# Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time-Series Forecast

arXiv: https://arxiv.org/abs/2204.13908 · HTML: https://mlsanity.com/paper/2204.13908/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 29 Apr 2022.

## Builds on (most cited first, 12 of 12)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Weight Uncertainty in Neural Networks](https://mlsanity.com/paper/1505.05424/) (2015)
- [A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay](https://mlsanity.com/paper/1803.09820/) (2018)
- [Entity Embeddings of Categorical Variables](https://mlsanity.com/paper/1604.06737/) (2016)
- [Transfer learning for time series classification](https://mlsanity.com/paper/1811.01533/) (2018)
- [Emerging Relation Network and Task Embedding for Multi-Task Regression Problems](https://mlsanity.com/paper/2004.14034/) (2020)
- [Hyper-Process Model: A Zero-Shot Learning algorithm for Regression Problems based on Shape Analysis](https://mlsanity.com/paper/1810.10330/) (2018)
- [Adaptive Transfer Learning in Deep Neural Networks: Wind Power Prediction using Knowledge Transfer from Region to Region and Between Different Task Domains](https://mlsanity.com/paper/1810.12611/) (2018)
- [Influences in Forecast Errors for Wind and Photovoltaic Power: A Study on Machine Learning Models](https://mlsanity.com/paper/1905.13668/) (2019)

## Built on by (most cited first, 4 of 4)

- [Grape Cold Hardiness Prediction via Multi-Task Learning](https://mlsanity.com/paper/2209.10585/) (2022)
- [Multi-Task Learning for Budbreak Prediction](https://mlsanity.com/paper/2301.01815/) (2023)
- [Parameterized Neural Networks for Finance](https://mlsanity.com/paper/2304.08883/) (2023)
- [Multi-Task Learning for Sparsely-Labeled Time Series: A Case Study on Cold-Hardiness Modeling](https://mlsanity.com/paper/2609.09062/) (2026)

## Abstract (arXiv)

Task embeddings in multi-layer perceptrons for multi-task learning and inductive transfer learning in renewable power forecasts have recently been introduced. In many cases, this approach improves the forecast error and reduces the required training data. However, it does not take the seasonal influences in power forecasts within a day into account, i.e., the diurnal cycle. Therefore, we extended this idea to temporal convolutional networks to consider those seasonalities. We propose transforming the embedding space, which contains the latent similarities between tasks, through convolution and providing these results to the network's residual block. The proposed architecture significantly improves up to 25 percent for multi-task learning for power forecasts on the EuropeWindFarm and GermanSolarFarm dataset compared to the multi-layer perceptron approach. Based on the same data, we achieve a ten percent improvement for the wind datasets and more than 20 percent in most cases for the solar dataset for inductive transfer learning without catastrophic forgetting. Finally, we are the first proposing zero-shot learning for renewable power forecasts to provide predictions even if no training data is available.