Comparing the Simple to Complex Automatic Methods with the Ensemble Approach in Forecasting Electrical Time Series Data

Winita Sulandari (), Yudho Yudhanto, Sri Subanti, Crisma Devika Setiawan, Riskhia Hapsari and Paulo Canas Rodrigues
Additional contact information
Winita Sulandari: Department of Statistics, Universitas Sebelas Maret, Surakarta 57126, Indonesia
Yudho Yudhanto: Informatics Engineering, Vocational School, Universitas Sebelas Maret, Surakarta 57129, Indonesia
Sri Subanti: Department of Statistics, Universitas Sebelas Maret, Surakarta 57126, Indonesia
Crisma Devika Setiawan: Department of Statistics, Universitas Sebelas Maret, Surakarta 57126, Indonesia
Riskhia Hapsari: Department of Statistics, Universitas Sebelas Maret, Surakarta 57126, Indonesia
Paulo Canas Rodrigues: Department of Statistics, Federal University of Bahia, Salvador 40170-110, Brazil

Energies, 2023, vol. 16, issue 22, 1-16

Abstract: The importance of forecasting in the energy sector as part of electrical power equipment maintenance encourages researchers to obtain accurate electrical forecasting models. This study investigates simple to complex automatic methods and proposes two weighted ensemble approaches. The automated methods are the autoregressive integrated moving average; the exponential smoothing error–trend–seasonal method; the double seasonal Holt–Winter method; the trigonometric Box–Cox transformation, autoregressive, error, trend, and seasonal model; Prophet and neural networks. All accommodate trend and seasonal patterns commonly found in monthly, daily, hourly, or half-hourly electricity data. In comparison, the proposed ensemble approaches combine linearly (EnL) or nonlinearly (EnNL) the forecasting values obtained from all the single automatic methods by considering each model component’s weight. In this work, four electrical time series with different characteristics are examined, to demonstrate the effectiveness and applicability of the proposed ensemble approach—the model performances are compared based on root mean square error (RMSE) and absolute percentage errors (MAPEs). The experimental results show that compared to the existing average weighted ensemble approach, the proposed nonlinear weighted ensemble approach successfully reduces the RMSE and MAPE of the testing data by between 28% and 82%.

Keywords: automatic methods; ensemble approach; electrical forecasting model; time series (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/22/7495/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/22/7495/ (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:16:y:2023:i:22:p:7495-:d:1276497

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 ().