Kapitel/Beitrag aus dem Buch: Schulte, H et al. 2022. Proceedings – 32. Workshop Computational Intelligence: Berlin, 1. – 2. Dezember 2022.
Accurate forecasts of the electrical load are needed to stabilize the electrical
grid and maximize the use of renewable energies. Many good forecasting
methods exist, including neural networks, and we compare them to the recently
developed Transformers, which are the state-of-the-art machine learning
technique for many sequence-related tasks. We apply different types of
Transformers, namely the Time-Series Transformer, the Convolutional Self-
Attention Transformer and the Informer, to electrical load data from Baden-
Württemberg. Our results show that the Transformes give up to 11% better
forecasts than multi-layer perceptrons for long prediction horizons. Furthermore,
we analyze the Transformers’ attention scores to get insights into the
model.
Hertel, M et al. 2022. Evaluation of Transformer Architectures forElectrical Load Time-Series Forecasting. In: Schulte, H et al (eds.), Proceedings – 32. Workshop Computational Intelligence: Berlin, 1. – 2. Dezember 2022. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.58895/ksp/1000151141-6
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Veröffentlicht am 20. November 2022