Machine Learning Platform for Extreme Scale Computing on Compressed IoT Data

Autor: Seshu Tirupathi, Dhaval Salwala, Giulio Zizzo, Ambrish Rawat, Mark Purcell, Soren Kejser Jensen, Christian Thomsen, Nguyen Ho, Carlos E. Muniz Cuza, Jonas Brusokas, Torben Bach Pedersen, Giorgos Alexiou, Giorgos Giannopoulos, Panagiotis Gidarakos, Alexandros Kalimeris, Stavros Maroulis, George Papastefanatos, Ioannis Psarros, Vassilis Stamatopoulos, Manolis Terrovitis

Publikováno v: Tirupathi, S, Salwala, D, Zizzo, G, Rawat, A, Purcell, M, Jensen, S K, Thomsen, C, Ho, N, Cuza, C E M, Brusokas, J, Pedersen, T B, Alexiou, G, Giannopoulos, G, Gidarakos, P, Kalimeris, A, Maroulis, S, Papastefanatos, G, Psarros, I, Stamatopoulos, V & Terrovitis, M 2022, Machine Learning Platform for Extreme Scale Computing on Compressed IoT Data . in S Tsumoto, Y Ohsawa, L Chen, D Van den Poel, X Hu, Y Motomura, T Takagi, L Wu, Y Xie, A Abe & V Raghavan (eds), 2022 IEEE International Conference on Big Data (Big Data) ., 10020540, IEEE Communications Society, pp. 3179-3185, 2022 IEEE International Conference on Big Data (Big Data), 17/12/2022 . https://doi.org/10.1109/BigData55660.2022.10020540

With the lowering costs of sensors, high-volume and high-velocity data are increasingly being generated and analyzed, especially in IoT domains like energy and smart homes. Consequently, applications that require accurate short-term forecasts and pre

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6a2118a8cdcd489e5491601b574f9032
https://vbn.aau.dk/da/publications/771ac3a1-1afc-44d3-b442-4f64c86ec4e0

Distributed Incremental Machine Learning for Big Time Series Data

Autor: Dhaval Salwala, Seshu Tirupathi, Brian Quanz, Wesley M. Gifford, Stuart Siegel, Vijay Ekambaram, Arindam Jati

Publikováno v: 2022 IEEE International Conference on Big Data (Big Data).

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::4b2bf33e3199d2d75759c10af66a1d5a
https://doi.org/10.1109/bigdata55660.2022.10020361

Knowledge- and Data-driven Services for Energy Systems using Graph Neural Networks

Autor: Robert Gormally, Francesco Fusco, Seshu Tirupathi, Bradley J. Eck, Mark Purcell

Publikováno v: IEEE BigData

The transition away from carbon-based energy sources poses several challenges for the operation of electricity distribution systems. Increasing shares of distributed energy resources (e.g. renewable energy generators, electric vehicles) and internet-

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::416c815e3eb946cf4c820d2ee5e0089d
http://arxiv.org/abs/2103.07248

Surrogate modeling and risk-based analysis for solute transport simulations

Autor: Seshu Tirupathi, Emanuele Ragnoli, Fearghal O'Donncha, Ernesto Arandia, Sean A. McKenna

Publikováno v: Stochastic Environmental Research and Risk Assessment. 33:1907-1921

This study is driven by the question of how quickly a solute will be flushed from an aquatic system after input of the solute into the system ceases. Simulating the fate and transport of a solute in an aquatic system can be performed at high spatial

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::7661d9980dc7fcb8adfcd634fcf197ad
https://doi.org/10.1007/s00477-018-1549-6

AI Modelling and Time-series Forecasting Systems for Trading Energy Flexibility in Distribution Grids

Autor: Bradley J. Eck, Mark Purcell, Robert Gormally, Francesco Fusco, Seshu Tirupathi

Publikováno v: Proceedings of the Tenth ACM International Conference on Future Energy Systems
Proceedings of the Tenth ACM International Conference on Future Energy Systems-e-Energy 19
Proceedings of the Tenth ACM International Conference on Future Energy Systems -e-Energy '19

We demonstrate progress on the deployment of two sets of technologies to support distribution grid operators integrating high shares of renewable energy sources, based on a market for trading local energy flexibilities. An artificial-intelligence (AI

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5f22656dba5e9e20cacd203e601684a5
https://doi.org/10.1145/3307772.3330158