref15 doi: 10.3390/en11112906 – ident: ref4 doi: 10.1109/TSTE.2017.2694551 – ident: ref26 doi: 10.3390/en10101591 – ident: ref8 doi: 10.1016/j.egypro.2015.03.208 – volume: 11 start-page: 528 year: 2018 ident: ref9 article-title: Ultra-short-term forecast of photovoltaic output power under fog and haze weather publication-title: Energies doi: 10.3390/en11030528 – ident: ref11 doi: 10.1016/j.solener.2012.09.018 – ident: ref16 doi: 10.1109/CFIS.2015.7391664 – ident: ref18 doi: 10.1109/TPWRS.2013.2287871 – ident: ref35 doi: 10.1007/978-3-319-33747-0_17 – ident: ref53 doi: 10.1109/TSTE.2018.2844102 – ident: ref40 doi: 10.1109/TSTE.2016.2610523 – ident: ref25 doi: 10.1109/TPWRS.2016.2608740 – ident: ref6 doi: 10.1016/j.energy.2016.08.060 – ident: ref44 doi: 10.1016/j.rser.2018.03.003 – ident: ref41 doi: 10.1109/TPWRS.2018.2794541 – ident: ref49 doi: 10.1073/pnas.122653799 – year: 2016 ident: ref52 article-title: Semi-supervised classification with graph convolutional networks – ident: ref36 doi: 10.1016/j.ijforecast.2016.02.001 – ident: ref21 doi: 10.1016/j.rser.2017.05.212 – start-page: 265 year: 0 ident: ref58 article-title: TensorFlow: A system for large-scale machine learning publication-title: Proc 11th USENIX Conf Operating Syst Des Implementation – year: 0 ident: ref51 article-title: Auto-encoding variational bayes – ident: ref2 doi: 10.17775/CSEEJPES.2015.00046 – ident: ref37 doi: 10.3390/app8050689 – ident: ref34 doi: 10.1109/ICMLA.2017.0-108 – ident: ref38 doi: 10.1109/APPEEC.2017.8308947 – ident: ref7 doi: 10.1016/j.renene.2016.01.039
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SubjectTerms	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
Title	Convolutional Graph Autoencoder: A Generative Deep Neural Network for Probabilistic Spatio-Temporal Solar Irradiance Forecasting
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