  Transfer learning (TL), the next frontier in machine learning (ML), has
gained much popularity in recent years, due to the various challenges faced in
ML, like the requirement of vast amounts of training data, expensive and
time-consuming labelling processes for data samples, and long training duration
for models. TL is useful in tackling these problems, as it focuses on
transferring knowledge from previously solved tasks to new tasks. Digital twins
and other intelligent systems need to utilise TL to use the previously gained
knowledge and solve new tasks in a more self-reliant way, and to incrementally
increase their knowledge base. Therefore, in this article, the critical
challenges in power forecasting and anomaly detection in the context of
renewable energy systems are identified, and a potential TL framework to meet
these challenges is proposed. This article also proposes a feature embedding
approach to handle the missing sensors data. The proposed TL methods help to
make a system more autonomous in the context of organic computing.
