Effective artificial neural network-based wind power generation and load demand forecasting for optimum energy management - Jamii, Jannet; Mansouri, Majdi; Trabelsi, Mohamed; Mimouni, Mohamed Fouazi; Shatanawi, Wasfi (Frontiers in Energy Research, 2022)
The variability of power production from Renewable Energy sources (RESs) presents serious challenges in energy management (EM) and power system stability. Thus, the power forecasting plays a crucial role in optimal energy management and grid security. Then, the accurate power forecasting ensures an optimum scheduling and EM. Therefore, this paper proposes an artificial neural network (ANN) based paradigm to predict Wind Power (WP) generation and load demand, where the meteorological parameters including wind speed, temperature, and atmospheric pressure are fed to the model as inputs. The Normalized Root Mean Square Error (NRMSE) and Normalized Mean Absolute Error (NMAE) criteria are used to evaluate forecasting technique. The performance of ANN was compared to four machine learning methods including LASSO, Decision Tree, Regression Vector Machines and Kernel Ridge Regression. The obtained results show that ANN provides a high effectiveness and accuracy for wind power forecasting. Also ANN has proven to be a very interesting tool in order to ensure an optimum scheduling and EM.