Cognitive Systems for the Energy Efficiency Industry

Javier Arevalo, Juan-Ignacio Latorre-Biel, Francisco-Javier Flor-Montalvo, Mercedes Perez-Parte and Julio Blanco ()
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Javier Arevalo: Department of Mechanical Engineering, Public University of Navarra, Av de Tarazona s/n, 31500 Tudela, Navarra, Spain
Juan-Ignacio Latorre-Biel: Department of Mechanical Engineering, Public University of Navarra, Av de Tarazona s/n, 31500 Tudela, Navarra, Spain
Francisco-Javier Flor-Montalvo: Department of Mechanical Engineering, Public University of Navarra, Av de Tarazona s/n, 31500 Tudela, Navarra, Spain
Mercedes Perez-Parte: Department of Mechanical Engineering, University of La Rioja, 26004 Logroño, La Rioja, Spain
Julio Blanco: Department of Mechanical Engineering, University of La Rioja, 26004 Logroño, La Rioja, Spain

Energies, 2024, vol. 17, issue 8, 1-16

Abstract: This 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.

Keywords: cognitive systems; energy efficiency industry; cognitive computing applications; artificial consciousness (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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