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.

翻译：在未来的智能电网中，对单个客户层面的精准负荷预测有助于实现本地供需平衡并防止电网中断。随着智能电表的持续推广，受监测客户数量将不断增加，但每个客户的数据量始终有限。我们评估了Transformer负荷预测模型是否受益于迁移学习策略——该策略利用多个客户的负荷时间序列训练一个全局单变量模型。在包含数百个客户负荷时间序列的两个数据集实验中，我们发现全局训练策略优于相关工作中使用的多变量和局部训练策略。平均而言，相比其他两种策略，全局训练策略在从未来一天到一个月的时间尺度上实现了21.8%和12.8%的预测误差降低。与线性模型、多层感知机和LSTM的对比表明，当采用全局训练策略时，Transformer在负荷预测中具有有效性。