Skip to main content
SpringerLink
Log in

Short-Term Load Forecasting Using EEMD-DAE with Enhanced CNN in Smart Grid

  • Conference paper
  • First Online:
Web, Artificial Intelligence and Network Applications (WAINA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1150))

Abstract

Traditional grid moves toward Smart Grid (SG). In traditional grids, electricity was wasted in generation-transmission-distribution. SG is introduced to solve prior issues. In smart grids, how to utilize massive smart meter’s data in order to improve and promote the efficiency and viability of both generation and demand side is a compelling issue. A good forecasting model makes an acceptable use of all characteristics of the electric loads’ data and also reduces dimensionality of that data. Many data-driven methods have been proposed in the literature for load forecasting. In this paper, EEMD based ECNN model is proposed to forecast load of electricity using AEMO data. From the results, ECNN outperforms benchmark methods especially by applying EEMD for decomposition and DAE for feature extraction.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
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. Wang, Y., Chen, Q., Hong, T., Kang, C.: Review of smart meter data analytics: applications, methodologies, and challenges. IEEE Trans. Smart Grid 10(3), 3125–3148 (2018)

    Article  Google Scholar 

  2. Fan, S., Hyndman, R.J.: Short-term load forecasting based on a semi-parametric additive model. IEEE Trans. Power Syst. 27(1), 134–141 (2011)

    Article  Google Scholar 

  3. Rahman, S., Bhatnagar, R.: An expert system based algorithm for short term load forecast. IEEE Trans. Power Syst. 3(2), 392–399 (1988)

    Article  Google Scholar 

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

    Article  Google Scholar 

  5. Lee, C.M., Ko, C.N.: Short-term load forecasting using lifting scheme and ARIMA models. Expert Syst. Appl. 38(5), 5902–5911 (2011)

    Article  Google Scholar 

  6. Hong, T.: Short term electric load forecasting. Ph.D. thesis, Graduate Program of Operation Research and Department of Electrical and Computer Engineering, North Carolina State University (2010)

    Google Scholar 

  7. Zhang, J., Wei, Y.M., Li, D., Tan, Z., Zhou, J.: Short term electricity load forecasting using a hybrid model. Energy 158, 774–781 (2018)

    Article  Google Scholar 

  8. Yu, C.N., Mirowski, P., Ho, T.K.: A sparse coding approach to household electricity demand forecasting in smart grids. IEEE Trans. Smart Grid 8(2), 738–748 (2016)

    Google Scholar 

  9. Gao, Y., Ge, G., Sheng, Z., Sang, E.: Analysis and solution to the mode mixing phenomenon in EMD. In: 2008 Congress on Image and Signal Processing, vol. 5, pp. 223–227. IEEE, May 2008

    Google Scholar 

  10. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. London. Ser. A: Math. Phys. Eng. Sci. 454(1971), 903–995 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  11. Rilling, G., Flandrin, P., Goncalves, P.: On empirical mode decomposition and its algorithms. In: IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, NSIP-03, Grado, vol. 3, no. 3, pp. 8–11, June 2003

    Google Scholar 

  12. Qingjie, Z., Huayong, Z., Lincheng, S.: A new method for mitigation of end effect in empirical mode decomposition. In: 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010), vol. 1, pp. 400–403. IEEE, March 2010

    Google Scholar 

  13. Qiu, X., Suganthan, P.N., Amaratunga, G.A.: Ensemble incremental learning random vector functional link network for short-term electric load forecasting. Knowl.-Based Syst. 145, 182–196 (2018)

    Article  Google Scholar 

  14. Chen, Y., Kloft, M., Yang, Y., Li, C., Li, L.: Mixed kernel based extreme learning machine for electric load forecasting. Neurocomputing 312, 90–106 (2018)

    Article  Google Scholar 

  15. Zhang, L., Suganthan, P.N.: A comprehensive evaluation of random vector functional link networks. Inf. Sci. 367, 1094–1105 (2016)

    Article  Google Scholar 

  16. Zhang, P.B., Yang, Z.X.: A new learning paradigm for random vector functional-link network: RVFL+. arXiv preprint arXiv:1708.08282 (2017)

  17. Qiu, X., Ren, Y., Suganthan, P.N., Amaratunga, G.A.: Empirical mode decomposition based ensemble deep learning for load demand time series forecasting. Appl. Soft Comput. 54, 246–255 (2017)

    Article  Google Scholar 

  18. Ouyang, T., He, Y., Li, H., Sun, Z., Baek, S.: Modeling and forecasting short-term power load with copula model and deep belief network. IEEE Trans. Emerg. Topics Comput. Intell. 3(2), 127–136 (2019)

    Article  Google Scholar 

  19. Rafiei, M., Niknam, T., Aghaei, J., Shafie-Khah, M., Catalao, J.P.: Probabilistic load forecasting using an improved wavelet neural network trained by generalized extreme learning machine. IEEE Trans. Smart Grid 9(6), 6961–6971 (2018)

    Article  Google Scholar 

  20. Deng, Z., Wang, B., Xu, Y., Xu, T., Liu, C., Zhu, Z.: Multi-scale convolutional neural network with time-cognition for multi-step short-term load forecasting. IEEE Access 7, 88058–88071 (2019)

