Model Selection, Adaptation, and Combination for Transfer Learning in Wind and Photovoltaic Power Forecasts
This addresses the crucial challenge of providing accurate power forecasts for renewable energy systems, though it is incremental as it adapts existing computer vision methods to a new domain.
The paper tackles the problem of model selection and adaptation for transfer learning in renewable power forecasts by conducting the first extensive experiment on 667 wind and photovoltaic parks, reducing computational effort and improving forecast error, with an approach using Bayesian linear regression outperforming the baseline using only seven days of training data.
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.