A New State-of-the-Art Transformers-Based Load Forecaster on the Smart Grid Domain

TL;DR

This paper introduces a new Transformer-based load forecasting model for Smart Grids, achieving over 13% improvement in accuracy compared to previous models like LSTM and RNN, thereby enhancing energy management efficiency.

Contribution

The paper presents a novel Transformer-based algorithm that outperforms existing models in meter-level load forecasting for Smart Grids.

Findings

Transformer model surpasses LSTM and RNN in MAPE by at least 13%

The proposed model improves forecasting accuracy significantly

Enhanced load prediction can reduce operational costs in Smart Grids

Abstract

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.