Title :
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
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;
Conference_Titel :
Cybernetics and Intelligent Systems (CIS), 2010 IEEE Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-6499-9
DOI :
10.1109/ICCIS.2010.5518553
