Browsing by Keyword "Data models"
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Item ICT architectures for TSO-DSO coordination and data exchange: a European perspective: A European Perspective(2023-03-01) Perez, Nestor Rodriguez; Domingo, Javier Matanza; Lopez, Gregorio Lopez; Avila, Jose Pablo Chaves; Bosco, Ferdinando; Croce, Vincenzo; Kukk, Kalle; Uslar, Mathias; Madina, Carlos; Santos-Mugica, Maider; Tecnalia Research & Innovation; POWER SYSTEMSThe coordination between system operators is a key element for the decarbonization of the power system. Over the past few years, many EU-funded research projects have addressed the challenges of Transmission System Operators (TSO) and Distribution System Operators (DSO) coordination by implementing different data exchange architectures. This paper presents a review of the ICT architectures implemented for the main coordination schemes demonstrated in such projects. The main used technologies are analyzed, considering the type of data exchanged and the communication link. Finally, the paper presents the different gaps and challenges on TSO-DSO coordination related to ICT architectures that must still be faced, paying especial attention to the expected contribution of the EU-funded OneNet project on this topic. IEEEItem Transfer Learning and Online Learning for Traffic Forecasting under Different Data Availability Conditions: Alternatives and Pitfalls(Institute of Electrical and Electronics Engineers Inc., 2020-09-20) Manibardo, Eric L.; Laña, Ibai; Del Ser, Javier; IAThis work aims at unveiling the potential of Transfer Learning (TL) for developing a traffic flow forecasting model in scenarios of absent data. Knowledge transfer from high-quality predictive models becomes feasible under the TL paradigm, enabling the generation of new proper models with few data. In order to explore this capability, we identify three different levels of data absent scenarios, where TL techniques are applied among Deep Learning (DL) methods for traffic forecasting. Then, traditional batch learning is compared against TL based models using real traffic flow data, collected by deployed loops managed by the City Council of Madrid (Spain). In addition, we apply Online Learning (OL) techniques, where model receives an update after each prediction, in order to adapt to traffic flow trend changes and incrementally learn from new incoming traffic data. The obtained experimental results shed light on the advantages of transfer and online learning for traffic flow forecasting, and draw practical insights on their interplay with the amount of available training data at the location of interest.