Decentralized prediction of electrical time series in smart grids using long short-term memory neural networks
In the modern power grid framework, Renewable Energy Sources must be integrated into the existing energy systems to optimally deal with load, power and electromagnetic imbalance issues. In this context, smart grids have a pivotal role in transforming the aggregation of decentralized power sources. In order to implement these complex systems and to enable such an integration, machine learning techniques must be investigated and adopted where necessary.