# Model Selection, Adaptation, and Combination for Transfer Learning in Wind and Photovoltaic Power Forecasts

arXiv: https://arxiv.org/abs/2204.13293 · HTML: https://mlsanity.com/paper/2204.13293/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 51 references that are in the map.
- First posted on arXiv 28 Apr 2022.

## Builds on (most cited first, 13 of 13)

- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Explicit Inductive Bias for Transfer Learning with Convolutional Networks](https://mlsanity.com/paper/1802.01483/) (2018)
- [LogME: Practical Assessment of Pre-trained Models for Transfer Learning](https://mlsanity.com/paper/2102.11005/) (2021)
- [Rethinking the Hyperparameters for Fine-tuning](https://mlsanity.com/paper/2002.11770/) (2020)
- [Transfer learning for time series classification](https://mlsanity.com/paper/1811.01533/) (2018)
- [Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs](https://mlsanity.com/paper/2110.10545/) (2021)
- [Emerging Relation Network and Task Embedding for Multi-Task Regression Problems](https://mlsanity.com/paper/2004.14034/) (2020)
- [A Multi-Scheme Ensemble Using Coopetitive Soft-Gating With Application to Power Forecasting for Renewable Energy Generation](https://mlsanity.com/paper/1803.06344/) (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)
- [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)
- [Coopetitive Soft Gating Ensemble](https://mlsanity.com/paper/1807.01020/) (2018)
- [Synthetic Photovoltaic and Wind Power Forecasting Data](https://mlsanity.com/paper/2204.00411/) (2022)

## Built on by (most cited first, 2 of 2)

- [Which Model to Transfer? A Survey on Transferability Estimation](https://mlsanity.com/paper/2402.15231/) (2024)
- [An XAI framework for robust and transparent data-driven wind turbine power curve models](https://mlsanity.com/paper/2304.09835/) (2023)

## Abstract (arXiv)

There is recent interest in using model hubs, a collection of pre-trained models, in computer vision tasks. To utilize the model hub, we first select a source model and then adapt the model for the target to compensate for differences. While there is yet limited research on model selection and adaption for computer vision tasks, this holds even more for the field of renewable power. At the same time, it is a crucial challenge to provide forecasts for the increasing demand for power forecasts based on weather features from a numerical weather prediction. We close these gaps by conducting the first thorough experiment for model selection and adaptation for transfer learning in renewable power forecast, adopting recent results from the field of computer vision on 667 wind and photovoltaic parks. To the best of our knowledge, this makes it the most extensive study for transfer learning in renewable power forecasts reducing the computational effort and improving the forecast error. Therefore, we adopt source models based on target data from different seasons and limit the amount of training data. As an extension of the current state of the art, we utilize a Bayesian linear regression for forecasting the response based on features extracted from a neural network. This approach outperforms the baseline with only seven days of training data. We further show how combining multiple models through ensembles can significantly improve the model selection and adaptation approach.