  Meter-level load forecasting is crucial for efficient energy management and
power system planning for Smart Grids (SGs), in tasks associated with
regulation, dispatching, scheduling, and unit commitment of power grids.
Although a variety of algorithms have been proposed and applied on the field,
more accurate and robust models are still required: the overall utility cost of
operations in SGs increases 10 million currency units if the load forecasting
error increases 1%, and the mean absolute percentage error (MAPE) in
forecasting is still much higher than 1%. Transformers have become the new
state-of-the-art in a variety of tasks, including the ones in computer vision,
natural language processing and time series forecasting, surpassing alternative
neural models such as convolutional and recurrent neural networks. In this
letter, we present a new state-of-the-art Transformer-based algorithm for the
meter-level load forecasting task, which has surpassed the former
state-of-the-art, LSTM, and the traditional benchmark, vanilla RNN, in all
experiments by a margin of at least 13% in MAPE.
