Arévalo Royo, JavierLatorre Biel, Juan IgnacioFlor Montalvo, Francisco JavierPérez-Parte, MercedesBlanco Fernández, Julio2024-08-122024-08-122024Arevalo, J., Latorre-Biel, J. I., Flor-Montalvo, F.-J., Perez-Parte, M., Blanco, J. (2024) Cognitive systems for the energy efficiency industry. Energies, 17(8), 1-16. https://doi.org/10.3390/en17081860.1996-107310.3390/en17081860https://academica-e.unavarra.es/handle/2454/50695This review underscores the pivotal role of Cognitive Systems (CS) in enhancing energy efficiency within the industrial sector, exploring the application of sophisticated algorithms, data analytics, and machine learning techniques to the real-time optimization of energy consumption. This methodology has the potential to reduce operational expenses and further diminish environmental repercussions; however, it also leverages data-driven insights and predictive maintenance to foresee equipment malfunctions and modulate energy utilization accordingly. The viability of integrating renewable energy sources is emphasized, supporting a transition towards sustainability. Furthermore, this research includes a bibliometric literature analysis from the past decade on the deployment of CS and Artificial Intelligence in enhancing industrial energy efficiency.application/pdfeng© 2024 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.Artificial consciousnessCognitive computing applicationsCognitive systemsEnergy efficiency industryinfo:eu-repo/semantics/article2024-08-12info:eu-repo/semantics/openAccess