# Transfer Learning as an Essential Tool for Digital Twins in Renewable Energy Systems

arXiv: https://arxiv.org/abs/2203.05026 · HTML: https://mlsanity.com/paper/2203.05026/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 9 Mar 2022.

## Builds on (most cited first, 9 of 9)

- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Meta-Transfer Learning for Few-Shot Learning](https://mlsanity.com/paper/1812.02391/) (2018)
- [Beyond Sharing Weights for Deep Domain Adaptation](https://mlsanity.com/paper/1603.06432/) (2016)
- [Latent Multi-task Architecture Learning](https://mlsanity.com/paper/1705.08142/) (2017)
- [A Transfer Learning Framework for Anomaly Detection Using Model of Normality](https://mlsanity.com/paper/2011.06210/) (2020)
- [Hyper-Process Model: A Zero-Shot Learning algorithm for Regression Problems based on Shape Analysis](https://mlsanity.com/paper/1810.10330/) (2018)
- [A Multi-Scheme Ensemble Using Coopetitive Soft-Gating With Application to Power Forecasting for Renewable Energy Generation](https://mlsanity.com/paper/1803.06344/) (2018)
- [Joint auto-encoders: a flexible multi-task learning framework](https://mlsanity.com/paper/1705.10494/) (2017)
- [Transfer Learning in the Field of Renewable Energies -- A Transfer Learning Framework Providing Power Forecasts Throughout the Lifecycle of Wind Farms After Initial Connection to the Electrical Grid](https://mlsanity.com/paper/1906.01168/) (2019)

## Abstract (arXiv)

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.