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Energy Resources Intelligent Management using on line real-time simulation: A decision support tool for sustainable manufacturing

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  • Cassettari, Lucia
  • Bendato, Ilaria
  • Mosca, Marco
  • Mosca, Roberto

Abstract

At a historic time when the eco-sustainability of industrial manufacturing is considered one of the cornerstones of relations between people and the environment, the use of energy from Renewable Energy Sources (RES) has become a fundamental element of this new vision. After years of vain attempts to hammer out an agreement to significantly reduce CO2 emissions produced by the burning of fossil fuels, a binding global accord was finally reached (Paris December 2015 - New York April 2016).

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  • Cassettari, Lucia & Bendato, Ilaria & Mosca, Marco & Mosca, Roberto, 2017. "Energy Resources Intelligent Management using on line real-time simulation: A decision support tool for sustainable manufacturing," Applied Energy, Elsevier, vol. 190(C), pages 841-851.
    • Handle: RePEc:eee:appene:v:190:y:2017:i:c:p:841-851
    DOI: 10.1016/j.apenergy.2017.01.009
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    References listed on IDEAS

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    1. Amrouche, Badia & Le Pivert, Xavier, 2014. "Artificial neural network based daily local forecasting for global solar radiation," Applied Energy, Elsevier, vol. 130(C), pages 333-341.
    2. Coelho, Vitor N. & Coelho, Igor M. & Coelho, Bruno N. & Reis, Agnaldo J.R. & Enayatifar, Rasul & Souza, Marcone J.F. & Guimarães, Frederico G., 2016. "A self-adaptive evolutionary fuzzy model for load forecasting problems on smart grid environment," Applied Energy, Elsevier, vol. 169(C), pages 567-584.
    3. Masa-Bote, D. & Castillo-Cagigal, M. & Matallanas, E. & Caamaño-Martín, E. & Gutiérrez, A. & Monasterio-Huelín, F. & Jiménez-Leube, J., 2014. "Improving photovoltaics grid integration through short time forecasting and self-consumption," Applied Energy, Elsevier, vol. 125(C), pages 103-113.
    4. Azcárate, Cristina & Mallor, Fermín & Mateo, Pedro, 2017. "Tactical and operational management of wind energy systems with storage using a probabilistic forecast of the energy resource," Renewable Energy, Elsevier, vol. 102(PB), pages 445-456.
    5. Bendato, Ilaria & Bonfiglio, Andrea & Brignone, Massimo & Delfino, Federico & Pampararo, Fabio & Procopio, Renato, 2017. "A real-time Energy Management System for the integration of economical aspects and system operator requirements: Definition and validation," Renewable Energy, Elsevier, vol. 102(PB), pages 406-416.
    6. Pascual, Julio & Barricarte, Javier & Sanchis, Pablo & Marroyo, Luis, 2015. "Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting," Applied Energy, Elsevier, vol. 158(C), pages 12-25.
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    Cited by:

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    4. Jingjing Xu & Lei Wang, 2017. "A Feedback Control Method for Addressing the Production Scheduling Problem by Considering Energy Consumption and Makespan," Sustainability, MDPI, vol. 9(7), pages 1-14, July.
    5. Athar Ajaz Khan & János Abonyi, 2022. "Simulation of Sustainable Manufacturing Solutions: Tools for Enabling Circular Economy," Sustainability, MDPI, vol. 14(15), pages 1-40, August.
    6. Hsiao, Kai-Long, 2017. "To promote radiation electrical MHD activation energy thermal extrusion manufacturing system efficiency by using Carreau-Nanofluid with parameters control method," Energy, Elsevier, vol. 130(C), pages 486-499.
    7. Gibb, Duncan & Johnson, Maike & Romaní, Joaquim & Gasia, Jaume & Cabeza, Luisa F. & Seitz, Antje, 2018. "Process integration of thermal energy storage systems – Evaluation methodology and case studies," Applied Energy, Elsevier, vol. 230(C), pages 750-760.
    8. Tan, Daniel & Suvarna, Manu & Shee Tan, Yee & Li, Jie & Wang, Xiaonan, 2021. "A three-step machine learning framework for energy profiling, activity state prediction and production estimation in smart process manufacturing," Applied Energy, Elsevier, vol. 291(C).
    9. Favi, Claudio & Marconi, Marco & Mandolini, Marco & Germani, Michele, 2022. "Sustainable life cycle and energy management of discrete manufacturing plants in the industry 4.0 framework," Applied Energy, Elsevier, vol. 312(C).
    10. Caro-Ruiz, C. & Lombardi, P. & Richter, M. & Pelzer, A. & Komarnicki, P. & Pavas, A. & Mojica-Nava, E., 2019. "Coordination of optimal sizing of energy storage systems and production buffer stocks in a net zero energy factory," Applied Energy, Elsevier, vol. 238(C), pages 851-862.
    11. Robert Ojstersek & Borut Buchmeister, 2020. "Simulation Modeling Approach for Collaborative Workplaces’ Assessment in Sustainable Manufacturing," Sustainability, MDPI, vol. 12(10), pages 1-18, May.

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