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A knowledge discovery in databases approach for industrial microgrid planning

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  • Gamarra, Carlos
  • Guerrero, Josep M.
  • Montero, Eduardo

Abstract

The progressive application of Information and Communication Technologies to industrial processes has increased the amount of data gathered by manufacturing companies during last decades. Nowadays some standardized management systems, such as ISO 50.001 and ISO 14.001, exploit these data in order to minimize the environmental impact of manufacturing processes. At the same time, microgrid architectures are progressively being developed, proving to be suitable for supplying energy to continuous and intensive consumptions, such as manufacturing processes.

Suggested Citation

  • Gamarra, Carlos & Guerrero, Josep M. & Montero, Eduardo, 2016. "A knowledge discovery in databases approach for industrial microgrid planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 615-630.
    • Handle: RePEc:eee:rensus:v:60:y:2016:i:c:p:615-630
    DOI: 10.1016/j.rser.2016.01.091
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    References listed on IDEAS

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    1. Bracco, Stefano & Delfino, Federico & Pampararo, Fabio & Robba, Michela & Rossi, Mansueto, 2014. "A mathematical model for the optimal operation of the University of Genoa Smart Polygeneration Microgrid: Evaluation of technical, economic and environmental performance indicators," Energy, Elsevier, vol. 64(C), pages 912-922.
    2. Gamarra, Carlos & Guerrero, Josep M., 2015. "Computational optimization techniques applied to microgrids planning: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 413-424.
    3. Zhang, Di & Samsatli, Nouri J. & Hawkes, Adam D. & Brett, Dan J.L. & Shah, Nilay & Papageorgiou, Lazaros G., 2013. "Fair electricity transfer price and unit capacity selection for microgrids," Energy Economics, Elsevier, vol. 36(C), pages 581-593.
    4. Soshinskaya, Mariya & Crijns-Graus, Wina H.J. & van der Meer, Jos & Guerrero, Josep M., 2014. "Application of a microgrid with renewables for a water treatment plant," Applied Energy, Elsevier, vol. 134(C), pages 20-34.
    5. Leif Hanrahan, Brian & Lightbody, Gordon & Staudt, Lawrence & G. Leahy, Paul, 2014. "A powerful visualization technique for electricity supply and demand at industrial sites with combined heat and power and wind generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 860-869.
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    Cited by:

    1. Vasja Roblek & Maja Meško & Alojz Krapež, 2016. "A Complex View of Industry 4.0," SAGE Open, , vol. 6(2), pages 21582440166, June.
    2. Arafat, M.Y. & Hossain, M.J. & Alam, Md Morshed, 2024. "Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
    3. Xu, Zhirong & Yang, Ping & Zheng, Chengli & Zhang, Yujia & Peng, Jiajun & Zeng, Zhiji, 2018. "Analysis on the organization and Development of multi-microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2204-2216.
    4. Fang, Xinli & Yang, Qiang & Dong, Wei, 2018. "Fuzzy decision based energy dispatch in offshore industrial microgrid with desalination process and multi-type DGs," Energy, Elsevier, vol. 148(C), pages 744-755.
    5. Jerman Andrej & Erenda Ivan & Bertoncelj Andrej, 2019. "The Influence of Critical Factors on Business Model at a Smart Factory: A Case Study," Business Systems Research, Sciendo, vol. 10(1), pages 42-52, April.
    6. Bahram Shakerighadi & Amjad Anvari-Moghaddam & Juan C. Vasquez & Josep M. Guerrero, 2018. "Internet of Things for Modern Energy Systems: State-of-the-Art, Challenges, and Open Issues," Energies, MDPI, vol. 11(5), pages 1-23, May.

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