Combination of Singular Spectrum Analysis and Autoregressive Model for Short Term Load Forecasting
作者:
A. Hossein Vahabie, M. Mahdi Rezaei Yousefi, Babak N. Araabi, Caro Lucas, and Saeedeh
所属专业方向:
Short Term Load Forecasting
摘要:
Abstract—One of the most important requirements for the
operation and planning activities of an electrical utility is the
prediction of load for the next hour to several days out, known as
Short Term Load Forecasting (STLF). This paper presents a new
method based on spectral analysis and Autoregressive (AR)
modeling that is capable to predict the electricity demand
accurately. An AR model is optimized for each of the principle
components obtained from Singular Spectrum Analysis (SSA),
and the multi step predicted values are recombined to make the
load time series. This method is used for the STLF of Iran
National Power System (INPS) and the performance of the
method shows promising results for one hour up to a day
prediction. The proposed method is comparable with intelligent
methods
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发表于 2011-8-2 02:11:34
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