Conference proceedings

Displaying 1 - 3 of 3 in Enviornmental data analysis and modelling (remove filter), data (remove filter)

CEST Proceedings are published under the ISSN 2944-9820.

A comparison between the past and future expected wind conditions in the European coastal environment of the Mediterranean Sea

(Corresponding) RUSU E.

Topic:

Enviornmental data analysis and modelling

In the last years, exploitation of the wind power has been constantly increasing together with the size of the turbines. Furthermore, by 2030 wind energy is expected to supply around 30% of EU’s power demand. Offshore wind represents a significant future opportunity, since resources are abundant...Read more

Keywords:

Mediterranean Sea, wind power, 2050, RCP4.5, historical data, average and extreme wind conditions

DOI:

https://doi.org/10.30955/gnc2019.00092

Conference:

CEST2019

Paper ID:

cest2019_00092

The integration of three field survey datasets in Athens, Greece: transformation of five-point to seven-point thermal sensation scale

(Corresponding) Pantavou K., Lykoudis S., Delibasis K., Tseliou A., Koletsis I., Nikolopoulou M., Tsiros I.

Topic:

Enviornmental data analysis and modelling

The integration of the datasets from three different field surveys on thermal sensation conducted at eight different sites of the area of Athens, Greece was examined. All three surveys were carried out with similar methodologies so data integration can be considered meaningful. The surveys included...Read more

Keywords:

field surveys, thermal sensation, data integration, PET

DOI:

https://doi.org/10.30955/gnc2019.00216

Conference:

CEST2019

Paper ID:

cest2019_00216

Meteorological Data Science: exploiting causality discovery in time-series for knowledge discovery and improved forecasting

Gkikas A., (Corresponding) Maragoudakis M.

Topic:

Enviornmental data analysis and modelling

Climate change and its impact on everyday life still remains one of the greatest challenge of our era. The complex nature of climate data addresses the use of data science techniques to provide predictive analytics to the task at hand. While most existing approaches exploit correlation between...Read more

Keywords:

Data Science, Causal Inference, Time-Series Analysis, Graph Analysis, Feature Selection

DOI:

https://doi.org/10.30955/gnc2019.00828

Conference:

CEST2019

Paper ID:

cest2019_00828