Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
The Power of the “Pursuit” Learning Paradigm in the Partitioning of Data Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Reservoir Computing Approaches Applied to Energy Management in Industry

verfasst von : Valentina Colla, Ismael Matino, Stefano Dettori, Silvia Cateni, Ruben Matino

Erschienen in: Engineering Applications of Neural Networks

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Echo-State Neural Networks represent a very efficient solution for modelling of dynamic systems, thanks to their particular structure, which allows faithful reproduction of the behavior of the system to model with a usually limited computational burden for a training phase. This aspect favors the deployment of Echo-State Neural networks in the industrial field. In this paper, a novel application of such approach is proposed for the modelling of industrial processes. The developed models are part of a complex system for optimizing the exploitation of process off-gases in an integrated steelwork. Two models are presented and discussed, where both shallow Echo-State Neural Networks and Deep Echo State Neural networks are applied. The achieved results are presented and discussed, by comparing advantages and drawbacks of both approaches.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Outlier Detection in Temporal Spatial Log Data Using Autoencoder for Industry 4.0
Nächstes Kapitel Signal2Vec: Time Series Embedding Representation
Literatur
1.
Porzio, G.F., et al.: Reducing the energy consumption and CO2 emissions of energy intensive industries through decision support systems–an example of application to the steel industry. Appl. Energy 112, 818–833 (2013)CrossRef
2.
Porzio, G.F., et al.: Process integration in energy and carbon intensive industries: an example of exploitation of optimization techniques and decision support. Appl. Therm. Eng. 70(2), 1148–1155 (2014)CrossRef
3.
Porzio, G.F., Nastasi, G., Colla, V., Vannucci, M., Branca, T.A.: Comparison of multi-objective optimization techniques applied to off-gas management within an integrated steelwork. Appl. Energy 136, 1085–1097 (2014)CrossRef
4.
Zhang, Q., Gu, Y.L., Ti, W., Cai, J.J.: Supply and demand forecasting of blast furnace gas based on artificial neural network in iron and steel works. Adv. Mat. Res. 443, 183–188 (2012)
5.
Yang, L., He, K., Zhao, X., Lv, Z.: The prediction for output of blast furnace gas based on genetic algorithm and LSSVM. In: IEEE 9th Conference on Industrial Electronics and Applications, pp. 1493–1498 (2015)
6.
Zhao, J., Wang, W., Liu, Y., Pedrycz, W.: A two-stage online prediction method for a blast furnace gas system and its application. IEEE Trans. Control Syst. Tech. 19(3), 507–520 (2011)CrossRef
7.
Pascanu, R., Mikolov, T., Bengio, Y.: On the difficulty of training recurrent neural networks. In: International Conference on Machine Learning, pp. 1310–1318, February 2013
8.
Schäfer, A.M., Zimmermann, H.G.: Recurrent neural networks are universal approximators. Int. J. Neural Syst. 17(04), 253–263 (2007)CrossRef
9.
Jaeger, H.: The, “echo state” approach to analysing and training recurrent neural networks-with an erratum note. Bonn Ger.: Ger. Natl. Res. Cent. Inf. Technol. GMD Tech. Rep. 148(34), 13 (2001)
10.
Jaeger, H., Haas, H.: Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304(5667), 78–80 (2004)CrossRef
11.
Grigoryeva, L., Ortega, J.P.: Echo state network are universal. Neural Netw. 108, 495–508 (2018)CrossRef
12.
Gallicchio, C., Micheli, A., Pedrelli, L.: Deep reservoir computing: a critical experimental analysis. Neurocomputing 268, 87–99 (2017)CrossRef
13.
Gallicchio, C., Micheli, A.: Echo state property of deep reservoir computing networks. Cogn. Comput. 9(3), 337–350 (2017)CrossRef
14.
Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT Press, Cambridge (2016)MATH
15.
Yildiz, I.B., Jaeger, H., Kiebel, S.J.: Re-visiting the echo state property. Neural Netw. 35, 1–9 (2012)CrossRef
16.
17.
Maat, J.R., Gianniotis, N., Protopapas, P.: Efficient optimization of echo state networks for time series datasets. In: International Joint Conference on Neural Networks (IJCNN), pp. 1–7 (2018)
18.
Gallicchio, C., Micheli, A., Pedrelli, L.: Design of deep echo state networks. Neural Netw. 108, 33–47 (2018)CrossRef
19.
Grubbs, F.E.: Procedures for detecting outlying observation sin samples. Technometrics 11, 1–21 (1969)CrossRef
20.
Knorr, E.M., Ng, R.: Algorithms for mining distance-based outliers in large datasets. In: Proceedings of VLDB, pp. 392–403 (2003)
21.
Cateni, S., Colla, V., Nastasi, G.: A multivariate fuzzy system applied for outliers detection. J. Intell. Fuzzy Syst. 24(4), 889–903 (2013)MathSciNet
22.
Cateni, S., Colla, V., Vannucci, M.: A fuzzy logic-based method for outliers detection. In: Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2007, pp. 561–566 (2007)
23.
Mahalanobis, P.C.: On the generalized distance in statistics. Proc. Natl. Inst. Sci. India 4, 9–55 (1936)MATH
24.
Grandoni, F.: A note on the complexity of minimum dominating set. J. Discrete Algorithms 4(2), 209–214 (2006)MathSciNetCrossRef
25.
Cateni, S., Colla, V., Vannucci, M.: General purpose input variables extraction: a genetic algorithm based procedure GIVE a GAP. In: 9th International Conference on Intelligent Systems Design and Applications, ISDA 2009, pp. 1278–1283 (2009)
26.
Cateni, S., Colla, V., Vannucci, M.: A genetic algorithm-based approach for selecting input variables and setting relevant network parameters of a SOM-based classifier. Int. J. Simul. Syst. Sci. Technol. 12(2), 30–37 (2011)
Metadaten
Titel
Reservoir Computing Approaches Applied to Energy Management in Industry
verfasst von
Valentina Colla
Ismael Matino
Stefano Dettori
Silvia Cateni
Ruben Matino
Copyright-Jahr
2019
Buch
Engineering Applications of Neural Networks

Print ISBN: 978-3-030-20256-9

Electronic ISBN: 978-3-030-20257-6

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-20257-6_6

Premium Partner

    Bildnachweise