English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Meter-level load forecasting is crucial for efficient energy management andpower system planning for Smart Grids (SGs), in tasks associated withregulation, 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 ofoperations in SGs increases 10 million currency units if the load forecastingerror increases 1%, and the mean absolute percentage error (MAPE) inforecasting is still much higher than 1%. Transformers have become the newstate-of-the-art in a variety of tasks, including the ones in computer vision,natural language processing and time series forecasting, surpassing alternativeneural models such as convolutional and recurrent neural networks. In thisletter, we present a new state-of-the-art Transformer-based algorithm for themeter-level load forecasting task, which has surpassed the formerstate-of-the-art, LSTM, and the traditional benchmark, vanilla RNN, in allexperiments by a margin of at least 13% in MAPE.

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