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.

中文翻译：

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用于非平稳数据流在线学习的神经网络：智能电网灵活性改进的回顾和应用

在动态环境中学习有效的预测模型需要考虑生成数据流的现象的不断变化的性质，在机器学习中称为“概念漂移”。随着时间的推移，此类更改可能会影响模型的有效性，需要永久更新参数和结构以保持性能。已经开发了几种有监督的机器学习方法，以适应在动态和非平稳环境中学习。最著名和最有效的学习方法之一是神经网络。本文重点介绍为构建能够适应流数据概念漂移的学习模型而开发的不同神经网络。他们的表现将使用有意义的标准进行研究和比较。将强调在能源转型背景下，在分布式风-光伏可再生能源存在的情况下，解决与提高电网灵活性问题相关的挑战的局限性。最后，该研究提供了一种基于使用神经网络的自适应方案，以克服这些限制并应对这些挑战。