Stream-Based Short-Term Demand Forecasting Model Using ARIMA

Tao Liu; Rui Min Chen; Yong Xiao; Jin Feng Yang

doi:10.4028/www.scientific.net/AMM.220-223.315

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 220-223Stream-Based Short-Term Demand Forecasting Model...

Stream-Based Short-Term Demand Forecasting Model Using ARIMA

Abstract:

Short-term load forecasting for streaming load data is an important issue for power system planning, operation and control. Smart meters of Advanced Meter Infrastructure distributed around the distribution power grid produce streams of load at high-speed. The collected data can be characterized as a non-stationary continuous flow. A stream-based short-term demand forecasting model based on ARIMA is proposed. This method is used to forecast hourly electricity demand for next few days ahead. The performance of this methodology is validated with streaming data collected in real-time from the power grid.

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Periodical:

Applied Mechanics and Materials (Volumes 220-223)

Pages:

315-318

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.220-223.315

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Online since:

November 2012

Authors:

Tao Liu, Rui Min Chen, Yong Xiao, Jin Feng Yang

Keywords:

Demand Side Management, Stream Forecast, Time Series

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References

[1] Chen J.F, Wang, W.M, Huang C.M, Analysis of an adaptive time-series autoregressive moving-average (ARMA) model for short-term load forecasting, Electric Power Systems Research, 34 (1995) 187-196.

DOI: 10.1016/0378-7796(95)00977-1

Google Scholar

[2] Kiartzis S.J, Bakirtzis, A.G. & Petridis, V, Short-term loading forecasting using neural networks, Electric Power Systems Research, 33 (1994) 1-6.

DOI: 10.1016/0378-7796(95)00920-d

Google Scholar

[3] Tamimi M, Egbert R, Short term electric load forecasting via fuzzy neural collaboration, Electric Power Systems Research, 56 (2000) 243-248.

DOI: 10.1016/s0378-7796(00)00123-1

Google Scholar

[4] Topalli A.K, Erkmen I, A hybrid learning for neural networks applied to short term load forecasting, Neurocomputing, 51 (2000) 495-500.

DOI: 10.1016/s0925-2312(02)00870-6

Google Scholar

[5] Taylor J.W, Menezes L.M, McSharry P.E, A comparison of univariate methods for forecasting electricity demand up to a day ahead, International Journal of Forecasting, 22 (2006) 1-16.

DOI: 10.1016/j.ijforecast.2005.06.006

Google Scholar