Short Term Wind Speed Forecast Using Recurrent Neural Networks for Wind/Battery Energy Management - Rim Ben Ammar (International Journal of Intelligent Systems and Applications in Engineering, 2024)
As the enhanced energy crisis, wind power generated through wind turbines, widely known as a bright renewable energy source, is being mostly utilized. As a result, wind energy forecasting, notably wind speed forecasting is crucial for power energy management and production-consummation balance. Nevertheless, wind speed prediction is deeply challenging due to its non-stationary and nonlinear character. The main research aim to develop an effective paradigm for wind speed estimation based on recurrent neural network. Three topologies are proposed namely the modified Elman neural network, the Jordan neural network and the hybrid model that combines the latest cited networks. The mentioned forecasting models are evaluated through various statistical metrics mainly the normalized root mean squared error, the mean absolute percentage error and the correlation factor. The experimental results show that the predictors performed satisfactory forecasts. While, the efficiency index is slightly finer using the hybrid algorithm with an R-ratio equal to 99%. The estimated wind power is derived through the forecasted wind speed via a mathematical model. The derived generated power is utilized for Wind/Battery energy management in isolated area. The proposed supervision algorithm raises the wind power use to 92%.