A New State-of-the-Art Transformers-Based Load Forecaster on the Smart Grid Domain
This addresses the problem of high forecasting errors in Smart Grids, which can increase operational costs by millions, but it is incremental as it applies an existing Transformer paradigm to a specific domain.
The paper tackled meter-level load forecasting for Smart Grids to improve energy management, achieving a new state-of-the-art with at least a 13% reduction in MAPE compared to previous methods like LSTM and vanilla RNN.
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.