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Item Achievements, experiences, and lessons learned from the European research infrastructure ERIGrid related to the validation of power and energy systems(2020-11-09) Strasser, T. I.; de Jong, E. C. W.; Sosnina, M.; Rodriguez-Seco, J. E.; Kotsampopoulos, P.; Babazadeh, D.; Mäki, K.; Bhandia, R.; Brandl, R.; Sandroni, C.; Heussen, K.; Coffele, F.; POWER SYSTEMSPower system operation is of vital importance and must be developed far beyond today’s practice to meet future needs. Almost all European countries are facing an abrupt and very important increase of renewables with intrinsically varying yields which are difficult to predict. In addition, an increase of new types of electric loads and a reduction of traditional production from bulk generation can be observed as well. Hence, the level of complexity of system operation steadily increases. Because of these developments, the traditional power system is being transformed into a smart grid. Previous and ongoing research has tended to focus on how specific aspects of smart grids can be developed and validated, but until now there exists no integrated approach for analysing and evaluating complex smart grid configurations. To tackle these research and development needs, a pan-European research infrastructure is realized in the ERIGrid project that supports the technology development as well as the roll-out of smart grid technologies and solutions. This paper provides an overview of the main results of ERIGrid which have been achieved during the last four years. Also, experiences and lessons learned are discussed and an outlook to future research needs is provided. © 2020, CIGRE - Reprint from www.cigre.org with kind permission.Item An integrated pan-European research infrastructure for validating smart grid systems(2018-12-01) Strasser, Thomas I.; Pröstl Andrén, F.; Widl, E.; Lauss, G.; De Jong, E. C. W.; Calin, M.; Sosnina, M.; Khavari, A.; Rodriguez, J. E.; Kotsampopoulos, P.; Blank, M.; Steinbrink, C.; Mäki, K.; Kulmala, A.; van der Meer, A.; Bhandia, R.; Brandl, R.; Arnold, G.; Sandroni, C.; Pala, D.; Morales Bondy, D. E.; Heussen, K.; Gehrke, O.; Coffele, F.; Tran, Q.-T.; Rikos, E.; Nguyen, V. H.; Orue, I.; Degefa, M. Z.; Manikas, S.; POWER SYSTEMSA driving force for the realization of a sustainable energy supply in Europe is the integration of distributed, renewable energy resources. Due to their dynamic and stochastic generation behaviour, utilities and network operators are confronted with a more complex operation of the underlying distribution grids. Additionally, due to the higher flexibility on the consumer side through partly controllable loads, ongoing changes of regulatory rules, technology developments, and the liberalization of energy markets, the system’s operation needs adaptation. Sophisticated design approaches together with proper operational concepts and intelligent automation provide the basis to turn the existing power system into an intelligent entity, a so-called smart grid. While reaping the benefits that come along with those intelligent behaviours, it is expected that the system-level testing will play a significantly larger role in the development of future solutions and technologies. Proper validation approaches, concepts, and corresponding tools are partly missing until now. This paper addresses these issues by discussing the progress in the integrated Pan-European research infrastructure project ERIGrid where proper validation methods and tools are currently being developed for validating smart grid systems and solutions.Item 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.