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

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2 Author(s)
Jianguang Deng ; Department of Electrical & Computer Engineering, National University of Singapore, Singapore ; Panida Jirutitijaroen

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.

Published in:

2010 IEEE Conference on Cybernetics and Intelligent Systems

Date of Conference:

28-30 June 2010