Effective artificial neural network-based wind power generation and load demand forecasting for optimum energy management

Abstract

The variability of power production from renewable energy sources (RESs) presents serious challenges in energy management (EM) and power system stability. Power forecasting plays a crucial role in optimal EM and grid security. Then, accurate power forecasting ensures optimum scheduling and EM. Therefore, this study 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 the forecasting technique. The performance of ANN was compared to four machine learning methods: LASSO, decision tree (DT), regression vector machines (RVM), and kernel ridge regression (KRR). The obtained results show that ANN provides high effectiveness and accuracy for WP forecasting. Furthermore, ANN has proven to be an interesting tool in ensuring optimum scheduling and EM.