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.
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,根据从数字天气预测得出的天气特征预测,对不断增长的电力预报需求作出预测是一项关键的挑战。我们通过进行首次彻底试验,为可再生能源预测的传输学习进行模型选择和调整,从而弥补这些差距,我们采用了667个风能和光伏公园计算机愿景领域的最新成果。根据我们的知识,这使它成为在可再生能源预测中进行转移学习的最为广泛的研究,减少了计算努力并改进了预测错误。因此,我们采用基于不同季节的目标数据并限制培训数据数量的源模型。作为当前技术的延伸,我们利用巴耶斯线性回归来预测基于从神经网络中提取的特征的响应情况,从而预测反应,从而采用最新的结果。这一方法超越了从多天培训中提取的数据的模型。我们只能通过多个模型来大大地展示。