The 12th International Symposium on Intelligent Distributed Computing (IDC 2018) (http://idc2018.jrlab.science/) was held in Bilbao from October 15^th to 17^th, 2018. These series of symposiums aim at fostering the discussion on latest findings, achievements and ideas in the area of Intelligent Distributed Computing by gathering together both researchers and practitioners in the areas of Intelligent Computing and Distributing Computing. The symposium had around 50 attendees, 38 paper presentations and five well-known researchers in the area as invited speakers: José A. Lozano, David Camacho, Josu Ceberio, Eleni I Vlahogianni and Albert Bifet.

As a part of this symposium and with the sponsorship of the PostlowCIT project, the Smart Mobility Unit from DeustoTech organized an award valued in 400€ for the best paper related to the reduction of CO2 emissions from those submitted to this symposium. The Award Committee formed by Antonio D. Masegosa (DeustoTech/IKERBASQUE), Pedro Lopez (DeustoTech), Andrés Masegosa (University of Almería), Ignacio Angulo (DeustoTech) and Pilar Elejoste (DeustoTech) considered both the quality of the paper and the social impact of the contribution. From the 38 papers, seven articles were eligible for the award for being related to CO2 reduction, and the next three were nominated as finalists:

Cornejo-Bueno L., Acevedo-Rodríguez J., Prieto L., Salcedo-Sanz S. (2018) A Hybrid Ensemble of Heterogeneous Regressors for Wind Speed Estimation in Wind Farms. In: Del Ser J., Osaba E., Bilbao M., Sanchez-Medina J., Vecchio M., Yang XS. (eds) Intelligent Distributed Computing XII. IDC 2018. Studies in Computational Intelligence, vol 798. Springer, Cham

Olivares-Rodríguez C., Castillo-Calzadilla T., Kamara-Esteban O. (2018) Bio-inspired Approximation to MPPT Under Real Irradiation Conditions. In: Del Ser J., Osaba E., Bilbao M., Sanchez-Medina J., Vecchio M., Yang XS. (eds) Intelligent Distributed Computing XII. IDC 2018. Studies in Computational Intelligence, vol 798. Springer, Cham

Martinez I., Viles E., Cabrejas I. (2018) Labelling Drifts in a Fault Detection System for Wind Turbine Maintenance. In: Del Ser J., Osaba E., Bilbao M., Sanchez-Medina J., Vecchio M., Yang XS. (eds) Intelligent Distributed Computing XII. IDC 2018. Studies in Computational Intelligence, vol 798. Springer, Cham

Finally, the paper titled “Labelling Drifts in a Fault Detection System for Wind Turbine Maintenance” was selected as the winner. This paper presented an approach for improving the predictive maintenance strategies for wind turbines which can lead to the reduction of costs in the maintenance of these systems. Concretely, the authors proposed a methodology for identifying unpredictable statistical changes in the measured variables that can deteriorate the performance of machine learning techniques when they are applied to detect incipient failures and anomalies in the working of wind turbines.

To know more details about the POSTLowCIT project, click on this link.