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 a 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 six datasets. We adopt models based on 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. In fact, with more than 30 days of training data, both proposed model combination techniques achieve similar results to those models trained with a full year of training data.
翻译:最近人们有兴趣在计算机愿景任务中使用模型枢纽,即一套经过预先培训的模型集。为了利用模型枢纽,我们首先选择一个源模型,然后调整模型,以弥补差异。虽然对于计算机愿景任务的模型选择和调整研究还很有限,但对于再生电力领域来说,这甚至更为重要。与此同时,根据天气特征预测数字天气特征预测对不断增长的电力预报需求作出预测是一项关键的挑战。我们缩小了这些差距,方法是进行首次彻底试验,为可再生能源预测的转让学习进行模型选择和调整,采纳了计算机愿景领域对六个数据集的最新成果。我们采用了基于不同季节的数据的模型,并限制培训数据的数量。作为目前艺术状态的延伸,我们利用海湾线性线性回归来预测从神经网络中提取的特征所作的反应。这一方法比基线差7天的培训数据。我们进一步表明,通过聚合多种模型可以大大改进模型的选择和适应方法。事实上,30多天以上的培训模型与全年培训数据相结合。