    Article  Google Scholar 

  21. Li, Y., Wu, H., Liu, H.: Multi-step wind speed forecasting using EWT decomposition, LSTM principal computing, RELM subordinate computing and IEWT reconstruction. Energy Convers. Manag. 167, 203–219 (2018)

    Article  Google Scholar 

  22. Tong, C., Li, J., Lang, C., Kong, F., Niu, J., Rodrigues, J.J.: An efficient deep model for day-ahead electricity load forecasting with stacked denoising auto-encoders. J. Parallel Distrib. Comput. 117, 267–273 (2018)

    Article  Google Scholar 

  23. Moon, J., Kim, K.H., Kim, Y., Hwang, E.: A short-term electric load forecasting scheme using 2-stage predictive analytics. In: 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 219–226. IEEE, January 2018

    Google Scholar 

  24. Chen, Y., Xu, P., Chu, Y., Li, W., Wu, Y., Ni, L., Bao, Y., Wang, K.: Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings. Appl. Energy 195, 659–670 (2017)

    Article  Google Scholar 

  25. Song, J., Wang, J., Lu, H.: A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting. Appl. Energy 215, 643–658 (2018)

    Article  Google Scholar 

  26. Samuel, O., Alzahrani, F.A., Hussen Khan, R.J.U., Farooq, H., Shafiq, M., Afzal, M.K., Javaid, N.: Towards modified entropy mutual information feature selection to forecast medium-term load using a deep learning model in smart homes. Entropy 22(1), 68 (2020)

    Article  MathSciNet  Google Scholar 

  27. Khalid, R., Javaid, N., Al-zahrani, F.A., Aurangzeb, K., Qazi, E.U.H., Ashfaq, T.: Electricity load and price forecasting using Jaya-Long Short Term Memory (JLSTM) in smart grids. Entropy 22(1), 10 (2020)

    Article  Google Scholar 

  28. Mujeeb, S., Javaid, N.: ESAENARX and DE-RELM: novel schemes for big data predictive analytics of electricity load and price. Sustain. Cities Soc. 51, 101642 (2019)

    Article  Google Scholar 

  29. Mujeeb, S., Alghamdi, T.A., Ullah, S., Fatima, A., Javaid, N., Saba, T.: Exploiting deep learning for wind power forecasting based on big data analytics. Appl. Sci. 9(20), 1–19 (2019). https://doi.org/10.3390/app9204417. ISSN: 2076-3417. Article Number: 4417

    Article  Google Scholar 

  30. Naz, A., Javaid, N., Rasheed, M.B., Haseeb, A., Alhussein, M., Aurangzeb, K.: Game theoretical energy management with storage capacity optimization and photo-voltaic cell generated power forecasting in micro grid. Sustainability 11(10), 1–22 (2019). https://doi.org/10.3390/su11102763. ISSN: 2071-1050. Article Number: 2763

    Article  Google Scholar 

  31. Naz, A., Javed, M.U., Javaid, N., Saba, T., Alhussein, M., Aurangzeb, K.: Short-term electric load and price forecasting using enhanced extreme learning machine optimization in smart grids. Energies 12(5), 1–30 (2019). https://doi.org/10.3390/en12050866. EISSN 1996-1073. Article Number: 866

    Article  Google Scholar 

  32. Mujeeb, S., Javaid, N., Ilahi, M., Wadud, Z., Ishmanov, F., Afzal, M.K.: Deep long short-term memory: a new price and load forecasting scheme for big data in smart cities. Sustainability 11(4), 1–29 (2019). https://doi.org/10.3390/su11040987. ISSN: 2071-1050. Article Number: 987

    Article  Google Scholar 

  33. Australian energy market operator (2016). http://www.aemo.com.au/

  34. Zixuan, Z.: (2019). https://towardsdatascience.com/understand-data-normalization-in-machine-learning-8ff3062101f0

Download references

Author information

Authors and Affiliations

  1. COMSATS University Islamabad, Islamabad, Pakistan

    Afrah Naeem, Hira Gul, Arooj Arif, Sahiba Fareed, Mubbashra Anwar & Nadeem Javaid

Authors
  1. Afrah Naeem
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hira Gul
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arooj Arif
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sahiba Fareed
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mubbashra Anwar
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nadeem Javaid
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nadeem Javaid .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Electrical Engineering and Information Technology, University of Naples “Frederico II”, Naples, Italy

    Flora Amato

  3. Department of Political Science, University of Campania Luigi Vanvitelli, Caserta, Italy

    Francesco Moscato

  4. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

  5. Department of Advanced Sciences, Hosei University, Tokyo, Japan

    Makoto Takizawa

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Naeem, A., Gul, H., Arif, A., Fareed, S., Anwar, M., Javaid, N. (2020). Short-Term Load Forecasting Using EEMD-DAE with Enhanced CNN in Smart Grid. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Web, Artificial Intelligence and Network Applications. WAINA 2020. Advances in Intelligent Systems and Computing, vol 1150. Springer, Cham. https://doi.org/10.1007/978-3-030-44038-1_107

Download citation

Publish with us

Policies and ethics

Search

Navigation