Skip to main content

Short-Term Electricity Demand Forecasting and Warning Signal Generation

  • Chapter
  • First Online:
Smart Metering Design and Applications

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSAPPLSCIENCES))

  • 1525 Accesses

Abstract

Short-Term Electricity Demand Forecasting (STEDF) provides many advantages for electricity suppliers as well as consumers. In this chapter the authors focus on STEDF and how it contributes to demand side load management. Different types of electricity demand forecasting methods are highlighted. A case study is done by selecting a medium voltage industrial consumer to illustrate the applications of STEDF. The model developed for demand forecasting can be used with smart meters to forecast the demand, calculate the maximum demand and to control the demand side loads. Furthermore the forecasted demand can also be used to generate warning signals regarding maximum demand. Ultimately the proposed system will help reduce the demand and save the electricity bill for both residential and industrial consumers.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 64.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Cooray TMJA (2008) Scope of the book. In: Applied time series : analysis and forecasting, Narosa Publishing House, New Delhi, India, p 1–21

    Google Scholar 

  2. Alfares HK, Nazeeruddin M (2002) Electric load forecasting: a literature survey and classification of methods. Int J Syst Sci 33:23–34. doi:10.1080/00207720110067421

    Google Scholar 

  3. Mbamalu GAN, El-Hawary ME (1993) Load forecasting via suboptimal autoregressive models and iteratively recursive least squares estimation. IEEE Trans Power Syst 8:343–348. doi:10.1109/59.221222

    Google Scholar 

  4. Exponential smoothing (2013) en.wikipedia.org/wiki/Exponentialsmoothing. Accessed 7 March 2013

  5. Zhang G, Patuwo BE, Hu MY (1998) Forecasting with artificial neural networks: the state of the art. Int J Forecast 14:35–62. doi:10.1016/S0169-2070(97)00044-7

    Google Scholar 

  6. Jain A, Babita M (2012) Fuzzy modeling and similarity based short term load forecasting using evolutionary particle swarm optimization. In: Proceedings IEEE power and energy society general meeting, San Diego, CA, 22–26 July 2012

    Google Scholar 

  7. Mohamed A, Abu-El-Magd, Naresh KS (1982) Short-term load demand modeling and forecasting: a review. IEEE Trans Syst Man Cybern 12:370–382. doi:10.1109/TSMC.1982.4308827

  8. Autoregressive model (2013) en.wikipedia.org/wiki/Autoregressive_model. Accessed 7 March 2013

  9. Moving average model (2013) en.wikipedia.org/wiki/Moving-average_model. Accessed 7 March 2013

  10. Autoregressive moving average model (2013) en.wikipedia.org/wiki/Autoregressive–moving-average_model. Accessed 7 March 2013

  11. Autoregressive integrated moving average model (2013) en.wikipedia.org/wiki/Autoregressive_integrated_moving_average. Accessed 7 March 2013

  12. ARIMA Model (2013) http://www.jmp.com/support/help/ARIMA_Model.shtml. Accessed 7 March 2013

  13. Towill S (1974) Estimation of maximum demand on a British electricity-board system (forecast periods of 1–3 years). In: Proceedings of the Institute of Electrical Engineers, vol 121, pp. 609–615. doi:10.1049/piee.1974.0142

  14. Sheikh SS, Sharma S (2011) Design and implementation of wireless automatic meter reading system. Eng Sci Technol Int J 3(3):2329–2334

    Google Scholar 

  15. De Silva D, Xinghuo Y, Alahakoon D, Holmes G (2011) Semi-supervised classification of characterized patterns for demand forecasting using smart electricity meters. In: Proceedings electrical machines and systems international conference, Beijing, 20–23 Aug 2011

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kasun Weranga .

Rights and permissions

Reprints and permissions

Copyright information

© 2014 The Author(s)

About this chapter

Cite this chapter

Weranga, K., Kumarawadu, S., Chandima, D.P. (2014). Short-Term Electricity Demand Forecasting and Warning Signal Generation. In: Smart Metering Design and Applications. SpringerBriefs in Applied Sciences and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-4451-82-6_5

Download citation

  • DOI: https://doi.org/10.1007/978-981-4451-82-6_5

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-4451-81-9

  • Online ISBN: 978-981-4451-82-6

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics