  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.
