A Review on Wind Power Forecasting Models for improved Renewable Energy Integration

Abstract

This paper gives an overview of the research that has been done on wind power forecasting models, which may be used to aid in the optimum integration of Renewable Energy (RE) into electric power networks. Aside from the economic advantages, the variable nature of wind energy production has a variety of negative consequences for the electric grid system, including stability, reliability, and the capacity to plan for future operations, among other things. As a result, precise forecasting of wind power output is critical for grid stability and security, as well as for the promotion of large-scale wind power. To ensure the accuracy of wind energy forecasts, a variety of conventional, artificial intelligence, and hybrid methodologies have been developed. The simplicity and robustness of time-series-based methods have made them popular for forecasting applications. Artificial Neural Networks (ANNs) and Fuzzy Logic have recently been supported by several researchers for forecasting because of their flexibility. This review covers the performance of many wind power forecasting models that are classified according to their categories. It is also offered a critical examination of contemporary studies, which includes statistical model and machine learning models that are based on historical data. Aspects of this study that are taken into consideration include the advantages and disadvantages of various forecasting models, including hybrid models, as well as performance matrices used in assessing the forecasting model. In addition, the possible advantages of model optimization are examined in detail as well.