Beyond conventional predictions: unfolding the ensemble Kalman filter's publications in renewable energy

Abstract

Within the process of development of sustainable energy solutions, the Ensemble Kalman Filter (EnKF) holds an allimportant key by assisting in forecasting and optimization of renewable energy systems. This essay describes in detail how the EnKF is utilized in diverse sectors of the renewable energy, of which one of its many vital roles is managing the variability and uncertainty that characterize wind and solar energy sources. By performing a meta-analysis and bibliometric work we discover that EnKF do two things very well -the level of the predictive model accuracy is increased and it is also easy to allocate the resources. Grid stability is another issue which EnKF solves well. The versatility of EnKF in wind forecasting has been highlighted in light of a study which has not only demonstrated how this method may be applied in renewable energy sources but also sheds light on recent developments as well. We will leave the forward part to researching potential additional studies such as EnKF integration with machine learning methods and its use towards renewables recent development. The work above shows how EnKF capable advanced data assimilation methods are highly needed for phasing out fossil fuels and ensure the shift to renewable energy sources as the global primary energy source.