3 research outputs found
Transformer Training Strategies for Forecasting Multiple Load Time Series
In the smart grid of the future, accurate load forecasts on the level of
individual clients can help to balance supply and demand locally and to prevent
grid outages. While the number of monitored clients will increase with the
ongoing smart meter rollout, the amount of data per client will always be
limited. We evaluate whether a Transformer load forecasting model benefits from
a transfer learning strategy, where a global univariate model is trained on the
load time series from multiple clients. In experiments with two datasets
containing load time series from several hundred clients, we find that the
global training strategy is superior to the multivariate and local training
strategies used in related work. On average, the global training strategy
results in 21.8% and 12.8% lower forecasting errors than the two other
strategies, measured across forecasting horizons from one day to one month into
the future. A comparison to linear models, multi-layer perceptrons and LSTMs
shows that Transformers are effective for load forecasting when they are
trained with the global training strategy
Towards Automatic Parsing of Structured Visual Content through the Use of Synthetic Data
Structured Visual Content (SVC) such as graphs, flow charts, or the like are used by authors to illustrate various concepts. While such depictions allow the average reader to better understand the contents, images containing SVCs are typically not machine-readable. This, in turn, not only hinders automated knowledge aggregation, but also the perception of displayed in-formation for visually impaired people. In this work, we propose a synthetic dataset, containing SVCs in the form of images as well as ground truths. We show the usage of this dataset by an application that automatically extracts a graph representation from an SVC image. This is done by training a model via common supervised learning methods. As there currently exist no large-scale public datasets for the detailed analysis of SVC, we propose the Synthetic SVC (SSVC) dataset comprising 12,000 images with respective bounding box annotations and detailed graph representations. Our dataset enables the development of strong models for the interpretation of SVCs while skipping the time-consuming dense data annotation. We evaluate our model on both synthetic and manually annotated data and show the transferability of synthetic to real via various metrics, given the presented application. Here, we evaluate that this proof of concept is possible to some extend and lay down a solid baseline for this task. We discuss the limitations of our approach for further improvements. Our utilized metrics can be used as a tool for future comparisons in this domain. To enable further research on this task, the dataset is publicly available at https://bit.ly/3jN1pJ