Two combined forecasting models based on singular spectrum analysis and intelligent optimized algorithm for short-term wind speed

2016 ◽  
Vol 30 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Ping Jiang ◽  
Ranran Li ◽  
Kequan Zhang
Keyword(s):  
Wind Speed ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Short Term ◽  
Forecasting Models ◽  
Singular Spectrum ◽  
Combined Forecasting

scholarly journals A Hybrid Short-Term Power Load Forecasting Model Based on the Singular Spectrum Analysis and Autoregressive Model

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Hongze Li ◽  
Liuyang Cui ◽  
Sen Guo
Keyword(s):  
Spectrum Analysis ◽  
Electricity Market ◽  
Autoregressive Model ◽  
Singular Spectrum Analysis ◽  
Load Forecasting ◽  
Ar Model ◽  
Short Term ◽  
Singular Spectrum ◽  
Power Load ◽  
Power Load Forecasting

Short-term power load forecasting is one of the most important issues in the economic and reliable operation of electricity power system. Taking the characteristics of randomness, tendency, and periodicity of short-term power load into account, a new method (SSA-AR model) which combines the univariate singular spectrum analysis and autoregressive model is proposed. Firstly, the singular spectrum analysis (SSA) is employed to decompose and reconstruct the original power load series. Secondly, the autoregressive (AR) model is used to forecast based on the reconstructed power load series. The employed data is the hourly power load series of the Mid-Atlantic region in PJM electricity market. Empirical analysis result shows that, compared with the single autoregressive model (AR), SSA-based linear recurrent method (SSA-LRF), and BPNN (backpropagation neural network) model, the proposed SSA-AR method has a better performance in terms of short-term power load forecasting.

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