Unraveling the complexity: A taxonomy for characterizing and structuring smart energy services in the building sector

Fabri, Lukas; Weissflog, Jan; Wenninger, Simon

doi:10.1016/j.jclepro.2024.142522

Journal of Cleaner Production

2024

DOI: 10.1016/j.jclepro.2024.142522

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Unraveling the complexity: A taxonomy for characterizing and structuring smart energy services in the building sector

Lukas Fabri,

Jan Weissflog,

Simon Wenninger

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2024

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Cited by 2 publications

References 51 publications

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Developing a Smart Energy Service Canvas

Günter,

Fabri,

Wenninger

et al. 2024

Schmalenbach J Bus Res

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Rising energy and CO2 prices are driving industrial companies to focus increasingly on energy efficiency and flexibility to remain competitive. Besides technical improvements, smart energy services like nonintrusive load monitoring (NILM) are promising approaches to increasing a company’s energy efficiency and flexibility potential. Despite its advantages, smart energy services are not yet widely used in practice, and service providers are cautious. Existing research investigates almost exclusively technical aspects of smart energy service applications and systems, particularly for NILM, while a business model perspective is missing. This study addresses this research gap following a design science research approach with literature research and expert interviews, and develops a business model framework—the Smart Energy Service Canvas (SESC). The SESC is a practical and empirically validated tool to formalize, structure, and implement a business idea on smart energy services.

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Developing a Smart Energy Service Canvas

Günter,

Fabri,

Wenninger

et al. 2024

Schmalenbach J Bus Res

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La dualidad de la inteligencia artificial en la sostenibilidad de las cadenas de suministro: una revisión narrativa

Amado Mateus

2024

epsir

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Introducción: En la era digital actual, la inteligencia artificial (IA) se posiciona como una herramienta crucial para avanzar hacia cadenas de suministro sostenibles, abordando ineficiencias y reduciendo emisiones de carbono derivadas de la creciente demanda energética. Metodología: Se realizó una revisión narrativa de la literatura, evaluando artículos publicados en las bases de datos Scopus y Science Direct entre 2022 y 2024, para capturar los avances recientes del impacto de la IA en la sostenibilidad de las cadenas de suministro. Resultados: Los hallazgos subrayan la capacidad de la IA para optimizar procesos logísticos, mejorar la predicción de la demanda y gestionar inventarios de manera eficiente, reduciendo la huella de carbono y optimizando el uso de recursos. Discusión: Aunque los beneficios son significativos, la implementación de la IA enfrenta desafíos como el alto consumo energético y la complejidad en la integración de datos. Es esencial considerar las implicaciones éticas y sociales para maximizar los beneficios y minimizar los impactos negativos. Conclusiones: La integración de la IA en la gestión de la cadena de suministro representa un avance significativo en sostenibilidad y eficiencia operativa. Se requieren tecnologías más eficientes y políticas que apoyen la adopción de IA sostenible para superar los desafíos y maximizar los beneficios.

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Unraveling the complexity: A taxonomy for characterizing and structuring smart energy services in the building sector

Cited by 2 publications

References 51 publications

Developing a Smart Energy Service Canvas

Developing a Smart Energy Service Canvas

La dualidad de la inteligencia artificial en la sostenibilidad de las cadenas de suministro: una revisión narrativa

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