Short-term load forecasting using time series analysis: A case study for Singapore

Author

Deng, Jianguang ; Jirutitijaroen, Panida

Author_Institution

Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore

fYear

2010

fDate

28-30 June 2010

Firstpage

231

Lastpage

236

Abstract

This paper presents time series analysis for short-term Singapore electricity demand forecasting. Two time series models are proposed, namely, the multiplicative decomposition model and the seasonal ARIMA Model. Forecasting errors of both models are computed and compared. Results show that both time series models can accurately predict the short-term Singapore demand and that the Multiplicative decomposition model slightly outperforms the seasonal ARIMA model.

Keywords

autoregressive moving average processes; demand forecasting; load forecasting; power system economics; time series; ARIMA Model; multiplicative decomposition model; short-term Singapore electricity demand forecasting; short-term load forecasting; time series analysis; Data analysis; Demand forecasting; Economic forecasting; Load forecasting; Maintenance; Power system reliability; Power system security; Predictive models; Time series analysis; Weather forecasting; Short-Term Load Forecasting; Singapore Data; Time Series Analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Cybernetics and Intelligent Systems (CIS), 2010 IEEE Conference on

Conference_Location

Singapore

Print_ISBN

978-1-4244-6499-9

Type

conf

DOI

10.1109/ICCIS.2010.5518553

Filename

5518553