Discussion and Review of the Use of Neural Networks to Improve the Flexibility of Smart Grids in Presence of Distributed Renewable Ressources

Abstract

The evolving and nonstationary behavior of realworld data generally generated in streaming way creates serious challenges for learning models. Thus, changes may deteriorate previous decision models accuracy, which requires permanent adaptation strategies. Artificial neural networks have been among the popular choice of adaptation strategies to tackle concept drifting data streams, relying on their online learning capabilities. In this paper, the ability of most known neural networks of the literature to learn from data streams in presence of concept drift will be studied and compared using some meaningful criteria. Their limits will be highlighted using a case-study about the design of decision making aid model to improve the flexibility of electrical grids in presence of distributed Wind-PV renewable energy ressources. Finally, a self-adaptive scheme based on the use of neural networks is proposed in order to avoid these limits.