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An ANN-Based Energy Forecasting Framework for the District Level Smart Grids

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Smart Grid Inspired Future Technologies

Abstract

This study presents an Artificial Neural Network (ANN) based district level smart grid forecasting framework for predicting both aggregated and disaggregated electricity demand from consumers, developed for use in a low-voltage smart electricity grid. To generate the proposed framework, several experimental study have been conducted to determine the best performing ANN. The framework was tested on a micro grid, comprising six buildings with different occupancy patterns. Results suggested an average percentage accuracy of about 96%, illustrating the suitability of the framework for implementation.

B. Yuce—The work has been funded by the European Commission in the context of the MAS2TERING project (the grant number 619682).

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Acknowledgments

The authors would like to acknowledge the financial support of the European Commission in the context of the MAS2TERING project (Ref: 619682) funded under the ICT-2013.6.1 - Smart Energy Grids program.

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Authors and Affiliations

  1. School of Engineering, BRE Centre for Sustainable Engineering, Cardiff University, Queen’s Building, The Parade, Cardiff, CF24 3AA, UK

    Baris Yuce, Monjur Mourshed & Yacine Rezgui

Authors
  1. Baris Yuce
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  2. Monjur Mourshed
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  3. Yacine Rezgui
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Corresponding author

Correspondence to Baris Yuce .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Exeter, Exeter, United Kingdom

    Jia Hu

  2. Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, British Columbia, Canada

    Victor C. M. Leung

  3. University of Essex, Essex, United Kingdom

    Kun Yang

  4. Simula Research Laboratory, Fornebu, Norway

    Yan Zhang

  5. Imperial College, London, United Kingdom

    Jianliang Gao

  6. The Institute of Information and System Science, Xi’an Jiaotong University, Xi’an, China

    Shusen Yang

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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Yuce, B., Mourshed, M., Rezgui, Y. (2017). An ANN-Based Energy Forecasting Framework for the District Level Smart Grids. In: Hu, J., Leung, V., Yang, K., Zhang, Y., Gao, J., Yang, S. (eds) Smart Grid Inspired Future Technologies. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 175. Springer, Cham. https://doi.org/10.1007/978-3-319-47729-9_12

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  • DOI: https://doi.org/10.1007/978-3-319-47729-9_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47728-2

  • Online ISBN: 978-3-319-47729-9

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