Skip to main content
SpringerLink
Log in

A Dilated Convolutional Based Model for Time Series Forecasting

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Smart grids infrastructure is rapidly adopting the recent technology to optimize the power generation and energy saving. The load forecasting in smart grids has been one such technology integration and accurate load forecasting models has been a challenge. With the advent of advanced infrastructure, huge data is being generated at different time frequencies, that can be used to build accurate load forecasting models. Focusing on the state-of-the-art machine learning techniques, in this work, we propose a load forecasting model of stacked dilated convolutional layers. The dilations efficiently captures the local trend and seasonality from the time series for future predictions. Proposed model has been trained on multiple time series data with varying frequencies. Results show that the proposed model is an improvement to the existing state-of-the-art.

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

Access this article

Log in via an institution

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
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Bilton M, Carmichael R, Whitney A, Dragovic J, Schofield J, Woolf M, Strbac G. Accessiblity and validity of smart meter data”, Report C5 for the “Low Carbon London. LCNF project: Imperial College London; 2014.

    Google Scholar 

  2. Borovykh A, Bohte S, Oosterlee CW. Conditional time series forecasting with convolutional neural networks. arXiv preprint. 2017;arXiv:170304691.

  3. Cai M, Pipattanasomporn M, Rahman S. Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques. Appl Energy. 2019;236:1078–88.

    Article  Google Scholar 

  4. Commission Energy Regulation. Electricity smart metering technology trials findings report. https://www.ucd.ie/t4cms/Electricity%20Smart%20Metering%20Technology%20Trials%20Findings%20Report.pdf. Accessed 16 May 2011.

  5. De Livera AM, Hyndman RJ, Snyder RD. Forecasting time series with complex seasonal patterns using exponential smoothing. J Am Stat Assoc. 2011;106(496):1513–27.

    Article  MathSciNet  Google Scholar 

  6. Deng Y, Ren Z, Kong Y, Bao F, Dai Q. A hierarchical fused fuzzy deep neural network for data classification. IEEE Trans Fuzzy Syst. 2017;25(4):1006–12.

    Article  Google Scholar 

  7. Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, 2010. pp. 249–256.

  8. Granell R, Axon CJ, Wallom DC. Impacts of raw data temporal resolution using selected clustering methods on residential electricity load profiles. IEEE Trans Power Syst. 2015;30(6):3217–24.

    Article  Google Scholar 

  9. He K, Zhang X, Ren S, Sun J. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: 2015 IEEE international conference on computer vision (ICCV), 2015. pp. 1026–1034.

  10. Hong Chen, Canizares CA, Singh A. Ann-based short-term load forecasting in electricity markets. In: 2001 IEEE power engineering society winter meeting. Conference proceedings (Cat. No. 01CH37194), 2001. vol. 2, pp. 411–415.

  11. Huang S. Short-term load forecasting using threshold autoregressive models. IEE Proc Gener Transm Distrib. 1997;144(5):477–81.

    Article  Google Scholar 

  12. Kingma DP, Ba J. Adam: a method for stochastic optimization. arXiv preprint. 2014;arXiv:14126980.

  13. Klambauer G, Unterthiner T, Mayr A, Hochreiter S. Self-normalizing neural networks. In: Advances in neural information processing systems, 2017. pp. 971–980.

  14. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.

    Article  Google Scholar 

  15. Lee CM, Ko CN. Short-term load forecasting using lifting scheme and arima models. Expert Syst Appl. 2011;38(5):5902–11.

    Article  Google Scholar 

  16. Lusis P, Khalilpour KR, Andrew L, Liebman A. Short-term residential load forecasting: impact of calendar effects and forecast granularity. Appl Energy. 2017;205:654–69.

    Article  Google Scholar 

  17. Mbamalu G, El-Hawary M. Load forecasting via suboptimal seasonal autoregressive models and iteratively reweighted least squares estimation. IEEE Trans Power Syst. 1993;8(1):343–8.

    Article  Google Scholar 

  18. Mishra K, Basu S, Maulik U. Danse: a dilated causal convolutional network based model for load forecasting. In: International conference on pattern recognition and machine intelligence, Springer; 2019. pp. 234–241.

