Browse > Article
http://dx.doi.org/10.5351/KJAS.2020.36.6.791

Time series clustering for AMI data in household smart grid  

Lee, Jin-Young (Department of Applied Statistics, Chung-Ang University)
Kim, Sahm (Department of Applied Statistics, Chung-Ang University)
Publication Information
The Korean Journal of Applied Statistics / v.33, no.6, 2020 , pp. 791-804 More about this Journal
Abstract
Residential electricity consumption can be predicted more accurately by utilizing the realtime household electricity consumption reference that can be collected by the AMI as the ICT developed under the smart grid circumstance. This paper studied the model that predicts residential power load using the ARIMA, TBATS, NNAR model based on the data of hour unit amount of household electricity consumption, and unlike forecasting the consumption of the whole households at once, it computed the anticipated amount of the electricity consumption by aggregating the predictive value of each established model of cluster that was collected by the households which show the similiar load profile. Especially, as the typical time series data, the electricity consumption data chose the clustering analysis method that is appropriate to the time series data. Therefore, Dynamic Time Warping and Periodogram based method is used in this paper. By the result, forecasting the residential elecrtricity consumption by clustering the similiar household showed better performance than forecasting at once and in summertime, NNAR model performed best, and in wintertime, it was TBATS model. Lastly, clustering method showed most improvements in forecasting capability when the DTW method that was manifested the difference between the patterns of each cluster was used.
Keywords
smart grid; AMI data; household electric usage forecasting; time series clustering;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Berndt, D. J. and Clifford, J. (1994). Using dynamic time warping to find patterns in time series, In KDD workshop, 10, 359-370.
2 Box, G. E., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.), John Wiley & Sons, New York.
3 Caiado, J., Crato, N., and Pena, D. (2006). A periodogram-based metric for time series classification, Computational Statistics & Data Analysis, 50, 2668-2684.   DOI
4 Casado, D. (2010). Classification techniques for time series and functional data, Doctoral dissertation, Universidad Carlos III de Madrid
5 De Livera, A. M., Hyndman, R. J., and Snyder, R. D. (2011). Forecasting time series with complex seasonal patterns using exponential smoothing, Journal of the American Statistical Association, 106, 1513-1527.   DOI
6 Gajowniczek, K. and Zabkowski, T. (2014). Short term electricity forecasting using individual smart meter data, Procedia Computer Science, 35, 589-597.   DOI
7 Hyndman, R., Athanasopoulos, G., Bergmeir, C., et al. (2020). 'forecast: Forecasting functions for time series and linear models'. R package version 8.12.
8 Hyndman R. J. and Khandakar, Y. (2008). Automatic time series forecasting: the forecast package for R, 'Journal of Statistical Software', 26, 1-22.
9 Kong, W., Dong, Z. Y., Hill, D. J., Luo, F., and Xu, Y. (2017). Short-term residential load forecasting based on resident behaviour learning, IEEE Transactions on Power Systems, 33, 1087-1088.   DOI
10 Kruskal, J. B. (1983). An overview of sequence comparison: Time warps, string edits, and macromolecules, SIAM review, 25, 201-237.   DOI
11 Sohn, H., Jung, S., and Kim, S. (2016). A study on electricity demand forecasting based on time series clustering in smart grid, The Korean Journal of Applied Statistics, 29, 190-203.
12 Lee, J. S., Sohn, H. G., and Kim, S. (2013). Daily peak load forecasting for electricity demand by time series models, The Korean Journal of Applied Statistics, 26, 349-360.   DOI
13 McLoughlin, F., Duffy, A., and Conlon, M. (2015). A clustering approach to domestic electricity load profile characterisation using smart metering data, Applied energy, 141, 190-199.   DOI
14 Montero, P. and Vilar, J. A. (2014). TSclust: An R Package for Time Series Clustering, Journal of Statistical Software, 62, 1-43.
15 Quilumba, F. L., Lee, W. J., Huang, H., Wang, D. Y., and Szabados, R. L. (2014). Using smart meter data to improve the accuracy of intraday load forecasting considering customer behavior similarities, IEEE Transactions on Smart Grid, 6, 911-918.   DOI
16 Shahzadeh, A., Khosravi, A., and Nahavandi, S. (2015). Improving load forecast accuracy by clustering consumers using smart meter data, In 2015 international joint conference on neural networks, 1-7.
17 Son, H. G., Kim, Y., and Kim, S. (2020). Time Series Clustering of Electricity Demand for Industrial Areas on Smart Grid, Energies, 13, 2377.   DOI
18 Tureczek, A., Nielsen, P. S., and Madsen, H. (2018). Electricity consumption clustering using smart meter data, Energies, 11, 859.   DOI
19 Wijaya, T. K., Vasirani, M., Humeau, S., and Aberer, K. (2015). Cluster-based aggregate forecasting for residential electricity demand using smart meter data, In 2015 IEEE international conference on Big data, 879-887.