Athanasios Sfetsos

TL;DR: A new approach for the forecasting of mean hourly global solar radiation received by a horizontal surface with developed artificial intelligence models that predict the solar radiation time series more effectively compared to the conventional procedures based on the clearness index.

TL;DR: A comparison of various forecasting approaches, using time series analysis, on mean hourly wind speed data, including the traditional linear (ARMA) models and the commonly used feed forward and recurrent neural networks is presented.

TL;DR: In this article, a method for the forecasting of mean hourly wind speed data using time series analysis is presented. But the method is based on the multi-step forecasting of 10 minutes averaged data and the subsequent averaging to generate mean hourly predictions.

TL;DR: In this paper, a non-linear regression model for wind turbine power curve approximation was proposed, which stands out with several advantages, such as fitting physical properties of wind turbine (i.e., power curve does not exceed the highest value of generated power as it is maximum physically possible), lower number of parameters to be estimated, dependency on only one factor.

TL;DR: In this article, the authors evaluated the performance and the uncertainty of receptor models (RMs) in intercomparison exercises employing real-world and synthetic input datasets and concluded that RMs are capable of estimating the contribution of the major pollution source categories over a given time window with a level of accuracy that is in line with the needs of air quality management.