Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

An Improved Spectral Clustering Algorithm Using Fast Dynamic Time Warping for Power Load Curve Analysis

  • Conference paper
  • First Online:
Mobile Computing, Applications, and Services (MobiCASE 2020)

Abstract

Cluster analysis of power loads can not only accurately extract the commonalities and characteristics of the loads, but also help to understand the users’ habits and patterns of electricity consumption, so as to optimize the power dispatching and regulate the operation of the entire power grid. Based on the traditional clustering methods, this paper proposes a clustering algorithm that can automatically determine the optimal cluster number. Firstly, Fast-DTW algorithm is used as the similarity measuring function to calculate the similar matrix between two time series, and then Spectral Clustering and Affinity Propagation (AP) algorithm are used for clustering. It is combined with Euclidean distance, DTW and Fast-DTW algorithms to evaluate the algorithm effect. By analyzing the actual power data, our results show that the improved external performance evaluation index ARI, AMI and internal performance evaluation index SSE are significantly improved and have better time series similarity and accuracy. Applying the algorithm to more than six thousands of users, twelve kinds of typical power load patterns can be obtained. For any other load curve, it can be mapped to a standard load by feature extraction. The corresponding prediction model is adopted, which is of great significance to reduce the peak power consumption, adjust the electricity price appropriately and solve the problem of system balance.

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

Access this chapter

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

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Similar content being viewed by others

References

  1. Panapakidis, I.P., Christoforidis, G.C.: Implementation of modified versions of the K-means algorithm in power load curves profiling. Sustain. Cities Soc. 35, 83–93 (2017)

    Article  Google Scholar 

  2. Gao, Z., Li, Z., Bao, S.: Short term prediction of photovoltaic power based on FCM and CG DBN combination. J. Electr. Eng. Technol. 15, 333–341 (2020)

    Article  Google Scholar 

  3. Fu, X., Zeng, X.J., Feng, P., Cai, X.: Clustering-based short-term load forecasting for residential electricity under the increasing-block pricing tariffs in China. Energy 165, 76–89 (2018)

    Article  Google Scholar 

  4. Khan, Z.A., Jayaweera, D., Alvarez-Alvarado, M.S.: A novel approach for load profiling in smart power grids using smart meter data. Electr. Power Syst. Res. 165, 191–198 (2018)

    Article  Google Scholar 

  5. Rajabi, A., Eskandari, M., Ghadi, M.J., Li, L., Zhang, J., Siano, P.: A comparative study of clustering techniques for electrical load pattern segmentation. Renew. Sustain. Energy Rev. 120, 109628 (2019)

    Article  Google Scholar 

  6. Charwand, M., Gitizadeh, M., Siano, P., Chicco, G., Moshavash, Z.: Clustering of electrical load patterns and time periods using uncertainty-based multi-level amplitude thresholding. Int. J. Electr. Power Energy Syst. 117, 105624 (2020)

    Article  Google Scholar 

  7. Motlagh, O., Berry, A., O’Neil, L.: Clustering of residential electricity customers using load time series. Energy 237, 11–24 (2019)

    Google Scholar 

  8. Janani, R., Vijayarani, S.: Text document clustering using spectral clustering algorithm with particle swarm optimization. Expert Syst. Appl. 134, 192–200 (2019)

    Article  Google Scholar 

  9. Zhao, Y., Yuan, Y., Nie, F., Wang, Q.: Spectral clustering based on iterative optimization for large-scale and high-dimensional data. Neurocomputing 318, 227–235 (2018)

    Article  Google Scholar 

  10. Wan, Y., Chen, X.-L., Shi, Y.: Adaptive cost dynamic time warping distance in time series analysis for classification. J. Comput. Appl. Math. 319, 514–520 (2017)

    Article  MathSciNet  Google Scholar 

  11. Han, T., Peng, Q., Zhu, Z., Shen, Y., Huang, H., Abid, N.N.: A pattern representation of stock time series based on DTW. Phys. A Stat. Mech. Appl. 550, 124161 (2020)

    Article  Google Scholar 

  12. Kang, Z., et al.: Multi-graph fusion for multi-view spectral clustering. Knowl.-Based Syst. 189, 105102 (2019)

    Article  Google Scholar 

  13. Salvadora, S., Chan, P.: Toward accurate dynamic time warping in linear time and space. Intell. Data Anal. 11, 561–580 (2007)

    Article  Google Scholar 

  14. Cao, Y., Rakhilin, N., Gordon, P.H., Shen, X., Kan, E.C.: A real-time spike classification method based on dynamic time warping for extracellular enteric neural recording with large waveform variability. J. Neurosci. Methods 261, 97–109 (2016)

    Article  Google Scholar 

  15. Han, Y., Wu, H., Jia, M., Geng, Z., Zhong, Y.: Production capacity analysis and energy optimization of complex petrochemical industries using novel extreme learning machine integrating affinity propagation. Energy Convers. Manag. 180, 240–249 (2019)

    Article  Google Scholar 

  16. Alcock, R.: Synthetic control chart time series data set (1999). http://archive.ics.uci.edu/ml/machine-learning-databases/synthetic_control-mld/. Accessed via UCI

  17. CER smart metering project-electricity customer behaviour trial (2017). http://www.ucd.ie/issda/data/commissionforenergyregulationcer/. Accessed via the Irish Social Science Data Archive

  18. Xie, J., Gao, H., Xie, W.: Robust clustering by detecting density peaks and assigning points based on fuzzy weighted K-nearest neighbors. Inf. Sci. 354, 19–40 (2016)

    Article  Google Scholar 

  19. Xie, J., Zhou, Y., Ding, L.: Local standard deviation spectral clustering. In: IEEE International Conference on Big Data and Smart Computing, vol. 143, pp. 242–250 (2018)

    Google Scholar 

  20. Martiniano, A., Ferreira, R.P., Sassi, R.J.: Absenteeism at work Data Set (2010). http://archive.ics.uci.edu/ml/datasets/Absenteeismatwork/. Accessed via UCI

Download references

Acknowledgment

This work is supported by the National Nature Science Foundation of China (No. 61972357, No. 61672337).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yongbin Li .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bi, Z., Leng, Y., Liu, Z., Li, Y., Fuentes, S. (2020). An Improved Spectral Clustering Algorithm Using Fast Dynamic Time Warping for Power Load Curve Analysis. In: Liu, J., Gao, H., Yin, Y., Bi, Z. (eds) Mobile Computing, Applications, and Services. MobiCASE 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 341. Springer, Cham. https://doi.org/10.1007/978-3-030-64214-3_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-64214-3_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64213-6

  • Online ISBN: 978-3-030-64214-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics