  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.
