Time Series Clustering of Electricity Demand for Industrial Areas on Smart Grid

Heung-gu Son, Yunsun Kim and Sahm Kim
Additional contact information
Heung-gu Son: Department of Short-term Demand Forecasting, Korea Power Exchange, Naju 58322, Korea
Yunsun Kim: Department of Applied Statistics, Chung-ang University, Seoul 06974, Korea
Sahm Kim: Department of Applied Statistics, Chung-ang University, Seoul 06974, Korea

Energies, 2020, vol. 13, issue 9, 1-14

Abstract: This study forecasts electricity demand in a smart grid environment. We present a prediction method that uses a combination of forecasting values based on time-series clustering. The clustering of normalized periodogram-based distances and autocorrelation-based distances are proposed as the time-series clustering methods. Trigonometrical transformation, Box–Cox transformation, autoregressive moving average (ARMA) errors, trend and seasonal components (TBATS), double seasonal Holt–Winters (DSHW), fractional autoregressive integrated moving average (FARIMA), ARIMA with regression (Reg-ARIMA), and neural network nonlinear autoregressive (NN-AR) are used for demand forecasting based on clustering. The results show that the time-series clustering method performs better than the method using the total amount of electricity demand in terms of the mean absolute percentage error (MAPE).

Keywords: smart grid; DSHW; TBATS; NN-AR; time-series clustering (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/9/2377/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/9/2377/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:9:p:2377-:d:355978

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().