Autores UPV
Rubio-Rico Alejandro,
Mengod-Bautista Fernando,
Ruiz-Perdomo Luis,
Lluna-Arriaga Andres,
Cutillas-Sanchez Pedro,
Fuster Roig Vicente Luis
Abstract
Digital Twin technology, which has undergone significant advancements in recent years, holds transformative potential for the manufacturing industry. The benefits it offers are diverse, with applications tailored to specific industrial sectors and organizational needs. A prominent application lies in energy optimization, where predictive consumption models enhance strategic decision-making processes. This article examines methodologies for energy consumption forecasting in complex industrial environments, integrating a state-of-the-art review with novel applied approaches. It evaluates their implementation feasibility, technical challenges, and performance outcomes, offering insights into optimizing energy management through DT-driven solutions. Copyright (C) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
