Adaptive Explainable Continual Learning Framework for Regression Problems with Focus on Power Forecasts
This work addresses the need for adaptive and explainable continual learning in dynamic environments like power forecasting, but it appears incremental as it builds on prior research.
The authors tackled the problem of enabling deep neural networks to learn continuously without forgetting previous knowledge in regression tasks, specifically applying their framework to power generation and consumption forecasting with real-world data.
Compared with traditional deep learning techniques, continual learning enables deep neural networks to learn continually and adaptively. Deep neural networks have to learn new tasks and overcome forgetting the knowledge obtained from the old tasks as the amount of data keeps increasing in applications. In this article, two continual learning scenarios will be proposed to describe the potential challenges in this context. Besides, based on our previous work regarding the CLeaR framework, which is short for continual learning for regression tasks, the work will be further developed to enable models to extend themselves and learn data successively. Research topics are related but not limited to developing continual deep learning algorithms, strategies for non-stationarity detection in data streams, explainable and visualizable artificial intelligence, etc. Moreover, the framework- and algorithm-related hyperparameters should be dynamically updated in applications. Forecasting experiments will be conducted based on power generation and consumption data collected from real-world applications. A series of comprehensive evaluation metrics and visualization tools can help analyze the experimental results. The proposed framework is expected to be generally applied to other constantly changing scenarios.