Major dependency on fossil energy resources and emission of greenhouse gases are common problems that have a very harmful impact on human communities. Thus, the use of renewable energy resources, such as wind power, has become a strong alternative to solve this problem. Nevertheless, because of the intermittence and unpredictability of the wind energy, an accurate wind speed forecasting is a very challenging research subject. This paper addresses a short-term wind speed forecasting based on Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The forecasting performances of the model were conducted using the same dataset under different evaluation metrics in terms of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) performance evaluation metrics. The obtained results denote that the used model achieves excellent forecasting accuracy.

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Wind is a dominant source of renewable energy with a high sustainability potential. However, the intermittence and unstable nature of wind source affect the efficiency and reliability of wind energy conversion systems. The prediction of the available wind potential is also heavily flawed by its unstable nature. Thus, evaluating the wind energy trough wind speed prevision, is crucial for adapting energy production to load shifting and user demand rates. This work aims to forecast the wind speed using the statistical Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model and the Deep Neural Network model of Long Short-Term Memory (LSTM). In order to shed light on these methods, a comparative analysis is conducted to select the most appropriate model for wind speed prediction. The errors metrics, mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to evaluate the effectiveness of each model and are used to select the best prediction model. Overall, the obtained results showed that LSTM model, compared to SARIMA, has shown leading performance with an average of absolute percentage error (MAPE) of 14.05%.

To improve efficiency and productivity of electric energy generators based on photovoltaic, wind or hybrid systems; several DC/AC conversion techniques have been developed and tested like multilevel inverters. Multilevel inverters are a performant solution for the ramp-up of converters. As soon as the DC supply voltage exceeds a few kV, it is necessary to combine switches, switching cells or converters. This paper presents a progressive study of an interesting type of these inverters namely flying capacitor multilevel inverters (FCMLI): architecture, evolutions, benefits and inconvenient. In fact, we processed 3-and 5-level FCMLI while presenting possible circuit schemes and simulation results on Matlab Simulink. Finally, a general formulation has been adopted and applied to a 17 level FCMLI.