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Environment Modelling for Spatial Load Forecasting

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Book cover Agent Environments for Multi-Agent Systems IV

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9068))

Abstract

We present here an autonomous agent-based system tightly coupled with Geographic Information Systems (GIS). Our objective is to model a city’s dynamic in order to foresee both its urban evolution and the influence that the appearance of new settlements has on the overall electricity demand. This environment is deployed on a GIS-based Multi-Agent System platform where the geographical and communication components have been abstracted from the agent system onto the environment. The configuration model uses geographical information in order to improve the agents’ connection and perception of their surroundings. Based on the agent’s choices, we forecast urban evolution and derive the expected increment in electric consumption. We have validated our approach with real data and discuss here our conclusions.

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Acknowledgments

This research was partially funded by ITEA2 Nemo & Coded (ITI-20110864) and the Ph.D. grant PRE_2013_1_516 given by the Basque Government. The authors would also like to thank the reviewers for their comments and suggestions to improve this paper.

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Authors and Affiliations

  1. Deusto Institute of Technology – DeustoTech Energy, University of Deusto, Avda. Universidades 24, 48007, Bilbao, Spain

    Ander Pijoan, Oihane Kamara-Esteban & Cruz E. Borges

Authors
  1. Ander Pijoan
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  2. Oihane Kamara-Esteban
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  3. Cruz E. Borges
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Corresponding author

Correspondence to Ander Pijoan .

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Editors and Affiliations

  1. Linnaeus University Department of Computer Science, Växjö, Sweden

    Danny Weyns

  2. Université Montpellier II , Montpellier, France

    Fabien Michel

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Pijoan, A., Kamara-Esteban, O., Borges, C.E. (2015). Environment Modelling for Spatial Load Forecasting. In: Weyns, D., Michel, F. (eds) Agent Environments for Multi-Agent Systems IV. Lecture Notes in Computer Science(), vol 9068. Springer, Cham. https://doi.org/10.1007/978-3-319-23850-0_12

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  • DOI: https://doi.org/10.1007/978-3-319-23850-0_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23849-4

  • Online ISBN: 978-3-319-23850-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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