A Neural Network Based Prediction System of Distributed Generation for the Management of Microgrids
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Massimo Panella | Rodolfo Araneo | Amedeo Andreotti | Antonello Rosato | M. Panella | R. Araneo | A. Andreotti | A. Rosato
[1] P. Siano,et al. Combined Operations of Renewable Energy Systems and Responsive Demand in a Smart Grid , 2011, IEEE Transactions on Sustainable Energy.
[2] Henrik Madsen,et al. Multi-site solar power forecasting using gradient boosted regression trees , 2017 .
[3] Stephen P. Boyd,et al. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..
[4] Massimo Panella,et al. A Distributed Algorithm for the Cooperative Prediction of Power Production in PV Plants , 2019, IEEE Transactions on Energy Conversion.
[5] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[6] Gonzalo Mateos,et al. Modeling and Optimization for Big Data Analytics: (Statistical) learning tools for our era of data deluge , 2014, IEEE Signal Processing Magazine.
[7] Alex Graves,et al. DRAW: A Recurrent Neural Network For Image Generation , 2015, ICML.
[8] Jaime Lloret,et al. Artificial neural networks for short-term load forecasting in microgrids environment , 2014 .
[9] Dong Liu,et al. Research on Stochastic Optimal Operation Strategy of Active Distribution Network Considering Intermittent Energy , 2017 .
[10] Tamer Khatib,et al. A review of islanding detection techniques for renewable distributed generation systems , 2013 .
[11] Martin Hasler,et al. Distributed machine learning in networks by consensus , 2014, Neurocomputing.
[12] Anzar Mahmood,et al. Prosumer based energy management and sharing in smart grid , 2018 .
[13] Dianhui Wang,et al. Distributed music classification using Random Vector Functional-Link nets , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).
[14] Jürgen Schmidhuber,et al. Learning to forget: continual prediction with LSTM , 1999 .
[15] Samy Bengio,et al. Show and tell: A neural image caption generator , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Simone Scardapane,et al. Distributed semi-supervised support vector machines , 2016, Neural Networks.
[17] B. Hodge,et al. The value of day-ahead solar power forecasting improvement , 2016 .
[18] Massimo Panella,et al. Prediction in Photovoltaic Power by Neural Networks , 2017 .
[19] Quoc V. Le,et al. Sequence to Sequence Learning with Neural Networks , 2014, NIPS.
[20] Thomas Morstyn,et al. Incentivizing Prosumer Coalitions With Energy Management Using Cooperative Game Theory , 2019, IEEE Transactions on Power Systems.
[21] Alex Graves,et al. Generating Sequences With Recurrent Neural Networks , 2013, ArXiv.
[22] Vassilios G. Agelidis,et al. Unified Distributed Control for DC Microgrid Operating Modes , 2016, IEEE Transactions on Power Systems.
[23] Nikos D. Hatziargyriou,et al. Integrating distributed generation into electric power systems: A review of drivers, challenges and opportunities , 2007 .
[24] Kevin P. Murphy,et al. Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.
[25] Viktor K. Prasanna,et al. Submitted to Ieee Transactions on Parallel and Distributed Systems 1 Match for the Prosumer Smart Grid the Algorithmics of Real-time Power Balance , 2022 .
[26] Anastasios G. Bakirtzis,et al. Optimal Offering Strategy of a Virtual Power Plant: A Stochastic Bi-Level Approach , 2016, IEEE Transactions on Smart Grid.
[27] Rahmat-Allah Hooshmand,et al. A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems , 2017 .
[28] Simone Scardapane,et al. Fully Decentralized Semi-supervised Learning via Privacy-preserving Matrix Completion , 2017, IEEE Transactions on Neural Networks and Learning Systems.
[29] Bernhard Sick,et al. Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).
[30] Reza Olfati-Saber,et al. Consensus and Cooperation in Networked Multi-Agent Systems , 2007, Proceedings of the IEEE.
[31] Zoran Obradovic,et al. The distributed boosting algorithm , 2001, KDD '01.
[32] R. Urraca,et al. Review of photovoltaic power forecasting , 2016 .
[33] Yoshua Bengio,et al. Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies , 2001 .
[34] Qing Zhao,et al. Distributed Learning in Wireless Sensor Networks , 2007 .
[35] Yoshua Bengio,et al. Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.
[36] Amit Kumar Yadav,et al. Solar radiation prediction using Artificial Neural Network techniques: A review , 2014 .
[37] Soteris A. Kalogirou,et al. Machine learning methods for solar radiation forecasting: A review , 2017 .
[38] Jinyu Wen,et al. Determining the Minimal Power Capacity of Energy Storage to Accommodate Renewable Generation , 2017 .
[39] Ali H. Sayed,et al. Adaptive Networks , 2014, Proceedings of the IEEE.
[40] Hermann Ney,et al. LSTM Neural Networks for Language Modeling , 2012, INTERSPEECH.
[41] Sonia Leva,et al. Physical and hybrid methods comparison for the day ahead PV output power forecast , 2017 .