  19. Papadopoulos K. Seriesnet: a dilated causal convolutional neural network for forecasting. https://github.com/kristpapadopoulos/seriesnet (2018).

  20. Quilumba FL, Lee WJ, Huang H, Wang DY, Szabados RL. Using smart meter data to improve the accuracy of intraday load forecasting considering customer behavior similarities. IEEE Trans Smart Grid. 2015;6(2):911–8.

    Article  Google Scholar 

  21. Raza MQ, Khosravi A. A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renew Sustain Energy Rev. 2015;50:1352–72.

    Article  Google Scholar 

  22. Rodrigues F, Cardeira C, Calado JMF. The daily and hourly energy consumption and load forecasting using artificial neural network method: a case study using a set of 93 households in portugal. Energy Proc. 2014;62:220–9.

    Article  Google Scholar 

  23. Shi H, Xu M, Li R. Deep learning for household load forecasting-a novel pooling deep RNN. IEEE Trans Smart Grid. 2018;9(5):5271–80.

    Article  Google Scholar 

  24. Taylor JW, De Menezes LM, McSharry PE. A comparison of univariate methods for forecasting electricity demand up to a day ahead. Int J Forecast. 2006;22(1):1–16.

    Article  Google Scholar 

  25. Taylor JW, McSharry PE, et al. Short-term load forecasting methods: an evaluation based on European data. IEEE Trans Power Syst. 2007;22(4):2213–9.

    Article  Google Scholar 

  26. Tian C, Ma J, Zhang C, Zhan P. A deep neural network model for short-term load forecast based on long short-term memory network and convolutional neural network. Energies. 2018;11(12):3493.

    Article  Google Scholar 

  27. Voss M, Bender-Saebelkampf C, Albayrak S. Residential short-term load forecasting using convolutional neural networks. In: 2018 IEEE international conference on communications, control, and computing technologies for smart grids (SmartGridComm), IEEE, 2018. pp. 1–6.

  28. Wang L, Mao S, Wilamowski B. Short-term load forecasting with LSTM based ensemble learning. In: 2019 International conference on internet of things (iThings) and IEEE green computing and communications (GreenCom) and IEEE cyber. IEEE: physical and social computing (CPSCom) and IEEE smart data (SmartData); 2019. pp. 793–800.

  29. Wang Y, Chen Q, Kang C, Xia Q. Clustering of electricity consumption behavior dynamics toward big data applications. IEEE Trans Smart Grid. 2016;7(5):2437–47.

    Article  Google Scholar 

  30. Wang Z, Zhao B, Guo H, Tang L, Peng Y. Deep ensemble learning model for short-term load forecasting within active learning framework. Energies. 2019;12(20):3809.

    Article  Google Scholar 

  31. Wenpeng L. Advanced metering infrastructure. South Power Syst Technol. 2009;3(2):6–10.

    Google Scholar 

  32. Yiyan L, Dong H, Zheng Y. Long-term system load forecasting based on data-driven linear clustering method. J Mod Power Syst Clean Energy. 2018;6(2):306–16.

    Article  Google Scholar 

  33. Yu F, Koltun V. Multi-scale context aggregation by dilated convolutions. arXiv preprint. 2015;arXiv:151107122.

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India

    Kakuli Mishra & Ujjwal Maulik

  2. Department of Engineering and Technological Studies, Kalyani University, Kalyani, 741235, India

    Srinka Basu

Authors
  1. Kakuli Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Srinka Basu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ujjwal Maulik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kakuli Mishra.

Ethics declarations

Conflict of Interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

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

This article is part of the topical collection “Computational Biology and Biomedical Informatics” guest edited by Dhruba Kr Bhattacharyya, Sushmita Mitra and Jugal Kr Kalita.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mishra, K., Basu, S. & Maulik, U. A Dilated Convolutional Based Model for Time Series Forecasting. SN COMPUT. SCI. 2, 90 (2021). https://doi.org/10.1007/s42979-021-00464-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-021-00464-4

Keywords

Search

Navigation