Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Short-term load forecasting using neural attention model based on EMD

  • Original Paper
  • Published:
Electrical Engineering Aims and scope Submit manuscript

Abstract

The accuracy of short-term load forecasting plays an important role in the operation of the power system. However, because of the randomness of load data, it is a difficult task to provide accurate load forecasting. In this work, a short-term load forecasting approach integrating empirical mode decomposition (EMD), bidirectional long short-term memory (BiLSTM) and attention mechanism is proposed. At first, the electric load series are decomposed into several intrinsic mode functions (IMFs) by EMD. Then a BiLSTM neural network based on attention mechanism is applied on each of the extracted IMFs to predict the tendencies of these IMFs. At last, the prediction results of all IMFs are combined to get the final prediction result of electric load. The proposed approach is evaluated on a real-world dataset from Australian Energy Market Operator. The experimental results demonstrates that the prediction accuracy of the proposed approach can be greatly improved, compared with the other 7 benchmark models. The experiments also showed that the recommended number of IMFs are either 3 or 4 based on both prediction accuracy and running time.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Referencess

  1. Hernandez L et al (2014) A survey on electric power demand forecasting: future trends in smart grids, microgrids and smart buildings. IEEE Commun Surv Tutor 16(3):1460–1495

    Article  Google Scholar 

  2. Raza MQ, Khosravi A (2015) A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renew Sustain Energy Rev 50:1352–1372

    Article  Google Scholar 

  3. Hong T, Fan S (2016) Probabilistic electric load forecasting: a tutorial review. Int J Forecast 32(3):914–938

    Article  Google Scholar 

  4. Liu N, Babushkin V, Afshari A (2014) Short-term forecasting of temperature driven electricity load using time series and neural network model. J Clean Energy Technol 2(4):327–331

    Article  Google Scholar 

  5. Bercu S, Proïa F (2013) A SARIMAX coupled modelling applied to individual load curves intraday forecasting. J Appl Stat 40(6):1333–1348

    Article  MathSciNet  Google Scholar 

  6. Moon J, Kim Y, Son M, Hwang E (2018) Hybrid short-term load forecasting scheme using random forest and multilayer perceptron. Energies 11(12):3283

    Article  Google Scholar 

  7. Wang X, Yaqi W (2016) A hybrid model of EMD and PSO-SVR for short-term load forecasting in residential quarters. Math Probl Eng,2016,(2016–12–26) 2016(2016):1–10.

  8. Yu Y et al (2019) Forecasting a short-term wind speed using a deep belief network combined with a local predictor. IEEJ Trans Electr Electron Eng 14(2):238–244

    Article  Google Scholar 

  9. Samuel IA et al (2019) Artificial neural network base short-term electricity load forecasting: a case study of a 132/33 kv transmission sub-station. Int J Energy Econ Policy 10(2):200–205

    Article  Google Scholar 

  10. Sun G et al (2020) Short-term building load forecast based on a data-mining feature selection and LSTM-RNN method. IEEJ Trans Electr Electron Eng 15(7):1002–1010

    Article  Google Scholar 

  11. Bedi J, Toshniwal D (2020) Energy load time-series forecast using decomposition and autoencoder integrated memory network. Appl Soft Comput 93:106390

    Article  Google Scholar 

  12. Li H et al (2020) Ultra-short-term load demand forecast model framework based on deep learning. Energies 13(18):4900

    Article  Google Scholar 

  13. Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv:1409.0473.

  14. Luong T, Pham H, Manning CD (2015) Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 conference on empirical methods in natural language processing. Association for Computational Linguistics, Lisbon, Portugal, pp 1412–1421. https://doi.org/10.18653/v1/D15-1166

  15. Choi H, Cho K, Bengio Y (2018) Fine-grained attention mechanism for neural machine translation. Neurocomputing 284:171–176

    Article  Google Scholar 

  16. Lin J, Shao Y, Djenouri Y, Yun U (2021) ASRNN: a recurrent neural network with an attention model for sequence labelling. Knowl-Based Syst 212:106548

    Article  Google Scholar 

  17. Qin CX, Qu D (2020) Towards understanding attention-based speech recognition models. IEEE Access 99:1–1

    Google Scholar 

  18. Cun X, Pun CM (2020) Improving the harmony of the composite image by spatial-separated attention module. IEEE Trans Image Process 99:1–1

    Google Scholar 

  19. Ali SM, Farid G (2019) Image processing based optical flow estimation using dilated convolution & channel attention methodologies. J Flow Vis Image Process 26(4)

  20. Mandal BN, Chakrabarti A (2003) A generalization to the hybrid Fourier transform and its application. Appl Math Lett 16(5):703–708

    Article  MathSciNet  Google Scholar 

  21. Benedetto JJ, Frazier MW, Torrésani B (1994) Wavelets: mathematics and applications. Phys Today 47(11):90–91

    Article  Google Scholar 

  22. Huang NE et al (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc A 454(1971):903–995

    MathSciNet  MATH  Google Scholar 

  23. Qiu X et al (2017) Empirical mode decomposition based ensemble deep learning for load demand time series forecasting. Appl Soft Comput 54:246–255

    Article  Google Scholar 

  24. Fan GF et al (2016) Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression. Neurocomputing 173:958–970

    Article  Google Scholar 

  25. Ghelardoni L et al (2013) Energy load forecasting using empirical mode decomposition and support vector regression. IEEE Trans Smart Grid 4(1):549–556

    Article  Google Scholar 

  26. Australian Energy Market Operator (AEMO). Available online: www.aemo.com.au. Accessed Aug 2021

Download references

Author information

Authors and Affiliations

  1. School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, Fujian, 361024, P.R. China

    Zhaorui Meng, Yanqi Xie & Jinhua Sun

Authors
  1. Zhaorui Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yanqi Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jinhua Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhaorui Meng.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Meng, Z., Xie, Y. & Sun, J. Short-term load forecasting using neural attention model based on EMD. Electr Eng 104, 1857–1866 (2022). https://doi.org/10.1007/s00202-021-01420-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00202-021-01420-4

Keywords

Search

Navigation