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Knowledge-Based Short-Term Load-Forecasting for Maritime Container Terminals

An evaluation of two approaches based on operation plans
  • Norman IhleEmail author
  • Axel Hahn
Conference paper

Zusammenfassung

Short-term load-forecasting for individual industrial customers has become an important issue, as interest in demand response and demand side management in modern energy systems has increased. Integrating knowledge of planned operations at industrial sites into the following day’s energy-consumption forecasting process provides advantages. In the case of a maritime container terminal, these operation plans are based on the list of ship arrivals and departures. In this paper two different approaches to integrating this knowledge are introduced: (i) case-based reasoning, similar to a lazy-learner that uses available knowledge during the forecasting process, and (ii) an Artificial Neural Network that has to be trained before the actual forecasting process occurs. The outcomes show that integrating more knowledge into the forecasting process enables better results in terms of forecast accuracy

Schlüsselwörter

short-term load-forecasting case-based reasoning Artificial Neural Networks maritime container terminals 

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Literatur

  1. [1] J. Schmidt, L.-P. Lauven, N. Ihle and L. M. Kolbe, “Demand side integration for electric transport vehicles, “International Journal of Energy Sector Management, pp. 471 - 495, 9 2015.Google Scholar
  2. [2] T. Hong, “Short Term Electric Load Forecasting,” North Carolina State University University, Raleigh, 2010.Google Scholar
  3. [3] A. K. Singh, I. S. Khatoon, M. Muazzam und D. K. Chaturvedi, ,,An Overview of electricity demand forcasting techniques, “Network and Complex Systems, pp. 38-48, 3 2013.Google Scholar
  4. [4] J. Catalão, S. Mariano, V. Mendes und L.Ferreira, “An artificial neural network approach for short-term electricity prices forecasting, “in International Conference on Intelligent Systems Applications to Power Systems, IEEE, 2007.Google Scholar
  5. [5] H.-O. Günther and K.-H. Kim, “Container terminals and terminal Operations, “OR Spectrum, pp. 437-445, 2006.Google Scholar
  6. [6] N. Grundmeier, N. Ihle und A. Hahn, “A Discrete Event-driven Simulation Model to Analyse Power Consumption Processes in Container Terminals, “Simulation in Production and Logistics, Fraunhofer IRB Verlag, Stuttgart, 2015.Google Scholar
  7. [7] A. Aamodt und E. Plaza, “Case-Based Reasoning: Foundational issues, methodological variations, and system approaches, “AI communications , pp. 39-59, 1994.Google Scholar
  8. [8] R. Bergmann, “Experience management: foundations, development methodology, and internet-based applications,” Springer-Verlag, 2002.Google Scholar
  9. [9] N.Ihle, “Case Representation and Adaptation for Short-Term Load Forecast at a Container Terminal, “24th International Conference on Case-Based Reasoning (ICCBR) 2016 - Workshop Proceedings; 2016Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH 2017

Authors and Affiliations

  1. 1.R+D Division EnergyOFFIS e.V. - Institute for Information TechnologyOldenburgDeutschland
  2. 2.Department of Computing ScienceCarl von Ossietzky University of OldenburgOldenburgDeutschland

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