Neural networks for online learning of non-stationary data streams: a review and application for smart grids flexibility improvement

Learning efficient predictive models in dynamic environments requires taking into account the continuous changing nature of phenomena generating the data streams, known in machine learning as “concept drift”. Such changes may affect models’ effectiveness over time, requiring permanent updates of parameters and structure to maintain performance. Several supervised machine learning methods have been developed to be adapted to learn in dynamic and non-stationary environments. One of the most well-known and efficient learning methods is neural networks. This paper focuses on the different neural networks developed to build learning models able to adapt to concept drifts on streaming data. Their performance will be studied and compared using meaningful criteria. Their limits to address the challenges related to the problem of the improvement of electrical grid flexibility in presence of distributed Wind–PV renewable energy resources within the context of energy transition will be highlighted. Finally, the study provides a self-adaptive scheme based on the use of neural networks to overcome these limitations and tackle these challenges.