Arévalo Royo, Javier
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Arévalo Royo
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Javier
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Ingeniería
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Publication Open Access Cognitive systems for the energy efficiency industry(MDPI, 2024) Arévalo Royo, Javier; Latorre Biel, Juan Ignacio; Flor Montalvo, Francisco Javier; Pérez-Parte, Mercedes; Blanco Fernández, Julio; Ingeniería; IngeniaritzaThis 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.Publication Open Access AI algorithms in the agrifood industry: application potential in the Spanish agrifood context(MDPI, 2025-02-17) Arévalo Royo, Javier; Flor Montalvo, Francisco Javier; Latorre Biel, Juan Ignacio; Tino Ramos, Rubén; Martínez Cámara, Eduardo; Blanco Fernández, Julio; Ingeniería; Ingeniaritza; Institute of Smart Cities - ISCThis research explores the prospective implementations of artificial intelligence (AI) algorithms within the agrifood sector, focusing on the Spanish context. AI methodologies, encompassing machine learning, deep learning, and neural networks, are increasingly integrated into various agrifood sectors, including precision farming, crop yield forecasting, disease diagnosis, and resource management. Utilizing a comprehensive bibliometric analysis of scientific literature from 2020 to 2024, this research outlines the increasing incorporation of AI in Spain and identifies the prevailing trends and obstacles associated with it in the agrifood industry. The findings underscore the extensive application of AI in remote sensing, water management, and environmental sustainability. These areas are particularly pertinent to Spain¿s diverse agricultural landscapes. Additionally, the study conducts a comparative analysis between Spain and global research outputs, highlighting its distinctive contributions and the unique challenges encountered within its agricultural sector. Despite the considerable opportunities presented by these technologies, the research identifies key limitations, including the need for enhanced digital infrastructure, improved data integration, and increased accessibility for smaller agricultural enterprises. The paper also outlines future research pathways aimed at facilitating the integration of AI in Spain's agriculture. It addresses cost-effective solutions, data-sharing frameworks, and the ethical and societal implications inherent to AI deployment.