Personal profile
ShortBio
Erik Maqueda Moro – Industrial Engineer from E.T.S.I. Bilbao with a Master’s Degree in Data Science from Aston University (United Kingdom) and currently conducting a PhD at the University of the Basque Country (UPV-EHU) on the application of artificial intelligence techniques for optimizing the demand response of distributed electrical resources to provide flexibility to the grid. He works as a senior researcher in the Digital Energy team in the Energy, Climate, and Urban Transition division at Tecnalia, where he has participated in various projects for the development of machine learning models such as demand prediction models, virtual sensors, and optimization algorithms. He has the ability to act as a translator between electrical/business experts and data scientists. Previously, he worked for 4 years in the predictive maintenance department for gas turbines at Rolls-Royce (United Kingdom) and as a researcher for Lufthansa Technik.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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SDG 7 Affordable and Clean Energy
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SDG 9 Industry, Innovation, and Infrastructure
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Collaborations and top research areas from the last five years
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A novel methodology for day-ahead buildings energy demand forecasting to provide flexibility services in energy markets
Rodríguez, F., Maqueda, E., Fernández, M., Pimenta, P. & Inês Marques, M., Oct 2024, In: International Journal of Electrical Power and Energy Systems. 161, 110207.Research output: Contribution to journal › Article › peer-review
Open AccessFile12 Citations (Scopus)4 Downloads (Pure) -
SpeCluRC-NTL: Spearman's distance-based clustering Reservoir Computing solution for NTL detection in smart grids
Serra, A., Ortiz, A., Manjarrés, D., Fernández, M., Maqueda, E., Cortés, P. J. & Canals, V., Jun 2024, In: International Journal of Electrical Power and Energy Systems. 157, 109891.Research output: Contribution to journal › Article › peer-review
Open AccessFile3 Citations (Scopus) -
Hybrid-Model-Based Digital Twin of the Drivetrain of a Wind Turbine and Its Application for Failure Synthetic Data Generation
Pujana, A., Esteras, M., Perea, E., Maqueda, E. & Calvez, P., Jan 2023, In: Energies. 16, 2, 861.Research output: Contribution to journal › Article › peer-review
Open AccessFile41 Citations (Scopus)3 Downloads (Pure) -
Online Pentane Concentration Prediction System Based on Machine Learning Techniques †
Manjarrés, D., Maqueda, E. & Landa-Torres, I., 2023, In: Engineering Proceedings. 39, 1, 77.Research output: Contribution to journal › Article › peer-review
Open AccessFile1 Downloads (Pure) -
Computer-implemented method for optimizing the operation of a drivetrain of a wind turbine
Perea Olabarria, E. (Inventor), Pujana Goitia, A. (Inventor), Esteras Bejar, M. (Inventor) & Maqueda Moro, E. (Inventor), 28 Jul 2022, IPC No. F03D17/00||F03D7/04||F03D80/50, Patent No. EP4311934 (A1), Priority date 28 Jul 2022, Priority No. EP22382724.7Research output: Patent
Datasets
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int:net Use Case Repository
Jimeno, J. (Creator), Santos-Mugica, M. (Creator), Maqueda, E. (Creator), Gómez-Arriola, I. (Creator), Jimenez, D. (Creator), Arzoz Fernandez, E. (Creator), Kung, A. (Creator), Genest, O. (Creator), Gyrard, A. (Creator), Kuchenbuch, R. A. (Creator), Uslar, M. (Creator) & Dognini, A. (Creator), The int:net Consortium, 30 Apr 2024
Dataset