Skip to main content

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

Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 175)

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.

Keywords

  • ANN
  • District energy management
  • Grid electricity
  • Smart city

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

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-47729-9_12
  • Chapter length: 11 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   54.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-47729-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   72.00
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
Fig. 10.

References

  1. Farhangi, H.: The path of the smart grid. IEEE Mag. Power Energy 8(1), 18–28 (2010)

    MathSciNet  CrossRef  Google Scholar 

  2. Mourshed, M., Robert, S., Ranalli, A., Messervey, T., Reforgiato, D., Contreau, R., Becue, A., Quinn, K., Rezgui, Y., Lennard, Z.: Smart grid futures: perspectives on the integration of energy and ICT services. Energy Procedia 75, 1132–1137 (2015)

    CrossRef  Google Scholar 

  3. Chao, H.L., Tsai, C.C., Hsiung, P.A., Chou, I.H.: Smart grid as a service: a discussion on design issues. Sci. World J. 2014, 1–11 (2014)

    CrossRef  Google Scholar 

  4. Brusco, G., Burgio, A., Menniti, D., Pinnarelli, A., Sorrentino, N.: Energy management system for an energy district with demand response availability. IEEE Trans. Smart Grid 5(5), 2385–2393 (2014)

    CrossRef  Google Scholar 

  5. Patti, E., Ronzino, A., Osello, A., Verda, V., Acquaviva, A., Macii, E.: District information modeling and energy management. IT Prof. 17(6), 28–34 (2015)

    CrossRef  Google Scholar 

  6. Yuce, B., Rezgui, Y., Mourshed, M.: ANN-GA smart appliance scheduling for optimized energy management in the domestic sector. Energy Build. 111(1), 311–325 (2016)

    CrossRef  Google Scholar 

  7. Yuce, B., Li, H., Rezgui, Y., Petri, I., Jayan, B., Yang, C.: Utilizing artificial neural network to predict energy consumption and thermal comfort level: an indoor swimming pool case study. Energy Build. 80, 45–56 (2014)

    CrossRef  Google Scholar 

  8. Yang, C., Li, H., Rezgui, Y., Petri, I., Yuce, B., Chen, B., Jayan, B.: High throughput computing based distributed genetic algorithm for building energy consumption optimization. Energy Build. 76, 92–101 (2014)

    CrossRef  Google Scholar 

  9. Yuce, B., Rezgui, Y.: An ANN-GA semantic rule-based system to reduce the gap between predicted and actual energy consumption in buildings. IEEE Trans. Autom. Sci. Eng. PP(99), 1–13 (2015)

    CrossRef  Google Scholar 

  10. Dibley, M.J., Li, H., Rezgui, Y., Miles, J.C.: An ontology framework for intelligent sensor-based building monitoring. Autom. Constr. 28, 1–14 (2012)

    CrossRef  Google Scholar 

  11. Fanti, M.P., Mangini, A.M., Roccotelli, M., Ukovich, W., Pizzuti, S.: A control strategy for district energy managemet. In: 2015 IEEE International Conference on Automation Science and Engineering (CASE), Gothenburg, pp. 432–437 (2015)

    Google Scholar 

  12. Kalogirou, S.A.: Application of artificial neural network for energy systems. Appl. Energy 67(1–2), 17–35 (2000)

    CrossRef  Google Scholar 

  13. Ferreira, P.M., Ruano, A.E., Silva, S., Conceicao, E.Z.E.: Neural networks based predictive control for thermal comfort and energy savings in public buildings. Energy Build. 55, 238–251 (2012)

    CrossRef  Google Scholar 

  14. Gonzalez, P.A., Zamarreno, J.M.: Prediction of hourly energy consumption in buildings based on a feedback artificial neural network. Energy Build. 37, 595–601 (2005)

    CrossRef  Google Scholar 

  15. Valerio, A., Giuseppe, M., Gianluca, G., Alessandro, Q., Borean, C.: Intelligent systems for energy prosumer buildings at district level. In: 23rd International Conference on Electricity Distribution (CIRED), pp. 1–5 (2015)

    Google Scholar 

  16. ISSDA, CER Smart Metering Project. http://www.ucd.ie/issda/data/commissionforenergyregulationcer/. Accessed 15 Feb 2016

  17. Gul, M.S., Patidar, S.: Understanding the energy consumption and occupancy of a multi-purpose academic building. Energy Build. 87(1), 155–165 (2015)

    CrossRef  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Baris Yuce .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

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

About this paper

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47728-2

  • Online ISBN: 978-3-319-47729-9

  • eBook Packages: Computer ScienceComputer Science (R0)